John Lafferty

About

John Lafferty is the John C. Malone Professor of Statistics and Data Science at Yale University. He is also the director of the Center for Neurocomputation and Machine Intelligence at the Wu Tsai Institute at Yale. His research involves developing computational models to understand perception and memory, as demonstrated by his recent work on a study exploring how the brain prioritizes what to remember. In collaboration with Yale scientists, Lafferty contributed to creating a model that addresses the processes of visual signal compression and reconstruction, which helps explain why certain images are more memorable based on the difficulty of their reconstruction. His work aims to shed light on perception and memory formation, with potential applications in developing more efficient memory systems for artificial intelligence.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Data Mining
• Neuroscience
• Biology
• Algorithm
• Physics
• Computational science
• Theoretical computer science

Selected publications

• TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts

  Proceedings of the AAAI Conference on Artificial Intelligence · 2019-07-17 · 32 citations

  articleOpen accessSenior author

  Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between mathematical equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates mathematical equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the equation is generated by an RNN that depends on the latent topic activatio…

  PublisherOA PDFDOI

• Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons

  eLife · 2022 · 31 citations

  , we build anatomically-constrained shallow neural network models and train them to identify visual signals that correspond to impending collisions. Surprisingly, the optimization arrives at two distinct, opposing solutions, only one of which matches the actual dendritic weighting of LPLC2 neurons. Both solutions can solve the inference problem with high accuracy when the population size is large enough. The LPLC2-like solutions reproduces experimentally observed LPLC2 neuron responses for many…

  PublisherDOI

• The relational bottleneck as an inductive bias for efficient abstraction

  Trends in Cognitive Sciences · 2024-05-09 · 17 citations

  review
  PublisherDOI

• Images with harder-to-reconstruct visual representations leave stronger memory traces

  Nature Human Behaviour · 2024-05-13 · 13 citations

  article
  PublisherDOI

• Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers

  arXiv (Cornell University) · 2023-04-01 · 6 citations

  preprintOpen accessSenior author

  An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the Abstractor. At the core of the Abstractor is a variant of attention called relational cross-attention. The approach is motivated by an architectural inductive bias for relational learning that disentangles relational information from object-level features. This enables explicit relational reasoning, supporting abstraction and generalization from limited data. The Abstractor is fi…

  PublisherOA PDFDOI

Recent grants

• MSPA-MCS: Nonparametric Learning in High Dimensions

  NSF · $500k · 2006–2010

• Constrained Statistical Estimation and Inference: Theory, Algorithms and Applications

  NSF · $320k · 2015–2017

• Constrained Statistical Estimation and Inference: Theory, Algorithms and Applications

  NSF · $145k · 2017–2018

Frequent coauthors

• Larry Wasserman

  Carnegie Mellon University

  71 shared

• Han Liu

  46 shared

• Fang Han

  University of Washington

  40 shared

• Ming Yuan

  Peking University Shenzhen Hospital

  40 shared

• Mark Crowther

  St. Joseph’s Healthcare Hamilton

  32 shared

• David H.K. Chui

  Boston Medical Center

  27 shared

• John S. Waye

  Hamilton Regional Laboratory Medicine Program

  21 shared

• Andrew McFarlane

  21 shared

Similar researchers at Yale University

• Luciano Floridih-120
• Steven Tangh-49
• Jessica Thompsonh-48