output
20162024
most citedPhysics-informed data based neural networks for two-dimensional turbulence

63 citations

Showing 2021Show all

10 papers · 1 filter

cond-mat.soft20213 cited

Contact Force Mediated Rapid Deposition of Colloidal Microspheres Flowing Over Microstructured Barriers

P. Prakash, A. Z. Abdulla, M. Varma

Deposition of particles while flowing past constrictions is a ubiquitous phenomenon observed in diverse systems. Some common examples are jamming of salt crystals near the orifice…

cs.LG20212 cited

Smooth Imitation Learning via Smooth Costs and Smooth Policies

Sapana Chaudhary, Balaraman Ravindran

Imitation learning (IL) is a popular approach in the continuous control setting as among other reasons it circumvents the problems of reward mis-specification and exploration in re…

cs.LG20216 cited

Stabilizing Equilibrium Models by Jacobian Regularization

Shaojie Bai, Vladlen Koltun, J. Zico Kolter

Deep equilibrium networks (DEQs) are a new class of models that eschews traditional depth in favor of finding the fixed point of a single nonlinear layer. These models have been sh…

cs.LG20217 cited

DORO: Distributional and Outlier Robust Optimization

Runtian Zhai, Chen Dan, J. Zico Kolter +1

Many machine learning tasks involve subpopulation shift where the testing data distribution is a subpopulation of the training distribution. For such settings, a line of recent wor…

cs.LG20216 cited

TempoRL: Learning When to Act

André Biedenkapp, Raghu Rajan, Frank Hutter +1

Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment. However, behaviours are usually learned in a purely reactive fashion, whe…

cs.SE202116 cited

Model-Based Reliability and Safety: Reducing the Complexity of Safety Analyses Using Component Fault Trees

Kai Hoefig, Andreas Joanni, Marc Zeller +5

The importance of mission or safety critical software systems in many application domains of embedded systems is continuously growing, and so is the effort and complexity for relia…