1 citations · 2 across the 7 of their papers we have counts for
11 papers
Effectiveness of Curriculum Learning Depends on Reward Sparsity and Competing Optima
John J. Vastola, Ann Huang, Satpreet H. Singh +2
While animals and people tend to learn a task more quickly and reliably when they first train on simpler versions of it, curriculum learning's effectiveness in artificial settings…
Solvable models of learning to pursue a moving target
John J. Vastola, Kanaka Rajan
Agents tasked with intercepting a moving target must learn how to reach it, ideally as efficiently as possible. As observers of animal pursuit behavior have noticed, different stra…
Traversing the solution space of neural networks with Hessian Null Space Continuation
Ann Huang, Mitchell Ostrow, Zhouyang Lu +3
On a single task, deep networks can learn many solutions, depending on their optimizer, training data, architecture, and hyperparameters. Many of these solutions are mode-connected…
Implications of hierarchical Markov models of behavior: on irreversibility, predictability, and dimensionality
John J. Vastola, Kanaka Rajan
The maturation of quantitative tools for studying the high-level structure of animal behavior, and especially tools which represent spontaneous behavior as a sequence of stereotype…
A dimensional R2 regression metric
Jaesung Yoo, Stefan Lemke, Jian Zhong Guo +2
R2 score is the standard metric for evaluating regression tasks, offering a normalized magnitude-agnostic measure of accuracy that captures variance. However, R2 has three key limi…
A Variational Manifold Embedding Framework for Nonlinear Dimensionality Reduction
John J. Vastola, Samuel J. Gershman, Kanaka Rajan
Dimensionality reduction algorithms like principal component analysis (PCA) are workhorses of machine learning and neuroscience, but each has well-known limitations. Variants of PC…
Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives
Satpreet H. Singh, Sonja Johnson-Yu, Zhouyang Lu +7
How complex collective behavior emerges from individual interactions is a fundamental scientific question, but experimental cost and difficulty of simultaneous multi-brain recordin…
InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics
Ann Huang, Mitchell Ostrow, Satpreet H. Singh +3
In control problems and basic scientific modeling, it is important to compare observations with dynamical simulations. For example, comparing two neural systems can shed light on t…
Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules
John J. Vastola, Samuel J. Gershman, Kanaka Rajan
Learning rules -- prescriptions for updating model parameters to improve performance -- are typically assumed rather than derived. Why do some learning rules work better than other…
Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents
Raaghav Malik, Satpreet H. Singh, Sonja Johnson-Yu +4
Larval zebrafish hunting provides a tractable setting to study how ecological and energetic constraints shape adaptive behavior in both biological brains and artificial agents. Her…
Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks
Ann Huang, Satpreet H. Singh, Flavio Martinelli +1
Task-trained recurrent neural networks (RNNs) are widely used in neuroscience and machine learning to model dynamical computations. To gain mechanistic insight into how neural syst…