151 citations · 389 across the 13 of their papers we have counts for
7 papers · 1 filter
Building Efficient Deep Neural Networks with Unitary Group Convolutions
Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2
We propose unitary group convolutions (UGConvs), a building block for CNNs which compose a group convolution with unitary transforms in feature space to learn a richer set of repre…
Minibatch Gibbs Sampling on Large Graphical Models
Christopher De Sa, Vincent Chen, Wing Wong
Gibbs sampling is the de facto Markov chain Monte Carlo method used for inference and learning on large scale graphical models. For complicated factor graphs with lots of factors,…
Channel Gating Neural Networks
Weizhe Hua, Yuan Zhou, Christopher De Sa +2
This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channe…
Representation Tradeoffs for Hyperbolic Embeddings
Christopher De Sa, Albert Gu, Christopher Ré +1
Hyperbolic embeddings offer excellent quality with few dimensions when embedding hierarchical data structures like synonym or type hierarchies. Given a tree, we give a combinatoria…
High-Accuracy Low-Precision Training
Christopher De Sa, Megan Leszczynski, Jian Zhang +4
Low-precision computation is often used to lower the time and energy cost of machine learning, and recently hardware accelerators have been developed to support it. Still, it has b…
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory
Dan Alistarh, Christopher De Sa, Nikola Konstantinov
Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the optimization backbone for training several classic models, from regression to neu…