Search Results for author: Ila Fiete

Found 9 papers, 3 papers with code

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 neurobiology: faster learning on many cognitive tasks with fixed sparse RNNs

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

However, fully-connected RNNs contrast structurally with their biological counterparts, which are extremely sparse ($\sim 0. 1$\%).

How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective

1 code implementation15 Jun 2021 Akhilan Boopathy, Ila Fiete

Recent works have examined theoretical and empirical properties of wide neural networks trained in the Neural Tangent Kernel (NTK) regime.

Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes

no code implementations NeurIPS 2019 Rishidev Chaudhuri, Ila Fiete

Neural network models of memory and error correction famously include the Hopfield network, which can directly store---and error-correct through its dynamics---arbitrary N-bit patterns, but only for ~N such patterns.

Kernel RNN Learning (KeRNL)

no code implementations ICLR 2019 Christopher Roth, Ingmar Kanitscheider, Ila Fiete

We describe Kernel RNN Learning (KeRNL), a reduced-rank, temporal eligibility trace-based approximation to backpropagation through time (BPTT) for training recurrent neural networks (RNNs) that gives competitive performance to BPTT on long time-dependence tasks.

Associative content-addressable networks with exponentially many robust stable states

no code implementations6 Apr 2017 Rishidev Chaudhuri, Ila Fiete

The brain must robustly store a large number of memories, corresponding to the many events encountered over a lifetime.

Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems

1 code implementation NeurIPS 2017 Ingmar Kanitscheider, Ila Fiete

Self-localization during navigation with noisy sensors in an ambiguous world is computationally challenging, yet animals and humans excel at it.

Neurons and Cognition

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