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Pure transformers are powerful graph learners
We show that standard Transformers without graph-specific modifications can lead to
promising results in graph learning both in theory and practice. Given a graph, we simply …
promising results in graph learning both in theory and practice. Given a graph, we simply …
Rethinking attention with performers
We introduce Performers, Transformer architectures which can estimate regular (softmax)
full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to …
full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to …
Random feature attention
Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their
core is an attention function which models pairwise interactions between the inputs at every …
core is an attention function which models pairwise interactions between the inputs at every …
Monarch: Expressive structured matrices for efficient and accurate training
Large neural networks excel in many domains, but they are expensive to train and fine-tune.
A popular approach to reduce their compute or memory requirements is to replace dense …
A popular approach to reduce their compute or memory requirements is to replace dense …
Federated learning: Strategies for improving communication efficiency
Federated Learning is a machine learning setting where the goal is to train a high-quality
centralized model while training data remains distributed over a large number of clients …
centralized model while training data remains distributed over a large number of clients …
Random features for kernel approximation: A survey on algorithms, theory, and beyond
The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …
Distributed mean estimation with limited communication
Motivated by the need for distributed learning and optimization algorithms with low
communication cost, we study communication efficient algorithms for distributed mean …
communication cost, we study communication efficient algorithms for distributed mean …
Modeling the influence of data structure on learning in neural networks: The hidden manifold model
Understanding the reasons for the success of deep neural networks trained using stochastic
gradient-based methods is a key open problem for the nascent theory of deep learning. The …
gradient-based methods is a key open problem for the nascent theory of deep learning. The …
Multiplicative filter networks
Although deep networks are typically used to approximate functions over high dimensional
inputs, recent work has increased interest in neural networks as function approximators for …
inputs, recent work has increased interest in neural networks as function approximators for …
Memory attention networks for skeleton-based action recognition
Skeleton-based action recognition has been extensively studied, but it remains an unsolved
problem because of the complex variations of skeleton joints in 3-D spatiotemporal space …
problem because of the complex variations of skeleton joints in 3-D spatiotemporal space …