Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions (Paper Explained)

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions (Paper Explained)

Dec 01, 2021
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#imle #backpropagation #discrete Backpropagation is the workhorse of deep learning, but unfortunately, it only works for continuous functions that are amenable to the chain rule of differentiation. Since discrete algorithms have no continuous derivative, deep networks with such algorithms as part of them cannot be effectively trained using backpropagation. This paper presents a method to incorporate a large class of algorithms, formulated as discrete exponential family distributions, into deep networks and derives gradient estimates that can easily be used in end-to-end backpropagation. This enables things like combinatorial optimizers to be part of a network's forward propagation natively. OUTLINE: 0:00 - Intro & Overview 4:25 - Sponsor: Weights & Biases 6:15 - Problem Setup & Contributions 8:50 - Recap: Straight-Through Estimator 13:25 - Encoding the discrete problem as an inner product 19:45 - From algorithm to distribution 23:15 - Substituting the gradient 26:50 - Defining a target distribution 38:30 - Approximating marginals via perturb-and-MAP 45:10 - Entire algorithm recap 56:45 - Github Page & Example Paper: https://arxiv.org/abs/2106.01798 Code (TF): https://github.com/nec-research/tf-imle Code (Torch): https://github.com/uclnlp/torch-imle Our Discord: https://discord.gg/4H8xxDF Sponsor: Weights & Biases https://wandb.com Abstract: Combining discrete probability distributions and combinatorial optimization problems with neural network components has numerous applications but poses several challenges. We propose Implicit Maximum Likelihood Estimation (I-MLE), a framework for end-to-end learning of models combining discrete exponential family distributions and differentiable neural components. I-MLE is widely applicable as it only requires the ability to compute the most probable states and does not rely on smooth relaxations. The framework encompasses several approaches such as perturbation-based implicit differentiation and recent methods to differentiate through black-box combinatorial solvers. We introduce a novel class of noise distributions for approximating marginals via perturb-and-MAP. Moreover, we show that I-MLE simplifies to maximum likelihood estimation when used in some recently studied learning settings that involve combinatorial solvers. Experiments on several datasets suggest that I-MLE is competitive with and often outperforms existing approaches which rely on problem-specific relaxations. Authors: Mathias Niepert, Pasquale Minervini, Luca Franceschi Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

0:00 - Intro & Overview 4:25 - Sponsor: Weights & Biases 6:15 - Problem Setup & Contributions 8:50 - Recap: Straight-Through Estimator 13:25 - Encoding the discrete problem as an inner product 19:45 - From algorithm to distribution 23:15 - Substituting the gradient 26:50 - Defining a target distribution 38:30 - Approximating marginals via perturb-and-MAP 45:10 - Entire algorithm recap 56:45 - Github Page & Example
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