no code implementations • 21 Apr 2024 • Akhilan Boopathy, Aneesh Muppidi, Peggy Yang, Abhiram Iyer, William Yue, Ila Fiete
State estimation is crucial for the performance and safety of numerous robotic applications.
no code implementations • 5 Feb 2024 • Qiyao Liang, Ziming Liu, Ila Fiete
Corresponding to each of these phases, we identify qualitatively different generation behaviors: 1) multiple bumps are generated, 2) one bump is generated but at inaccurate $x$ and $y$ locations, 3) a bump is generated at the correct $x$ and y location.
1 code implementation • 26 Oct 2023 • Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
Deep reinforcement learning methods exhibit impressive performance on a range of tasks but still struggle on hard exploration tasks in large environments with sparse rewards.
1 code implementation • 11 Jul 2023 • Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
Agents build and use a local map to predict their observations; high surprisal leads to a "fragmentation event" that truncates the local map.
1 code implementation • 2 Jul 2023 • Andrew Kirjner, Jason Yim, Raman Samusevich, Shahar Bracha, Tommi Jaakkola, Regina Barzilay, Ila Fiete
The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine.
1 code implementation • NeurIPS 2023 • Mitchell Ostrow, Adam Eisen, Leo Kozachkov, Ila Fiete
To bridge this gap, we introduce a novel similarity metric that compares two systems at the level of their dynamics, called Dynamical Similarity Analysis (DSA).
1 code implementation • 1 May 2023 • Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh, Ila Fiete
The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models.
no code implementations • 5 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.
no code implementations • 7 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].
1 code implementation • 15 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.
no code implementations • NeurIPS 2020 • Rylan Schaeffer, Mikail Khona, Leenoy Meshulam, Brain Laboratory International, Ila Fiete
Third, the geometry of RNN dynamics reflects an induced coupling between the two separate inference processes necessary to solve the task.
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.
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.
no code implementations • 6 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.
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