Search Results for author: Yihan He

Found 6 papers, 1 papers with code

Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript

no code implementations ICML 2020 Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui

Recent years have witnessed intensive research interests on training deep neural networks (DNNs) more efficiently by quantization-based compression methods, which facilitate DNNs training in two ways: (1) activations are quantized to shrink the memory consumption, and (2) gradients are quantized to decrease the communication cost.

Quantization

Multi-fidelity Stability for Graph Representation Learning

no code implementations25 Nov 2021 Yihan He, Joan Bruna

In this example, we provide non-asymptotic bounds that highly depend on the sparsity of the receptive field constructed by the algorithm.

Graph Representation Learning Structured Prediction

PAC-Learning Uniform Ergodic Communicative Networks

no code implementations21 Nov 2021 Yihan He

This work addressed the problem of learning a network with communication between vertices.

Achievability and Impossibility of Exact Pairwise Ranking

no code implementations19 Nov 2021 Yihan He

We consider the problem of recovering the rank of a set of $n$ items based on noisy pairwise comparisons.

LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks

no code implementations1 Jan 2021 Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian

In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.

Graph Representation Learning

Dynamic Graph Representation Learning with Fourier Temporal State Embedding

1 code implementation1 Jan 2021 Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian

In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.

Graph Embedding Graph Representation Learning

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