17 citations · 26 across the 13 of their papers we have counts for
34 papers
The Role of Symmetry in Optimizing Overparameterized Networks
Kusha Sareen, Mohammad Pedramfar, Sékou-Oumar Kaba +2
Overparameterization is central to the success of deep learning, yet the mechanisms by which it improves optimization remain incompletely understood. We analyze weight-space symmet…
The Expressive Limits of Diagonal SSMs for State-Tracking
Mehran Shakerinava, Behnoush Khavari, Siamak Ravanbakhsh +1
State-Space Models (SSMs) have recently been shown to achieve strong empirical performance on a variety of long-range sequence modeling tasks while remaining efficient and highly-p…
Inverting Data Transformations via Diffusion Sampling
Jinwoo Kim, Sékou-Oumar Kaba, Jiyun Park +2
We study the problem of transformation inversion on general Lie groups: a datum is transformed by an unknown group element, and the goal is to recover an inverse transformation tha…
Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatism
Tianwei Ni, Esther Derman, Vineet Jain +3
Popular offline reinforcement learning (RL) methods rely on explicit conservatism, penalizing out-of-dataset actions or restricting rollout horizons. We question the universality o…
Energy Loss Functions for Physical Systems
Sékou-Oumar Kaba, Kusha Sareen, Daniel Levy +1
Effectively leveraging prior knowledge of a system's physics is crucial for applications of machine learning to scientific domains. Previous approaches mostly focused on incorporat…
Scaling Laws and Symmetry, Evidence from Neural Force Fields
Khang Ngo, Siamak Ravanbakhsh
We present an empirical study in the geometric task of learning interatomic potentials, which shows equivariance matters even more at larger scales; we show a clear power-law scali…