Search Results for author:
Found 14 papers, 4 papers with code
LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptation
Existing self-training methods use a model trained on labeled source data to generate pseudo labels for target data and refine the predictions via fine-tuning the network on the pseudo labels.
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples
Deploying deep visual models can lead to performance drops due to the discrepancies between source and target distributions.
Unsupervised Domain Adaptation Based on the Predictive Uncertainty of Models
Unsupervised domain adaptation (UDA) aims to improve the prediction performance in the target domain under distribution shifts from the source domain.
Meta Reinforcement Learning for Optimal Design of Legged Robots
We use meta reinforcement learning to train a locomotion policy that can quickly adapt to different designs.
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation
Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation.
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning
Imitation learning approaches such as adversarial motion priors aim to reduce this problem by encouraging a pre-defined motion style.
A Phaseless Auxiliary-Field Quantum Monte Carlo Perspective on the Uniform Electron Gas at Finite Temperatures: Issues, Observations, and Benchmark Study
We investigate the viability of the phaseless finite temperature auxiliary field quantum Monte Carlo (ph-FT-AFQMC) method for ab initio systems using the uniform electron gas as a model.
Chemical Physics Strongly Correlated Electrons
Even more efficient quantum computations of chemistry through tensor hypercontraction
We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e. g., molecular) orbitals.
Quantum Physics Chemical Physics
Learning Quadrupedal Locomotion over Challenging Terrain
The trained controller has taken two generations of quadrupedal ANYmal robots to a variety of natural environments that are beyond the reach of prior published work in legged locomotion.
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning
This paper addresses the problem of legged locomotion in non-flat terrain.
ProbAct: A Probabilistic Activation Function for Deep Neural Networks
We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.
Learning agile and dynamic motor skills for legged robots
In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes.
Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning
We experimentally validate our approach on the quadrupedal robot ANYmal, which is a dog-sized quadrupedal system with 12 degrees of freedom.
