30 citations · 63 across the 10 of their papers we have counts for
10 papers
Continuous Latent Contexts Enable Efficient Online Learning in Transformers
Emile Anand, Abdullah Ateyeh, Xinyuan Cao +1
Large language models (LLMs) exhibit a strong capacity for in-context learning: Given labeled examples, they can generate good predictions without parameter updates. However, many…
The smallest singular value of inhomogenous random rectangular matrices
Max Dabagia, Manuel Fernandez
Let () be a random matrix with with independent entries that have mean 0 variance 1 and bounded moment. We show that the smallest si…
Coin-Flipping In The Brain: Statistical Learning with Neuronal Assemblies
Max Dabagia, Daniel Mitropolsky, Christos H. Papadimitriou +1
How intelligence arises from the brain is a central problem in science. A crucial aspect of intelligence is dealing with uncertainty -- developing good predictions about one's envi…
Computation with Sequences in a Model of the Brain
Max Dabagia, Christos H. Papadimitriou, Santosh S. Vempala
Even as machine learning exceeds human-level performance on many applications, the generality, robustness, and rapidity of the brain's learning capabilities remain unmatched. How c…
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
Ran Liu, Mehdi Azabou, Max Dabagia +2
Complex time-varying systems are often studied by abstracting away from the dynamics of individual components to build a model of the population-level dynamics from the start. Howe…
Comparing high-dimensional neural recordings by aligning their low-dimensional latent representations
Max Dabagia, Konrad P Kording, Eva L Dyer
Many questions in neuroscience involve understanding of the responses of large populations of neurons. However, when dealing with large-scale neural activity, interpretation become…
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Ran Liu, Mehdi Azabou, Max Dabagia +5
Meaningful and simplified representations of neural activity can yield insights into how and what information is being processed within a neural circuit. However, without labels, f…
Assemblies of neurons learn to classify well-separated distributions
Max Dabagia, Christos H. Papadimitriou, Santosh S. Vempala
An assembly is a large population of neurons whose synchronous firing is hypothesized to represent a memory, concept, word, and other cognitive categories. Assemblies are believed…
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
Mehdi Azabou, Mohammad Gheshlaghi Azar, Ran Liu +10
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample. Without sufficient…
Hierarchical Optimal Transport for Multimodal Distribution Alignment
John Lee, Max Dabagia, Eva L. Dyer +1
In many machine learning applications, it is necessary to meaningfully aggregate, through alignment, different but related datasets. Optimal transport (OT)-based approaches pose al…