We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attention-kernels, Performers use a novel Fast Attention Via positive Orthogonal Random features approach (FAVOR+), which may be of independent interest for scalable kernel methods. FAVOR+ can be also used to efficiently model kernelizable attention mechanisms beyond softmax. This representational power is crucial to accurately compare softmax with other kernels for the first time on large-scale tasks, beyond the reach of regular Transform...
Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. The self-at...
Transformers allow attention between all pairs of tokens, but there is reason to believe that most o...
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of ch...
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-at...
The attention mechanism is considered the backbone of the widely-used Transformer architecture. It c...
Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkab...
Large transformer models have achieved state-of-the-art results in numerous natural language process...
Transformer models have achieved state-of-the-art results across a diverse range of domains. However...
To overcome the quadratic cost of self-attention, recent works have proposed various sparse attentio...
Pretrained transformer models have demonstrated remarkable performance across various natural langua...
In pursuit of faster computation, Efficient Transformers demonstrate an impressive variety of approa...
In this paper, we propose that the dot product pairwise matching attention layer, which is widely us...
The Transformer architecture model, based on self-attention and multi-head attention, has achieved r...
The attention mechanism is the key to many state-of-the-art transformer-based models in Natural Lang...
In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for lea...
Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. The self-at...
Transformers allow attention between all pairs of tokens, but there is reason to believe that most o...
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of ch...
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-at...
The attention mechanism is considered the backbone of the widely-used Transformer architecture. It c...
Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkab...
Large transformer models have achieved state-of-the-art results in numerous natural language process...
Transformer models have achieved state-of-the-art results across a diverse range of domains. However...
To overcome the quadratic cost of self-attention, recent works have proposed various sparse attentio...
Pretrained transformer models have demonstrated remarkable performance across various natural langua...
In pursuit of faster computation, Efficient Transformers demonstrate an impressive variety of approa...
In this paper, we propose that the dot product pairwise matching attention layer, which is widely us...
The Transformer architecture model, based on self-attention and multi-head attention, has achieved r...
The attention mechanism is the key to many state-of-the-art transformer-based models in Natural Lang...
In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for lea...
Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. The self-at...
Transformers allow attention between all pairs of tokens, but there is reason to believe that most o...
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of ch...
