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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