8 citations · 19 across the 21 of their papers we have counts for
5 papers · 1 filter
Accelerating scientific discovery with the common task framework
J. Nathan Kutz, Peter Battaglia, Michael Brenner +12
Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical,…
Generating Auxiliary Tasks with Reinforcement Learning
Judah Goldfeder, Matthew So, Hod Lipson
Auxiliary Learning (AL) is a form of multi-task learning in which a model trains on auxiliary tasks to boost performance on a primary objective. While AL has improved generalizatio…
Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks
Judah Goldfeder, Quinten Roets, Gabe Guo +2
Inferring the exact parameters of a neural network with only query access is an NP-Hard problem, with few practical existing algorithms. Solutions would have major implications for…
Balanced and Deterministic Weight-sharing Helps Network Performance
Oscar Chang, Hod Lipson
Weight-sharing plays a significant role in the success of many deep neural networks, by increasing memory efficiency and incorporating useful inductive priors about the problem int…
Accelerating Meta-Learning by Sharing Gradients
Oscar Chang, Hod Lipson
The success of gradient-based meta-learning is primarily attributed to its ability to leverage related tasks to learn task-invariant information. However, the absence of interactio…