Search Results for author: Sarthak Chandra

Found 5 papers, 1 papers with code

Bridging Associative Memory and Probabilistic Modeling

no code implementations15 Feb 2024 Rylan Schaeffer, Nika Zahedi, Mikail Khona, Dhruv Pai, Sang Truong, Yilun Du, Mitchell Ostrow, Sarthak Chandra, Andres Carranza, Ila Rani Fiete, Andrey Gromov, Sanmi Koyejo

Based on the observation that associative memory's energy functions can be seen as probabilistic modeling's negative log likelihoods, we build a bridge between the two that enables useful flow of ideas in both directions.

In-Context Learning

See and Copy: Generation of complex compositional movements from modular and geometric RNN representations

no code implementations5 Oct 2022 Sunny Duan, Mikail Khona, Adrian Bertagnoli, Sarthak Chandra, Ila Fiete

A hallmark of biological intelligence and control is combinatorial generalization: animals are able to learn various things, then piece them together in new combinations to produce appropriate outputs for new tasks.

Winning the lottery with neural connectivity constraints: faster learning across cognitive tasks with spatially constrained sparse RNNs

no code implementations7 Jul 2022 Mikail Khona, Sarthak Chandra, Joy J. Ma, Ila Fiete

We study LM-RNNs in a multitask learning setting relevant to cognitive systems neuroscience with a commonly used set of tasks, 20-Cog-tasks [Yang et al., 2019].

Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold

1 code implementation1 Feb 2022 Sugandha Sharma, Sarthak Chandra, Ila R. Fiete

We propose a novel CAM architecture, Memory Scaffold with Heteroassociation (MESH), that factorizes the problems of internal attractor dynamics and association with external content to generate a CAM continuum without a memory cliff: Small numbers of patterns are stored with complete information recovery matching standard CAMs, while inserting more patterns still results in partial recall of every pattern, with a graceful trade-off between pattern number and pattern richness.

Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model

no code implementations9 Mar 2018 Jaideep Pathak, Alexander Wikner, Rebeckah Fussell, Sarthak Chandra, Brian Hunt, Michelle Girvan, Edward Ott

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system.

BIG-bench Machine Learning Test +3

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