5 accepted papers
We present a differentiable optimization framework for multi-agent coordination. An input is decomposed into overlapping local views, each processed by an agent that solves a convex subproblem parametrized by a neural encoder. Agents coordinate through the Alternating Direction Method of Multipliers…
Scaling autoregressive large language models (LLMs) has had an unprecedented impact, but at vast computational costs. In this work, we tackle these costs by leveraging unstructured sparsity within an LLM's feedforward layers, which account for the majority of its parameters and execution FLOPs. To a…
Biological brains demonstrate complex neural activity, where neural dynamics are critical to how brains process information. Most artificial neural networks ignore the complexity of individual neurons. We challenge that paradigm. By incorporating neuron-level processing and synchronization, we reint…
Despite their nearly universal adoption for large language models, the internal workings of transformers are not well understood. We aim to better understand the impact of removing or reorganizing information throughout the layers of a pretrained transformer. Such an understanding could both yield…
The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture bas…