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Hugues Van Assel; รcole Normale Supรฉrieure de Lyon.
Thursday 13 June 2024, 11:00-12:00
Joint Machine Learning Seminars
The Machine Learning group at CUED and the Inference Group at the Cavendish Laboratory hold joint weekly group meetings, usually on Wednesdays at 2pm (but sometimes at other times), and alternating venues between Engineering and the Physics department. (This list is the union of the inference group list and the ML@CUED list.)
Contact: Carl Edward Rasmussen ; Zoubin Ghahramani ; Ryan Prescott Adams ; Oliver Stegle
0 upcoming talks View 688 archived talks
Machine Learning @ CUED
Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis
Machine Learning @ CUED
Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis
Machine Learning @ CUED
Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis
Machine Learning @ CUED
Consistent Validation for Predictive Methods in Spatial Settings
Machine Learning @ CUED
Modern Bayesian Experimental Design
Machine Learning @ CUED
Visit and talk by Jay McClelland: "Some thoughts on the differences between human and machine intelligence"
Machine Learning @ CUED
Compositional mathematics and automatic gradient descent
Machine Learning @ CUED
Convergence bounds for the Random Walk Metropolis algorithm - Perspectives from Isoperimetry
Machine Learning @ CUED
Calls to F the Algorithm: Lessons from the 2020 Exam Debacle
Machine Learning @ CUED
Scalable simulation and inference in non-Gaussian stochastic PDEs
Machine Learning @ CUED
Measuring Alignment Between Perceptual Systems: An Analysis Through The Lens of Shared Invariances
Machine Learning @ CUED
Gaussian processes, spectral analysis kernels and optimal transport
Machine Learning @ CUED
Deep Reinforcement Learning for Multi-Agent Interaction
Machine Learning @ CUED
Presenting Hawk-Eyeโs Skeletrack: Our machine learning approach to building a real time skeletal tracking system for sports, and how we're using it to shape the future of fan engagement
Machine Learning @ CUED
Noise-Aware Differentially Private Synthetic Data
Machine Learning @ CUED
Estimating RSV seasonality from pandemic disruptions: a modelling study
Machine Learning @ CUED
Beyond Conformal Prediction: Distribution-Free Uncertainty Quantification for Complex Machine Learning Tasks
Machine Learning @ CUED
Graph neural network approach for decentralized multi-robot coordination
Machine Learning @ CUED
Efficient and Structured Uncertainty: Challenges and Opportunities
Machine Learning @ CUED
Game Playing Meets Game Theory: Strategic Learning from Simulated Play
Machine Learning @ CUED
Robust Deep Learning Under Distribution Shift
Machine Learning @ CUED
Models and inference for temporal Gaussian processes
Machine Learning @ CUED
Reinforcement Learning at Huawei: Robustness, Safety, and Efficiency
Machine Learning @ CUED
Constructing temporal latent spaces: Representation learning for clustering and imputation on time series
Machine Learning @ CUED
Explaining Neural Networks: Post-hoc and Natural Language Explanations
Machine Learning @ CUED
Learning-Algorithms from Bayesian Principles
Machine Learning @ CUED
Rotation Invariant Householder Parameterization for Bayesian PCA
Machine Learning @ CUED
Two Approximate Sampling Methods for Bayesian Deep Learning
Machine Learning @ CUED
Semi-Unsupervised Learning with Deep Generative Models / Disentangling Improves VAEs' Robustness to Adversarial Attacks
Machine Learning @ CUED
Efficiency and Transferability of Neural Networks
Machine Learning @ CUED
Learning via Data Compression: Bayesian Coresets and Sparse Variational Inference
Machine Learning @ CUED
Deep Neural Networks: A Nonparametric Bayesian Approach with Local Competition
Machine Learning @ CUED
Information Consumption on Social Media: Efficiency, Trust, and Divisiveness
Machine Learning @ CUED
Why Do We Need New Hardware & Software for Machine Intelligence?
Machine Learning @ CUED
Nonparametric Generative Modeling via Optimal Transport and Diffusions with Provable Guarantees
Machine Learning @ CUED
NeVAE: A Deep Generative Model for Molecular Graphs
Machine Learning @ CUED
Global model explainability via aggregation
Machine Learning @ CUED
Robust machine learning for causal inference in health care
Computational Neuroscience
The K-FAC method for neural network optimization
Machine Learning Reading Group @ CUED
Generative models for few-shot prediction tasks
Machine Learning @ CUED
Online Meta-Learning
Computational Neuroscience
High-dimensional dynamics of generalization error in neural networks: implications for experience replay
Machine Learning @ CUED
Gauge Equivariant Convolutional Networks on Manifolds
Machine Learning @ CUED
Observation and Intervention Incentives in Causal Influence Diagrams: Towards an Understanding of Powerful Machine Learning Systems
Machine Learning @ CUED
Fairness for Sequential Decision Making Algorithms
Machine Learning @ CUED
Gaussian processes for inferring latent functions in complex data models
Machine Learning @ CUED
Causal Inference for Treatment Effects: A Theory and Associated Learning Algorithms
Machine Learning @ CUED
Interpretability - the myth, questions, and some answers
Machine Learning @ CUED
Constraint-based causal Discovery from NOnstationary/heterogeneous Data (CD-NOD)
Machine Learning @ CUED
Fast yet Simple Natural-Gradient Variational Inference in Complex Models
Machine Learning @ CUED
Variance in Policy Gradient methods and Learning Sequential Latent Variable Models
Machine Learning @ CUED
Structured deep models: Deep learning on graphs and beyond
Machine Learning @ CUED
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Machine Learning @ CUED
Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features
Machine Learning @ CUED
Algorithmic Glass Ceiling in Social Networks
Machine Learning @ CUED
A Bayesian Perspective on Generalization and SGD
Machine Learning @ CUED
Algorithmic Glass Ceiling in Social Networks
Machine Learning @ CUED
Structure in tensor-variate data: a trivial byproduct of simpler phenomena?
Machine Learning @ CUED
Lipschitz Global Optimization
Machine Learning @ CUED
Variational inference for some models with Polya-Gamma latent variables and Gaussian process priors
Machine Learning @ CUED
Bayesian Generative Adversarial Networks
Machine Learning @ CUED
AI for Inclusive Finance
Machine Learning @ CUED
Backprop through the Void: Optimizing Control Variates for Black-Box Gradient Estimation.
Machine Learning @ CUED
Learning to Learn without Gradient Descent by Gradient Descent
Machine Learning @ CUED
Towards true end-to-end learning & optimization
Machine Learning @ CUED
Targeted Disclosure to Support Auditing and Accountability for Automated Decision-making
Machine Learning @ CUED
Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning
Machine Learning @ CUED
Transfer Learning for NLP
Machine Learning @ CUED
A Bayesian Treatment for Uncertainty -- and its application in health care
Machine Learning @ CUED
Unbiased Estimation of the Eigenvalues of Large Implicit Matrices
Machine Learning @ CUED
The Grammar Variational Autoencoder & Counterfactual Fairness
Machine Learning @ CUED
Accelerating computation of SVM and DNN by binary approximation
Machine Learning @ CUED
Towards User-Friendly Image Inpainting: Learning-to-Rank based Image Quality Assessment for Image Inpainting
Machine Learning @ CUED
Deep learning for autonomous driving
Machine Learning @ CUED
Understanding Black-box Predictions via Influence Functions
Machine Learning @ CUED
A simple neural network module for relational reasoning
Machine Learning @ CUED
Future technology: machine learning using memristors networks
Machine Learning @ CUED
Future technology: machine learning using memristors networks
Machine Learning @ CUED
On Different Distances Between Distributions and Generative Adversarial Networks
Machine Learning Study Group
Deep Learning Book - Meeting 9 - Representation Learning
Machine Learning Study Group
Deep Learning Book - Meeting 8 - Autoencoders
Machine Learning Study Group
Deep Learning Book - Meeting 7 - Neural Turing Machines & Conditional Random Fields as RNNs
Information Engineering Division seminar list
Bayesian Optimization for Probabilistic Programs
Machine Learning Study Group
Deep Learning Book - Meeting 6 - Recurrent Neural Networks
Machine Learning @ CUED
Differentially Private Bayesian Learning
Computational and Biological Learning Seminar Series
Control, inference and learning
Machine Learning @ CUED
Differential Privacy Tutorial
Machine Learning Study Group
Deep Learning Book - Meeting 5 - ResNets and DenseNets
Machine Learning @ CUED
Cooperative Inverse Reinforcement Learning
Machine Learning Study Group
Deep Learning Book - Meeting 4 - Convolutional Networks
Machine Learning Study Group
Deep Learning Book - Meeting 3 - Optimization
Machine Learning @ CUED
Bayesian optimisation in many dimensions with bespoke probabilistic programs
Machine Learning Study Group
Deep Learning Book - Meeting 2 - Regularization
Machine Learning Study Group
Deep Learning Book - Meeting 1 - Deep Feedforward NNs
Machine Learning @ CUED
Variational Bayes In Private Settings
Machine Learning @ CUED
Julia: Introduction and new developments
Machine Learning @ CUED
Bayesian optimality and frequentist extended admissibility are equivalent in saturated models
Machine Learning @ CUED
Hierarchical Multi-Agent Reinforcement Learning through Communicative Actions for Human-Robot Collaboration
Machine Learning @ CUED
Variational autoencoders with latent graphical models
Machine Learning @ CUED
Learning with limited supervision
Machine Learning @ CUED
A tale of P-matrices and TripleSpinners - the unreasonable effectiveness of structured models in nonlinear embeddings
Machine Learning @ CUED
Rejection Sampling Variational Inference
Machine Learning @ CUED
Probabilistic Numerical Computation: A New Concept?
Machine Learning @ CUED
A talk in two parts: (1) AI Neuroscience: How much do deep neural networks understand about the images they classify? (2) Robots that can adapt like animals.
โ ๏ธ Note venue: James Dyson Building
Machine Learning @ CUED
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
Machine Learning @ CUED
Robots learning on the move: deep learning from lots of demonstration
Machine Learning @ CUED
Probabilistic modeling for position and orientation estimation using inertial sensors
Machine Learning @ CUED
Moment matching for latent variable models: from ICA to LDA and CCA
Machine Learning @ CUED
Multiresolution Matrix Factorization
Machine Learning @ CUED
Dynamic Models for Health Data
Machine Learning @ CUED
Learning with Memory Embeddings
Machine Learning @ CUED
Data Driven Discrete Time Modeling of Continuous Time Nonlinear Systems: Problems, Challenges, Success Stories
Machine Learning @ CUED
Inference as Learning
Machine Learning @ CUED
Interpretable and interactive machine learning
Machine Learning @ CUED
Discriminative Embeddings of Latent Variable Models for Structured Data
Machine Learning @ CUED
Structured Dynamic Graphical Models & Scaling Multivariate Time Series Methodology
Machine Learning @ CUED
Scaling and Generalizing Approximate Bayesian Inference
arg58's list
A modular architecture for Unicode text compression
Machine Learning @ CUED
Modeling the Dynamics of Online Learning Activity
Machine Learning @ CUED
Developments in Exact Inference in Graphical Models
Machine Learning @ CUED
Approximation strategies for structure learning in Bayesian networks
โ ๏ธ Note: via Skype!
Machine Learning @ CUED
Automated Reasoning and AI for Large Formal Mathematics
Machine Learning @ CUED
Variational inference for scalable Gaussian process approximations
Machine Learning @ CUED
Novel MCMC and SMC schemes for Poisson-Kingman Bayesian Nonparametric mixture models
Machine Learning @ CUED
Turing: Rejuvenating Probabilistic Programming in Julia
Machine Learning @ CUED
Towards Weaker Supervision and Simpler Pipelines in Speech Recognition
Machine Learning @ CUED
Sum-Product Networks for Probabilistic Modeling
Machine Learning @ CUED
MLE-Struct: Bethe Learning of Graphical Models
Machine Learning @ CUED
Inference and Learning in the Anglican Probabilistic Programming System
Machine Learning @ CUED
Unsupervised Risk Estimation with only Structural Assumptions
Machine Learning @ CUED
The geometry of uncertainty
Machine Learning @ CUED
A more Automated Statistician
Machine Learning @ CUED
General Reinforcement Learning
Machine Learning @ CUED
Inference of a partially observed kinetic Ising model
Machine Learning @ CUED
Meta-Bayesian Analysis
Machine Learning @ CUED
Efficient Inference and Learning with Intractable Posteriors? Yes, Please.
Machine Learning @ CUED
Efficient multi-task Gaussian process models for genome-wide association studies
Machine Learning @ CUED
Harmonic Exponential Families and Group-Equivariant Convolution Networks
Machine Learning @ CUED
Higher Order Fused Regularization for Supervised Learning with Grouped Parameters
Machine Learning @ CUED
A-Star Sampling Review
Machine Learning @ CUED
Convex Factorization Machines
Machine Learning @ CUED
Belief and Truth in Hypothesised Behaviours
Machine Learning @ CUED
Training and Understanding Deep Neural Networks for Robotics, Design, and Perception
Machine Learning @ CUED
Information-Theoretic Bounded Rationality
Machine Learning @ CUED
The limits of MAP inference by MWSS on perfect graphs
Machine Learning @ CUED
Scalable Gaussian Processes for Scientific Discovery
Machine Learning @ CUED
MCMC for non-linear state space models using ensembles of latent sequences
โ ๏ธ (via Skype!)
Machine Learning @ CUED
Gradient-based hyperparameter optimization through reversible learning
Machine Learning @ CUED
Rich Component Analysis
Machine Learning @ CUED
A new MCMC hybrid scheme for Poisson-Kingman Bayesian Nonparametric mixture models
Machine Learning @ CUED
Modeling Confounding by Half-Sibling Regression
โ ๏ธ SHORT TALK (20 minutes)
Machine Learning @ CUED
Extreme Classification: A New Paradigm for Ranking & Recommendation
Machine Learning @ CUED
Convex and non-convex worlds in machine learning
Machine Learning @ CUED
Random Function Classes for Machine Learning
Machine Learning @ CUED
Explaining Non-Linear Classifier Decisions with application to Deep Learning
Machine Learning @ CUED
Deep Gaussian processes and variational propagation of uncertainty
Machine Learning @ CUED
Deep Learning
Machine Learning @ CUED
Random Function Classes for Machine Learning
Machine Learning @ CUED
Unsupervised Learning with Latent Variable Models
Machine Learning @ CUED
Unsupervised Learning with Latent Variable Models
Machine Learning @ CUED
Structural Markov laws / Geometry and HMC
Machine Learning @ CUED
Mean Field Approaches to Two Sequential Decision Making Problems.
โ ๏ธ This talk will be given via Skype
Machine Learning @ CUED
Do Deep Nets Really Need to be Deep?
Machine Learning @ CUED
Probabilistic numerics: treating numerical computation as learning, or; it's Bayes all the way down
Machine Learning @ CUED
Machine Learning for Quantitative Finance: A collaboration between the Cambridge Machine Learning Group and Cambridge Capital Management
Machine Learning @ CUED
Title to be confirmed
Machine Learning @ CUED
Inference for infinite mixture models and Gaussian Process mixtures of experts using simple approximate MAP Inference
Machine Learning @ CUED
Evolutionary dynamics in a continuous public goods game
Machine Learning @ CUED
An introduction to the Mondrian Process
Machine Learning @ CUED
Population Inference for Functional Brain Connectivity
Machine Learning @ CUED
Probabilistic approaches to understanding bird conversations
Machine Learning @ CUED
A* Sampling
Machine Learning @ CUED
Orthologous networks in biological systems
Machine Learning @ CUED
Latent Branching Trees
Machine Learning @ CUED
Experiments with Non-parametric Topic Models
Machine Learning @ CUED
The Blended Paradigm: A Bayesian Approach to Handling Outliers and Misspecified Models
Machine Learning @ CUED
Machine Translation with LSTMs
โ ๏ธ Room changed to LR3B in Inglis building and time changed to 10:30
Machine Learning @ CUED
Oracle Variational Inference
Machine Learning @ CUED
Bayesian modeling for high-level real nursing activity recognition using accelerometers
โ ๏ธ short talk
Machine Learning @ CUED
A Tutorial on Probabilistic Programming
CUED Control Group Seminars
Optimal control and optimal sampling: A statistical physics perspective.
Machine Learning @ CUED
New Methods in Bayesian Optimization for Machine Learning
Machine Learning @ CUED
A marginal sampler for ฯ-Stable Poisson-Kingman mixture models
Machine Learning @ CUED
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
Machine Learning @ CUED
Unsupervised Many-to-many Object Matching
Machine Learning @ CUED
Probabilistic Numerics - a snapshot of an emerging community
Machine Learning @ CUED
On the Bethe approximation
Machine Learning @ CUED
Gaussian process regression on graphs
Machine Learning @ CUED
Implicit Representation Networks
Machine Learning @ CUED
Scalable Deep Gaussian Processes
Machine Learning @ CUED
Active Learning of Linear Embeddings for Gaussian Processes
Machine Learning @ CUED
Bayesian monitoring for the Comprehensive Nuclear-Test-Ban Treaty
Machine Learning @ CUED
Unifying logic and probability: A "New Dawn" for Artificial Intelligence?
โ ๏ธ NOTE THE SPECIAL VENUE (Microsoft Research)
Machine Learning @ CUED
Practical Machine Learning at Facebook. Examples and Lessons Learnt.
โ ๏ธ Room changed to LR4
Machine Learning @ CUED
Bayesian inference for integer-valued Lรฉvy processes with Non-Gaussian Ornstein-Uhlenbeck volatility modelling
โ ๏ธ short talk (30 mins) - NOTE TIME CHANGE
Machine Learning @ CUED
Density Estimation in Infinite Dimensional Exponential Families
Machine Learning @ CUED
Stable Poisson-Kingman species sampling priors generated by general ordered size biased generalized gamma mixing distributions
Machine Learning @ CUED
Probabilistic computing applications: BayesDB and stochastic digital circuits
Machine Learning @ CUED
Probabilistic computing for Bayesian inference
Machine Learning @ CUED
Machine Learning and Order Book Dynamics
Machine Learning @ CUED
Policy Evaluation with Temporal Differences
Machine Learning @ CUED
Matrix Means, Distances, Kernels, and Geometric Optimization
Machine Learning @ CUED
Learning to Learn for Structured Sparsity
Machine Learning @ CUED
Bayesian nonparametric dynamic-clustering and genetic imputation
Machine Learning @ CUED
Parameter estimation in deep learning architectures: Two new insights.
โ ๏ธ Note: time is 12 noon (*not* 11am)
Machine Learning @ CUED
Bayesian canonical correlation analysis
Machine Learning @ CUED
Cambridge - Tuebingen PhD Applicant Talks (via Skype)
โ ๏ธ Interview talks via Skype
Machine Learning @ CUED
Generative probabilistic programming: applications and new ideas
Machine Learning @ CUED
Bayesian nonparametrics: Dependency and Constraint Modeling
โ ๏ธ Note: TALK via Skype!
Machine Learning @ CUED
Bayesian Nonparametric Model for Power Disaggregation
Machine Learning @ CUED
Probabilistic machine learning for knowledge extraction from videos and text
โ ๏ธ Note venue: LR6
Machine Learning @ CUED
Frequentist coverage of adaptive nonparametric Bayesian credible sets
Machine Learning Reading Group @ CUED
Probabilistic Data Structures and Algorithms
Machine Learning @ CUED
Anglican; Particle MCMC inference for Probabilistic Programs
Machine Learning @ CUED
Sparse discriminative latent characteristics for predicting cancer drug sensitivity
Machine Learning @ CUED
Contrastive Learning Using Spectral Methods
Machine Learning @ CUED
Nonparametric Bayesian statistics with exchangeable random structures
Machine Learning @ CUED
Dissecting genotype to phenotype relationships
Machine Learning @ CUED
CANCELLED: Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
โ ๏ธ CANCELLED
Machine Learning @ CUED
Clustering Based on Predictive Variances in Gaussian Process Regression Models
Machine Learning @ CUED
Non-Parametric Conditional Random Fields in Computer Vision and Image Processing
Machine Learning @ CUED
Higher Order Learning for Classification in Emergency Situations
Machine Learning @ CUED
Annealing Between Distributions by Averaging Moments
NLIP Seminar Series
Recursive Deep Learning for Modeling Semantic Compositionality
Machine Learning @ CUED
Applied statistical genetics and next-generation association studies
Machine Learning @ CUED
Applied statistical genetics and next-generation association studies
Machine Learning @ CUED
The combinatorial structure of conditionally i.i.d. negative binomial processes directed by a beta process
jg691's list
Google's Approach to Building Relationships with Universities: Presentation and Talk by Dr David J Harper
Machine Learning @ CUED
Matrix Concentration Inequalities via the Method of Exchangeable Pairs
Machine Learning @ CUED
Deep Gaussian Processes
Machine Learning @ CUED
Approaches to statistical modeling of network data
Machine Learning @ CUED
Bayesian nonparametric methods for non-exchangeable data
Machine Learning @ CUED
Non-parametric Bayesian Chromatin State Segmentation
Machine Learning @ CUED
Fast Variational Inference in the Conjugate Exponential Family
Machine Learning @ CUED
An application of HDP And IBP for stream-based action recognition and high dimensional data
Machine Learning @ CUED
Using Context and Insight for the Analysis of LittleData?
Machine Learning @ CUED
Feature allocations, probability functions, and paintboxes
Machine Learning @ CUED
Modelling Reciprocating Relationships with Hawkes Processes
Machine Learning @ CUED
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables
Machine Learning @ CUED
The combinatorial structure underlying a beta processes is that of a continuum of Blackwell-MacQueen urn schemes
Machine Learning @ CUED
Learning of Milky Way Model Parameters Using Matrix-variate Data in a New Gaussian Process-based Method
Machine Learning @ CUED
Identification of causal effects
Machine Learning @ CUED
Probabilistic methods for biomolecular structure simulations
Computer Laboratory Systems Research Group Seminar
Distributed, Real-Time Bayesian Learning in Online Services
Machine Learning @ CUED
Compressed Sensing Applications in Functional Magnetic Resonance Imaging
Microsoft Research Cambridge, public talks
Convergent and Scalable Algorithms for Expectation Propagation Approximate Bayesian Inference
โ ๏ธ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Multi-Label Learning with Millions of Categories
Machine Learning @ CUED
Efficient Sampling with Kernel Herding
Machine Learning @ CUED
Frank-Wolfe optimization insights in machine learning
Machine Learning @ CUED
Deep learning for vision: a case study for visual textures, and some thoughts on a general framework
Machine Learning @ CUED
Thermodynamics as a Theory of Decision-Making with Information Processing Costs
Machine Learning @ CUED
Human Behavior Classification with Infinite Hidden Conditional Random Fields
Inference Group
Data Modelling With Neural Networks (II): Content-Addressable Memories And State-Of-The-Art Error-Correcting Codes
โ ๏ธ Last Lecture!
Machine Learning @ CUED
Fast Gaussian process learning for regression, semi-supervised classification, and multiway analysis
Machine Learning @ CUED
Non-parametric Bayesian Learning of User Preferences: Elicitation, Sparsification and Beyond
Machine Learning @ CUED
Structured Prediction using Linear Programming Relaxations
Inference Group
Advanced Monte Carlo Methods and Variational Inference
Machine Learning @ CUED
Discovery of Complex Behaviors through Contact-Invariant Optimization
Inference Group
Approximating Probability Distributions (I)-(II): Monte Carlo Methods and Variational Inference
โ ๏ธ Two lectures!
Inference Group
An Introduction to Statistical Inference, Data Modelling & Pattern Recogntion
โ ๏ธ Two Lectures!
Machine Learning @ CUED
Learning with nonparametric dependence and divergence estimation
Inference Group
Communication over noisy channels (II)
Machine Learning @ CUED
Past work and future interests: respectively, the scattering of Anyons and Monte Carlo methods
Machine Learning @ CUED
Non-parametric Bayesian Method and Maximum-A-Posteriori Inference in Statistical Machine Translation
Inference Group
Communication over noisy channels (I)
Machine Learning @ CUED
Infinite Structured Explicit Duration Hidden Markov Models
Inference Group
Entropy and data compression (II)
โ ๏ธ PLEASE NOTE: There will be no lectures during the Easter break (12 March - 9 April). Lectures will resume on 16 April.
Inference Group
Entropy and data compression (I)
Machine Learning @ CUED
Probabilistic computing: computation as universal stochastic inference, not deterministic calculation
Inference Group
Introduction to information theory
Machine Learning @ CUED
Scaling Machine Learning for the Internet
Machine Learning @ CUED
Bayesian Quadrature for Prediction and Optimisation
Machine Learning @ CUED
Efficient MCMC for Continuous Time Discrete State Systems
Machine Learning @ CUED
A Maximum Entropy Perspective on Spectral Dimensionality Reduction
Machine Learning @ CUED
Variational Inference for Non-Conjugate Models
Machine Learning @ CUED
Not so naive Bayesian classification
Machine Learning @ CUED
Bayesian Nonparametrics: Latent Feature and Prediction Models, and Efficient Inference
Machine Learning @ CUED
Machine Learning Markets
Machine Learning @ CUED
Factored Shapes and Appearances for Parts-based Object Understanding AND Transformation Equivariant Boltzmann Machines
Machine Learning @ CUED
Optimal Reinforcement Learning for Gaussian Systems
Machine Learning @ CUED
Exclusive Pรณlya Urns and their applications
Machine Learning @ CUED
Approximate Bayesian Inference for Large Scale Inverse Problems: A Computational Viewpoint
Machine Learning @ CUED
Some Practical Reflections on Graphical Models
Machine Learning @ CUED
Beyond Keyword Search: Discovering Relevant Scientific Literature
Machine Learning @ CUED
Graphical Models for Bandit Problems
Machine Learning @ CUED
Bayesian regression and classification with multivariate sparsifying priors
Machine Learning @ CUED
An FX trading system using adaptive reinforcement learning
โ ๏ธ (rescheduled from May 24th)
Machine Learning @ CUED
Censored Exploration in Dark Pools
Machine Learning @ CUED
An FX trading system using adaptive reinforcement learning
โ ๏ธ RESCHEDULED FOR JUNE 7th
Inference Group
Hierarchical hidden Markov models revisited
Inference Group
Mandarin Dasher
Machine Learning @ CUED
Characterization of the Ewens-Pitman family of random partitions by a deletion property and a de Finetti-type theorem for exchangeable hierarchies
Inference Group
Rigid body motion in conformal geometric algebra
Machine Learning @ CUED
Nonlinear Dynamics of Learning
Machine Learning @ CUED
Exponential Conditional Volatility Models
Inference Group
CRH: a Central Reference Handling system
Machine Learning @ CUED
Challenges in implementing the Bayesian paradigm
Machine Learning @ CUED
Expectation Propagation in Sparse Linear Models with Spike and Slab Priors
Machine Learning @ CUED
Probabilistic matrix factorization for reconstruction of missing data
Machine Learning @ CUED
Title to be confirmed
Inference Group
A computer-vision based facial gesture switch
Machine Learning @ CUED
Differential Geometric MCMC Methods
Machine Learning @ CUED
Mining viral datasets
Machine Learning @ CUED
Universal Bayesian Agents: Theory and Applications
Machine Learning @ CUED
Machine learning in cancer research (a.k.a CRI meets CUED)
โ ๏ธ (note new date)
Machine Learning @ CUED
Novi Quadrianto
Inference Group
Introduction to Geometric Algebra II
Machine Learning @ CUED
Learning item trees for collaborative filtering with implicit feedback
Inference Group
Title to be confirmed
Machine Learning @ CUED
Non-Smooth-Norm Image Reconstruction from Noisy Data
Machine Learning @ CUED
Crowdsourcing data modelling
Inference Group
Out-of-band Language Modelling
Inference Group
South Africa's Involvement With Square Kilometre Array Bid: Infrastructure and Electromagnetic Quietness
Machine Learning @ CUED
Short talks: Mixed Cumulative Distribution Networks; Nonparametric Bayesian community discovery in social networks; Expectation Propagation for Dirichlet Process Mixture Models
Inference Group
The Multiscale Composite Dirichlet Process and its applications
Physics of Medicine (PoM) Seminar Series
STORM below the localisation limit
Inference Group
Putting Some Language Back Into Language Modelling
Inference Group
Bayesian Psychometry
Inference Group
Continuous Gesture Recognition and Visualization
Machine Learning @ CUED
Searching for Knowledge Instead of Web Pages
Machine Learning @ CUED
Bayesian Inference with Kernels
Inference Group
Title to be confirmed
Inference Group
Ticker: Case Study
Machine Learning @ CUED
On the Equivalence of Graph Cuts and Max-product Belief Propagation
Inference Group
Statistical Language Modelling
Inference Group
Title to be confirmed
Inference Group
Topic Models -- and how to break them
Microsoft Research Cambridge, public talks
Information Asymmetries in Pay-Per-Bid Auctions: How Swoopo Makes Bank
Wednesday Seminars - Department of Computer Science and Technology
Some Uses of Hashing in Networking Problems
Machine Learning @ CUED
Culture wars, voting and polarization: divisions and unities in modern American politics
โ ๏ธ general audience talk (non-technical) -- NOTE change of room to LR4!
Machine Learning @ CUED
Creating structured and ๏ฌexible models: some open problems
Machine Learning @ CUED
Efficient Bayesian analysis of multiple changepoint models
Inference Group
Approximate Inference for the Loss-Calibrated Bayesian
Machine Learning @ CUED
Learning Common Grammar from Multilingual Corpus / Online Multiscale Dynamic Topic Models
Machine Learning @ CUED
Scalable Parallel Computing with CUDA
Inference Group
Introduction to Geometric Algebra
Inference Group
Dasher on a Space-filling curve
Machine Learning @ CUED
Using transformed domains to sparsify Gaussian Processes
Machine Learning @ CUED
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Inference Group
Bayesian head pose estimation
Inference Group
Differential Privacy and Probabilistic Inference
Machine Learning @ CUED
Natural Conjugate Gradient Learning for Fixed-Form Variational Bayes
Machine Learning @ CUED
Sparse Factor Analysis Applied to Three Biological Problems
Machine Learning @ CUED
Using topic models to help cure cancer
Machine Learning @ CUED
Structured Prediction Cascades
Machine Learning @ CUED
Parametric Bandits, Query Learning, and the Haystack Dimension
Inference Group
Robust Associative memory
Inference Group
Conditional Counting: Approximate Multinomial Probit Regression
Inference Group
Coherent Inference on Optimal Play in Games
Inference Group
Report from CHI 2010
Inference Group
Speech Dasher: Fast Writing using Speech and Gaze
Inference Group
Language Modelling with PPM
Machine Learning @ CUED
Slice sampling with latent Gaussian models
Inference Group
Bayesian feature tracking and head pose estimation
Machine Learning @ CUED
Dynamic Network Tomography: Model, Algorithm, Theory, and Application
Machine Learning @ CUED
Making Sense of Data - A Research Agenda
Machine Learning @ CUED
Subspace Codes for Adversarial Error-Correction in Network Coding
โ ๏ธ short talk
Machine Learning @ CUED
Bayesian Inference in Networks of Queues
Machine Learning @ CUED
Stochastic Outlier Selection
Inference Group
Neurally-inspired Computing
Inference Group
Latent Force Models with Gaussian Processes
Inference Group
Dasher
Machine Learning @ CUED
Mind Reading by Machine Learning: Optimal Experimental Design
โ ๏ธ Short talk
Inference Group
NIPS 2009 highlights
HEP phenomenology joint Cavendish-DAMTP seminar
Accelerated Bayesian inference in cosmology
Machine Learning @ CUED
Machine Learning Course (4F13)
โ ๏ธ 4F13 course - repeats Wed, Thurs at 10am during Lent
Machine Learning @ CUED
Machine Learning Course (4F13)
โ ๏ธ 4F13 course - repeats Wed, Thurs at 10am during Lent
Inference Group
Title to be confirmed
Inference Group
NIPS poster
Inference Group
Face Recognition: Who's That Blonde?
Inference Group
Probabilistic feature tracking with non-probabilistic techniques
Machine Learning @ CUED
Indian Buffet Processes with Power-law Behaviour
Machine Learning @ CUED
Gaussian Processes for Active Data Selection, Faults, Changepoints and Sensor Selection
Inference Group
็ ็ฉถใฎ็ดนไป
Machine Learning @ CUED
KL control theory and decision making under uncertainty
Inference Group
Optimizing Dasher for the iPhone
Machine Learning @ CUED
Information theoretic model selection in clustering
Inference Group
A facial gesture switch using hidden Markov models
Inference Group
Inclusive user modelling
Inference Group
Learning Logical Relations
Machine Learning @ CUED
Motor Skills Learning for Robotics
Inference Group
Recognition and Correction of Voice Web Search Queries
Machine Learning @ CUED
Coconut: Optimizing computations for machine learning
Inference Group
Of Uncertain Value -- Bayesian Quadratic Reinforcement Learning
Inference Group
Capacity of Spiking Neural Networks
Inference Group
Thermal expansion of beta-eucryptite
Machine Learning @ CUED
Convex Variational Bayesian Inference for Large Scale Generalized Linear Models
Inference Group
Text entry performance of state of the art unconstrained handwriting recognition: a longitudinal user study
Inference Group
Combining Collaborative Filtering with Meta Data for Scalable Recommendations
โ ๏ธ Note unusual time
Machine Learning @ CUED
Computable Probability Theory
Inference Group
Physics Dimensions in Sage
Inference Group
Dashing this way and that
Inference Group
Mapping facial gestures to control cursors and switches
Inference Group
A quick way to learn a mixture of exponentially many linear models
โ ๏ธ Note unusual time
Inference Group
Short ICML Practice Talk
Machine Learning @ CUED
Using gradient descent for optimization and learning
Inference Group
Useful Computation with Coincidence Detection
Inference Group
Bayesian periodicity detection
Inference Group
Literature review presentations - Tidal power, and Fuel cells
โ ๏ธ Note unusual time
Inference Group
Deducing Principles in Natural Perception
Machine Learning @ CUED
Learning from Measurements in Exponential Families
Inference Group
Geothermal power in Australia
Inference Group
Cloud Ships for global warming reduction
Inference Group
Painless Parse Errors
Machine Learning @ CUED
Shrinkage regression for multivariate inference with missing data, with an application to portfolio balancing
Machine Learning @ CUED
Quasi-linear Sensor Management
Optimization and Incentives Seminar
A Survey of Results for Deletion Channels and Related Synchronization Channels
โ ๏ธ Joint With Probability Seminar Series.
Kuwait Foundation Lectures
Bloom Filters, Related Data Structures, and their Applications
Machine Learning @ CUED
Generalization in Learning
Machine Learning @ CUED
Stochastic control as an inference problem
Inference Group
More NIPS highlights
Machine Learning @ CUED
Extending the Affinity Propagation Model
Inference Group
NIPS Highlights
Machine Learning @ CUED
The Block Diagonal Infinite Hidden Markov Model
Machine Learning @ CUED
Probabilistic Graph Models for Debugging Software
Machine Learning @ CUED
HMMs for Protein Sequencing from Mass Spectrometry Data
Inference Group
Optimizing Similarity Functions in Various Pattern Recognition Problems
โ ๏ธ Note unusual time
Inference Group
Visual Search: Applications and Computational Modelling
Inference Group
Eye-Tracking in Virtual Reality
Machine Learning @ CUED
Mondrian Processes
Inference Group
Estimating the Pen Trajectories of Handwritten Static Scripts using Hidden Markov Models
Cavendish Physical Society
Power to Choose: How the energy choices people make will change their lives.
Inference Group
Bayesian Inductive Programming
Machine Learning @ CUED
Hardness Ratios
Inference Group
Thermochemistry and Nuclear power - Clean responsive electricity
Machine Learning @ CUED
Consensus finding, exponential models and infinite rankings
Inference Group
Low-cost large-scale renewable energy
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part III)
Wednesday Seminars - Department of Computer Science and Technology
Forensic Genomics:Kin Privacy, Driftnets and Other Open Questions
Machine Learning @ CUED
Efficient Sequential Monte Carlo Inference for Kingman's Coalescent
โ ๏ธ Note unusual time
Machine Learning @ CUED
Context in human robot interaction
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part II)
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part I)
Machine Learning @ CUED
Shared Segmentation of Natural Scenes using Dependent Pitman-Yor Processes
Machine Learning @ CUED
Spoken Dialogue Management
Machine Learning @ CUED
A Bayesian approach to language learning
Machine Learning @ CUED
Learning Bigrams from Unigrams
Machine Learning @ CUED
Nonparametric Bayesian Natural Language Model Domain Adaptation: A Hierarchical, Hierarchical Pitman-Yor Process Language Model
Inference Group
Clustering by linear programming, convex optimization and belief propagation
โ ๏ธ Note unusual time
Machine Learning @ CUED
Bayesian approaches to autonomous Bayesian real-time learning
Machine Learning @ CUED
Non-negative matrix factorization with Gaussian process priors
Machine Learning @ CUED
Matrix Factorization and Relational Learning
Inference Group
Artificial intelligence in combinatorial game design
Inference Group
From Automated Currency Validation to Protein Fold Recognition: Probabilistic Multi-class Multi-kernel Learning
Inference Group
Modeling with Bounded Partition Functions
Machine Learning @ CUED
Double Feature: Optimal Precoding for MIMO and Divergence Estimation for Continuous Distributions
Machine Learning @ CUED
Nonparametric Bayesian Learning of Switching Dynamical Systems
Machine Learning @ CUED
Message-passing inference on graphical models
Microsoft Research Cambridge, general interest public talks
The Geometry of the Space of Shapes
โ ๏ธ Hosted by Prof. Andrew Blake (Microsoft Research)
Machine Learning @ CUED
Bayesian analysis of complex biological systems
Inference Group
Bayesian Models for Dependency Parsing Using Pitman-Yor Priors
Machine Learning @ CUED
Variational inference for partially observed diffusion processes
Machine Learning @ CUED
Assessing high-dimensional latent variable models
Machine Learning @ CUED
Identification of dynamical subpopulations, optimal experiment design and more
Inference Group
Gaussian Process Density Sampler
Machine Learning @ CUED
H-Infinity Clustering
Machine Learning @ CUED
Sustainable Energy - without the hot air
Machine Learning @ CUED
Modeling Behaviour in Economic Games using Game-Theoretic POMDPs
Machine Learning @ CUED
Talking with Robots: A Case Study in Architectures for Cognitive Robotics
Machine Learning @ CUED
An Introduction to Statistical Learning Theory
โ ๏ธ Machine Learning Tutorial Lecture
Inference Group
Noise in the central nervous system
Inference Group
On Sparsity and Overcompleteness in Image Models
Inference Group
Treed Gaussian Processes for Regression and Classification
โ ๏ธ Note new date and time.
Machine Learning @ CUED
Beam Sampling for Infinite Hidden Markov Models
Machine Learning @ CUED
Learning quantum physics
Inference Group
(clashing event: Sustainable Energy and Arithmetic)
โ ๏ธ Computer Lab seminar clashes with normal group meeting
Machine Learning @ CUED
Convergence analysis of the EM algorithm and joint minimization of free energy
Machine Learning @ CUED
Discriminative Methods with Structure
Machine Learning @ CUED
A Bayesian approach to network modularity: inferring the structure and scale of modular networks
Inference Group
How nervous systems are designed to be energy efficient
Machine Learning @ CUED
Model selection and model order adaptation for clustering
Machine Learning @ CUED
Expectation Propagation, Experimental Design for the Sparse Linear Model
Machine Learning @ CUED
Biomedical Image Search
Inference Group
Bayesian learning of visual chunks by human observers
Machine Learning @ CUED
Stable distribution and data sketching
Inference Group
Non-Rigid Photometric Stereo with Colored Lights
Machine Learning @ CUED
Reinforcement Learning
โ ๏ธ Machine Learning Tutorial Lecture (Note unusual day)
Machine Learning @ CUED
Modeling Science: Topic models of Scientific Journals and Other Large Document Collections
Machine Learning @ CUED
Sparse Gaussian Process in Disease Mapping
Inference Group
NIPS presentations
Inference Group
The development of a motor network in Drosophila, the role of precocious neural activity in the emergence of coordination
Machine Learning @ CUED
Gene Regulatory Network Inference: A Kernel-Based Learning Approach
Machine Learning @ CUED
Clinical data based optimal STI strategies for HIV: a reinforcement learning approach
Inference Group
Life beyond the Physics department
Machine Learning @ CUED
Machine Learning Applications / Challenges in Natural Language Parsing
โ ๏ธ Machine Learning Tutorial Lecture
Machine Learning @ CUED
An overview of covariance operators in Hilbert space, and their applications
Machine Learning @ CUED
Optimal Control and Reinforcement Learning with Gaussian Process Models
Inference Group
The last thing that we should talk about
Inference Group
Bliss-Dasher -- Efficient semantic writing with any muscle
Machine Learning @ CUED
Hidden Common Cause Relations in Relational Learning
Machine Learning @ CUED
Optimal Spreading Sequences for Chaos-Based Communication Systems; Using CSK as a Case Study
Machine Learning and Inference (One day meeting)
Mingle
Machine Learning and Inference (One day meeting)
Developing a general-purpose message-passing library
โ ๏ธ Registration is required for this one-day meeting
Machine Learning and Inference (One day meeting)
Short talks about statistical machine learning IIII
Machine Learning and Inference (One day meeting)
Discussion + tea
Machine Learning and Inference (One day meeting)
Short talks about statistical machine learning III
Machine Learning and Inference (One day meeting)
Inference in Gaussian Process models
Machine Learning and Inference (One day meeting)
Short talks about statistical machine learning II
Machine Learning and Inference (One day meeting)
coffee
Machine Learning and Inference (One day meeting)
Short talks about statistical machine learning
Machine Learning and Inference (One day meeting)
Sequencing hundreds of humans - how to collect the data efficiently and make sense of it
โ ๏ธ Registration is required for this one-day meeting
Machine Learning and Inference (One day meeting)
Coffee + mingle
Machine Learning @ CUED
Graph Kernels for Data Mining
Machine Learning @ CUED
Geometric Algorithms for Linear Independent Component Analysis
Machine Learning @ CUED
Covariate Shift Adaptation: Supervised Learning When Training and Test Inputs Have Different Distributions
Inference Group
Five energy plans for Britain
Inference Group
What is a Fab Lab?
Inference Group
Language Modelling Discussion
Inference Group
Open Vocabulary Confusion Networks for Speech Recognition
Inference Group
An Error-Correcting Keyboard
Inference Group
Probabilities, Complexities and Game Semantics
Inference Group
Learning and Stability in Random Sigma-Pi Networks
Inference Group
Shape writing: fluid text entry on mobile devices
Inference Group
Bayesian nonparametric latent feature models
Inference Group
Context Tree Weighting
Probabilistic Systems, Information, and Inference Group Seminars
Analysis of Channel Coding for Redundant Sources
Inference Group
Inductive Logic Programming and Kernel methods
Inference Group
Inference in Bayesian Networks using Dynamic Discretisation
โ ๏ธ Note unusual time
Inference Group
Logistic regression with a Laplacian prior on the singular values: convex duality and application to EEG classification.
โ ๏ธ Note unusual time and location
Inference Group
Monte Carlo Approaches to Game Playing
Machine Learning @ CUED
Logistic Regression with a Laplacian prior on the Eigenvalues: Convex duality and application to EEG classification
โ ๏ธ Note different room and time
Inference Group
Gaussian Process Product Models
Inference Group
Metropolis-Coupled MCMC for Nested Sampling
Machine Learning @ CUED
Dirichlet Processes and Hierarchical Dirichlet Processes
โ ๏ธ Advanced Machine Learning Tutorial Lecture
Inference Group
Some interesting data: Cryptic DNA sequence periodicities are ubiquitous, organism specific and distinguish introns from exons.
Machine Learning @ CUED
An Introduction to Non-parametric Bayesian Methods
โ ๏ธ Room changed this week!
Inference Group
NIPS presentations
Machine Learning @ CUED
Robot Localisation and Mapping
โ ๏ธ Advanced Machine Learning Tutorial Lecture
Inference Group
Modeling text with Dirichlet compound multinomial distributions
Inference Group
Learning from positive and unlabeled data
Inference Group
HMMs for controlling dynamical systems
Inference Group
Robust Gaussian Process Regression and Applications
Inference Group
Transcription regulation: from parts list to genomic network.
Inference Group
Bayesian estimation of neuronal connectivity from MEA recordings
Inference Group
Vector Gaussian Processes
Inference Group
Do Scanning Electron Microscopes have a point-spread function?
Machine Learning @ CUED
Gaussian Processes for Machine Learning
โ ๏ธ Advanced Machine Learning Tutorial Lecture
Inference Group
Dynamic imaging of blood flow in the conjunctival and episcleral microcirculations of the human eye: a new chapter in diagnostic imaging?
Inference Group
Teaching Statistics - A Bag of Tricks
Inference Group
Leaf Selection for Maximising Diversity in a Tree
Machine Learning @ CUED
Mixture Models and the EM Algorithm
โ ๏ธ Advanced Machine Learning Tutorial Lecture
Inference Group
Optimised Digital Fountain Codes for Medium Blocklengths
Inference Group
An interesting preprint
Inference Group
MCMC for doubly-intractable distributions
Inference Group
How to veto anonymously under surveillance?
Inference Group
Naive OCR?
Inference Group
Cognitive Cars
Inference Group
neural phase codes
Inference Group
Graphical Combinator Programming
Inference Group
The Manufacture of Bioethanol - A brief Introduction
Inference Group
Divergence measures and latent variable models
Inference Group
Implicit learning in Music
Inference Group
On the Analysis of Ellipsoid Approximation of Nested Sampling
Inference Group
Gaussian Process models for solving ODEs
Computer Modelling in Biology
Ligand detection and discrimination by spatial relocalisation: the complexities of T cell activation
Inference Group
Music Access for the Disabled
Inference Group
Blissymbolics - The Emergence of a Written Language
Inference Group
In search of the brain's wiring
Inference Group
A Bayesian Unfolding Method Applied to the PAMELA Experiment
Inference Group
Blissymbolics
Inference Group
Finite element modelling of growing microtubules
Inference Group
Title to be confirmed
Inference Group
How to Parameterize a Positive-definite Matrix
Inference Group
To be confirmed
Inference Group
The Capacity of the Deletion Channel
Inference Group
Bayesian approximation for physical inverse problems
Inference Group
Hierarchical Passage Retrieval
Inference Group
Bayesian Online Changepoint Detection
Inference Group
Epitomic representation of natural signals
Inference Group
Aggregation of Particles in Stochastic Flows
Machine Learning @ CUED
Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model
Inference Group
Speech Recognition During Dictation Corrections
Inference Group
Topic Modeling: Beyond Bag-of-Words
Inference Group
Uncertainty Logic
Inference Group
Experimental Annotation of the Human Genome
Inference Group
Virtual-move Parallel Tempering
Inference Group
Latency-optimal fault-tolerant replication
Inference Group
Dynamical Networks for Agent Systems
Inference Group
Recognition and prediction of biological motion
Inference Group
Eye Tracking with Consumer Hardware
Inference Group
Cheap and cheerful inference machines for tracking
Inference Group
Efficient Quantum Computation with linear optics
Inference Group
Locked-in syndrome
Inference Group
Monte Carlo is Bayesian
Inference Group
Dasher for the blind, and some visual illusions
Inference Group
A Cross-Entropy Based Method to Analyse Iterative Decoding
Inference Group
Bayesian Pattern Ranking for Move Prediction in the Game of Go
Inference Group
Adaptive Monte Carlo Methods for Simulation and Optimization
Inference Group
Language relationships, phonology, and Greek
Inference Group
Efficient communication with buttons: an update on Button Dasher
Inference Group
Progress with #Dasher, a Continuous Gesture IDE
Inference Group
Why do fruitflies like bananas?
Inference Group
An introduction to Brain-Computer Interfaces
Inference Group
To be confirmed
Inference Group
LOCUS: Learning Object Classes with Unsupervised Segmentation
Inference Group
Efficient computer interfaces using continuous gestures, language models, and speech; Probabilistic methods for interpreting electronic ink
Inference Group
A Fast Learning Algorithm for Deep Belief Nets
Inference Group
Epitomes And Epitexts
Inference Group
Monte Carlo experiments in computer Go
Inference Group
Language Modelling In Dasher: A Tutorial
Inference Group
Hierarchical Dirichlet Process Models for Time Series Data
Inference Group
Japanese Language Model
Inference Group
A Tale of T9
Inference Group
Information and Intelligence
Inference Group
Neighbourhood Components Analysis
Inference Group
Nested Sampling for Motif Discovery
Inference Group
Joint Gaussian Process-Density Mixtures
Inference Group
Computer Othello
Inference Group
Variational Gibbs Sampling
Inference Group
Astronomical Image Recognition
Inference Group
Bloom Filters and Tries (a comparison)
Inference Group
The predictive power of contractive neural-network spaces
Inference Group
Bayesian Learning for Visual Inference
Inference Group
Approximating The Hierarchical Dirichelet Distribution
Please see above for contact details for this list.