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Found 6 papers, 1 papers with code
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript
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.
Multi-fidelity Stability for Graph Representation Learning
In this example, we provide non-asymptotic bounds that highly depend on the sparsity of the receptive field constructed by the algorithm.
PAC-Learning Uniform Ergodic Communicative Networks
This work addressed the problem of learning a network with communication between vertices.
Achievability and Impossibility of Exact Pairwise Ranking
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
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.
Dynamic Graph Representation Learning with Fourier Temporal State Embedding
In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.
