14 citations · 15 across the 3 of their papers we have counts for
4 papers
Grounded Graph Decoding Improves Compositional Generalization in Question Answering
Yu Gai, Paras Jain, Wendi Zhang +3
Question answering models struggle to generalize to novel compositions of training patterns, such to longer sequences or more complex test structures. Current end-to-end models lea…
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training
Yatong Bai, Tanmay Gautam, Yu Gai +1
Recent work has shown that the training of a one-hidden-layer, scalar-output fully-connected ReLU neural network can be reformulated as a finite-dimensional convex program. Unfortu…
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Jianfei Chen, Yu Gai, Zhewei Yao +2
Fully quantized training (FQT), which uses low-bitwidth hardware by quantizing the activations, weights, and gradients of a neural network model, is a promising approach to acceler…
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…