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We build on the recently proposed EigenGame that views eigendecomposition as a competitive game. EigenGames updates are biased if computed using minibatches of data, which hinders convergence and more sophisticated parallelism in the stochastic setting. In this work, we propose an unbiased stochastic update that is asymptotically equivalent to EigenGame, enjoys greater parallelism allowing computation on datasets of larger sample sizes, and outperforms EigenGame in experiments. We present applications to finding the principal components of massive datasets and performing spectral clustering of graphs. We analyze and discuss our proposed update in the context of EigenGame and the shift in perspective from optimization to games.
The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. A common practice for this task is to train a model on the base set first and then transfer to novel classes through fine-
Is deep learning over-hyped? Where are the case studies that compare state-of-the-art deep learners with simpler options? In response to this gap in the literature, this paper offers one case study on using deep learning to predict issue close time i
As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intrigu
Random forests (RF) and deep networks (DN) are two of the most popular machine learning methods in the current scientific literature and yield differing levels of performance on different data modalities. We wish to further explore and establish the
We study local SGD (also known as parallel SGD and federated averaging), a natural and frequently used stochastic distributed optimization method. Its theoretical foundations are currently lacking and we highlight how all existing error guarantees in