The fusion of large language models and robotic systems has introduced a transformative
paradigm in human–robot interaction, offering unparalleled capabilities in natural language …

Transformers have achieved great success in many artificial intelligence fields, such as
natural language processing, computer vision, and audio processing. Therefore, it is natural …

Inference from large autoregressive models like Transformers is slow-decoding K tokens
takes K serial runs of the model. In this work we introduce speculative decoding-an …

We study the problem of efficient generative inference for Transformer models, in one of its
most challenging settings: large deep models, with tight latency targets and long sequence …

Transformers are slow and memory-hungry on long sequences, since the time and memory
complexity of self-attention are quadratic in sequence length. Approximate attention …

Convolutional architectures have proven extremely successful for vision tasks. Their hard
inductive biases enable sample-efficient learning, but come at the cost of a potentially lower …

Biological systems understand the world by simultaneously processing high-dimensional
inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The …

In deep learning, models typically reuse the same parameters for all inputs. Mixture of
Experts (MoE) models defy this and instead select different parameters for each incoming …

While today's video recognition systems parse snapshots or short clips accurately, they
cannot connect the dots and reason across a longer range of time yet. Most existing video …

A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …