111 citations · 182 across the 3 of their papers we have counts for
6 papers
Context-Aware Legal Citation Recommendation using Deep Learning
Zihan Huang, Charles Low, Mengqiu Teng +4
Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions. In this paper, we develop a citation recommendation tool th…
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Chaosheng Dong, Xiaojie Jin, Weihao Gao +5
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments. The sheer volume of streaming training…
Fixup Initialization: Residual Learning Without Normalization
Hongyi Zhang, Yann N. Dauphin, Tengyu Ma
Normalization layers are a staple in state-of-the-art deep neural network architectures. They are widely believed to stabilize training, enable higher learning rate, accelerate con…
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate
Jingzhao Zhang, Hongyi Zhang, Suvrit Sra
We study smooth stochastic optimization problems on Riemannian manifolds. Via adapting the recently proposed SPIDER algorithm \citep{fang2018spider} (a variance reduced stochastic…
Towards Riemannian Accelerated Gradient Methods
Hongyi Zhang, Suvrit Sra
We propose a Riemannian version of Nesterov's Accelerated Gradient algorithm (RAGD), and show that for geodesically smooth and strongly convex problems, within a neighborhood of th…
First-order Methods for Geodesically Convex Optimization
Hongyi Zhang, Suvrit Sra
Geodesic convexity generalizes the notion of (vector space) convexity to nonlinear metric spaces. But unlike convex optimization, geodesically convex (g-convex) optimization is muc…