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DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction Qilong Ma
Neural Information Processing Systems
Figure 1: Comparison between Lagrangian (left) and Eulerian (right) perspectives.
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GLinSA T: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent
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Constrained decision-making problems are pervasive across various disciplines.
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Diffuser Lite: Towards Real-time Diffusion Planning Zibin Dong 1 Jianye Hao
Neural Information Processing Systems
The capability of generating high-quality long-horizon trajectories makes it a promising research direction.
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Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Neural Information Processing Systems
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor.
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