1 citations · 2 across the 7 of their papers we have counts for
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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…
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…
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…
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…