4 citations · 5 across the 5 of their papers we have counts for
5 papers
Towards Exact Computation of Inductive Bias
Akhilan Boopathy, William Yue, Jaedong Hwang +2
Much research in machine learning involves finding appropriate inductive biases (e.g. convolutional neural networks, momentum-based optimizers, transformers) to promote generalizat…
Resampling-free Particle Filters in High-dimensions
Akhilan Boopathy, Aneesh Muppidi, Peggy Yang +3
State estimation is crucial for the performance and safety of numerous robotic applications. Among the suite of estimation techniques, particle filters have been identified as a po…
Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity
Jaedong Hwang, Zhang-Wei Hong, Eric Chen +3
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. To ad…
Model-agnostic Measure of Generalization Difficulty
Akhilan Boopathy, Kevin Liu, Jaedong Hwang +3
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.…
Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle
Rylan Schaeffer, Mikail Khona, Zachary Robertson +5
Double descent is a surprising phenomenon in machine learning, in which as the number of model parameters grows relative to the number of data, test error drops as models grow ever…