Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …

Recent studies have discovered that Chain-of-Thought prompting (CoT) can dramatically
improve the performance of Large Language Models (LLMs), particularly when dealing with …

Neural sequence models based on the transformer architecture have demonstrated
remarkable\emph {in-context learning}(ICL) abilities, where they can perform new tasks …

Attention-based neural networks such as transformers have demonstrated a remarkable
ability to exhibit in-context learning (ICL): Given a short prompt sequence of tokens from an …

In-context learning is the ability of a model to condition on a prompt sequence consisting of
in-context examples (input-output pairs corresponding to some task) along with a new query …

Attention layers, as commonly used in transformers, form the backbone of modern deep
learning, yet there is no mathematical description of their benefits and deficiencies as …

Attention-based architectures have become ubiquitous in machine learning. Yet, our
understanding of the reasons for their effectiveness remains limited. This work proposes a …

Transformers-based models, such as BERT, have been one of the most successful deep
learning models for NLP. Unfortunately, one of their core limitations is the quadratic …

Instructing the model to generate a sequence of intermediate steps, aka, a chain of thought
(CoT), is a highly effective method to improve the accuracy of large language models (LLMs) …