10 citations · 26 across the 12 of their papers we have counts for
7 papers · 1 filter
ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
Heng Zhao, Zilei Shao, Guy Van den Broeck +1
Mixture-of-Experts (MoE) models scale by activating only a small subset of experts per token. However, training such models remains challenging because top- routing is discrete…
Deep Generative Models with Hard Linear Equality Constraints
Ruoyan Li, Dipti Ranjan Sahu, Guy Van den Broeck +1
While deep generative models~(DGMs) have demonstrated remarkable success in capturing complex data distributions, they consistently fail to learn constraints that encode domain kno…
A Unified Approach to Count-Based Weakly-Supervised Learning
Vinay Shukla, Zhe Zeng, Kareem Ahmed +1
High-quality labels are often very scarce, whereas unlabeled data with inferred weak labels occurs more naturally. In many cases, these weak labels dictate the frequency of each re…
Probabilistically Rewired Message-Passing Neural Networks
Chendi Qian, Andrei Manolache, Kareem Ahmed +4
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring pote…
Collapsed Inference for Bayesian Deep Learning
Zhe Zeng, Guy Van den Broeck
Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty in deep learning. Current inference approaches for BNNs often resort to few-sample estimat…
SIMPLE: A Gradient Estimator for -Subset Sampling
Kareem Ahmed, Zhe Zeng, Mathias Niepert +1
-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity. The challenge lies in rendering -subset sampling amenable t…
Tractable Computation of Expected Kernels
Wenzhe Li, Zhe Zeng, Antonio Vergari +1
Computing the expectation of kernel functions is a ubiquitous task in machine learning, with applications from classical support vector machines to exploiting kernel embeddings of…