This paper surveys the recent attempts at leveraging machine learning to solve constrained
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …

We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …

We find that across a wide range of robot policy learning scenarios, treating supervised
policy learning with an implicit model generally performs better, on average, than commonly …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Deep learning has unlocked new paths towards the emulation of the peculiarly-human
capability of learning from examples. While this kind of bottom-up learning works well for …

We present a new approach to modeling sequential data: the deep equilibrium model
(DEQ). Motivated by an observation that the hidden layers of many existing deep sequence …

Attempts at combining logic and neural networks into neurosymbolic approaches have been
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …

Abstract Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with
methods that are based on artificial neural networks–has a long-standing history. In this …

Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained
end-to-end to solve logical reasoning problems presented in natural language? We attempt …

Large optimization problems with hard constraints arise in many settings, yet classical
solvers are often prohibitively slow, motivating the use of deep networks as cheap" …