The rapid growth in miniaturization of low-power embedded devices and advancement in
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …

The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

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 …

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 …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

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 …

Modeling long range dependencies in sequential data is a fundamental step towards
attaining human-level performance in many modalities such as text, vision, audio and video …

Recent studies have shown that deep learning models such as RNNs and Transformers
have brought significant performance gains for long-term forecasting of time series because …