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.
In this example, we provide non-asymptotic bounds that highly depend on the sparsity of the receptive field constructed by the algorithm.
This work addressed the problem of learning a network with communication between vertices.
We consider the problem of recovering the rank of a set of $n$ items based on noisy pairwise comparisons.
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.
In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.