Recent advances in deep learning have relied heavily on the use of large Transformers due
to their ability to learn at scale. However, the core building block of Transformers, the …

Genomic (DNA) sequences encode an enormous amount of information for gene regulation
and protein synthesis. Similar to natural language models, researchers have proposed …

We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …

State space models (SSM) have recently been shown to be very effective as a deep learning
layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers …

A central goal of sequence modeling is designing a single principled model that can
address sequence data across a range of modalities and tasks, particularly on long-range …

Visual data such as images and videos are typically modeled as discretizations of inherently
continuous, multidimensional signals. Existing continuous-signal models attempt to exploit …

Models using structured state space sequence (S4) layers have achieved state-of-the-art
performance on long-range sequence modeling tasks. An S4 layer combines linear state …

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations
(NDEs) are popular families of deep learning models for time-series data, each with unique …

The design choices in the Transformer attention mechanism, including weak inductive bias
and quadratic computational complexity, have limited its application for modeling long …

Abstract Machine learning models are increasingly being scaled in both sequence length
and model dimension to reach longer contexts and better performance. However, existing …