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Integrating Combinatorial Solvers and Neural Models

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If you have a question about this talk, please contact Richard Diehl Martinez .

Neural models—including language models such as ChatGPT—can exhibit remarkable abilities; paradoxically, they also struggle with algorithmic tasks where much simpler models excel. To solve these issues, we propose Implicit Maximum Likelihood Estimation (IMLE), a framework for end-to-end learning of models combining algorithmic combinatorial solvers and differentiable neural components, which allows us to incorporate planning and reasoning algorithms in neural architectures by just adding a simple decorator [1, 2].

[1] Implicit MLE : Backpropagating Through Discrete Exponential Family Distributions. https://arxiv.org/abs/2106.01798, NeurIPS 2021 [2] Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models. https://arxiv.org/abs/2209.04862, AAAI 2023

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This talk is part of the NLIP Seminar Series series.

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In person in the LMB's Klug Seminar Room (CB2 0QH) and via Zoom, link: https://mrc-lmb-cam-ac-uk.zoom.us/j/96568201418?pwd=c3F0aCtOeTVwR3IyQzZYdkxoWHdPUT09 Hicks & Mead Rooms, University Centre, Granta Place Zoom link: https://cam-ac-uk.zoom.us/j/4751389294?pwd=Z2ZOSDk0eG1wZldVWG1GVVhrTzFIZz09

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