Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

Recent work has shown how to embed differentiable optimization problems (that is,
problems whose solutions can be backpropagated through) as layers within deep learning …

The empirical success of reinforcement learning (RL) in contact-rich manipulation leaves
much to be understood from a model-based perspective, where the key difficulties are often …

We explore a class of end-to-end learnable models wherein data processing nodes (or
network layers) are defined in terms of desired behavior rather than an explicit forward …

The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …

Solving optimization problem is the key to decision making in many real-life analytics
applications. However, the coefficients of the optimization problems are often uncertain and …

Bridging logical and algorithmic reasoning with modern machine learning techniques is a
fundamental challenge with potentially transformative impact. On the algorithmic side, many …

Weather forecast models are essential for sustainable energy systems. However, forecast
accuracy may not be the best metric for developing forecast models. A more or less …

Optimization problems with nonlinear cost functions and combinatorial constraints appear in
many real-world applications but remain challenging to solve efficiently compared to their …

In this work, we develop methods for few-shot image classification from a new perspective of
optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as …