Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by
its unnormalized (log-) probabilities. Over the past years, the machine learning community …

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently,
provably boosting the expressive power of standard (message-passing) GNNs. However …

Abstract The Mixture-of-Experts (MoE) architecture is showing promising results in improving
parameter sharing in multi-task learning (MTL) and in scaling high-capacity neural networks …

Modern 3D object detectors have immensely benefited from the end-to-end learning idea.
However, most of them use a post-processing algorithm called Non-Maximal Suppression …

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 …

Using machine learning to solve combinatorial optimization (CO) problems is challenging,
especially when the data is unlabeled. This work proposes an unsupervised learning …

Objective. To develop an efficient, embedded electroencephalogram (EEG) channel
selection approach for deep neural networks, allowing us to match the channel selection to …

Neural graphics primitives are faster and achieve higher quality when their neural networks
are augmented by spatial data structures that hold trainable features arranged in a grid …

The integration of algorithmic components into neural architectures has gained increased
attention recently, as it allows training neural networks with new forms of supervision such …