Recent experimental and theoretical studies have highlighted the importance of cell-to-cell differences in the dynamics and functions of neural networks, suc

A neural network in computer vision processes images by learning hierarchical patterns through layers of mathematical op

Technical deep-dive into transformer neural network architecture, self-attention mechanisms, scaling laws, and the evolution from GPT to frontier AI systems powering cognitive computing

ACL Anthology About Announcements Communication channels Related work Copyright Credits Volunteer Development Feedback Using Citing papers Links in the Anthology Data access All FAQs Details Anthology identifiers Names ORCID iDs DOIs Verified authors Contributions Submissions Corrections Author pages Attachments GitHub Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP Yonatan Belinkov , Aaron Mueller , Najoung Kim , Hosein Mohebbi , Hanjie Chen , Dana Arad , Gab

"All media are extensions of some human faculty — psychic or physical." ~Marshall McLuhanFor most of 2024, and especially since publishing my last essay, I’ve been spending quite a bit of time trying to make sense of what we now call “generative AI” and its implications for me personally and for society more broadly. Like many, I’ve been captivated by AI as a creative tool, and have found myself implementing many of these new products into my workflows, particularly for creative writing and m...

Computational and Theoretical Neural Information Processing Laboratory

How to do graph, node, and edge predictions using your own Pandas/NetworkX datasets

Lux is a new Julia deep learning framework that decouples models and parameterization using deeply nested named tuples. Functional Layer API – Pure Functions and Deterministic Function Calls. No more implicit parameter…

Home > Blog > Study Examines the Neural Bases of Feeling Understood vs Misunderstood

Understanding the memory capacity of neural networks remains a challenging problem in implementing artificial intelligence systems. In this paper, we address the notion of capacity with respect to Hopfield networks and propose a dynamic approach to monitoring a network's capacity. We define our understanding of capacity as the maximum number of stored patterns which can be retrieved when probed by the stored patterns. Prior work in this area has presented static expressions dependent on neuron count $N$, fo