94 citations · 293 across the 12 of their papers we have counts for
13 papers
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Xuanlin Li, Brandon Trabucco, Dong Huk Park +4
The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generate…
Keyframe-Focused Visual Imitation Learning
Chuan Wen, Jierui Lin, Jianing Qian +2
Imitation learning trains control policies by mimicking pre-recorded expert demonstrations. In partially observable settings, imitation policies must rely on observation histories,…
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
Xiangyu Yue, Zangwei Zheng, Shanghang Zhang +4
Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-labeled source domain to an unlabeled target domain. In some applications, however, it is expensive ev…
Fighting Copycat Agents in Behavioral Cloning from Observation Histories
Chuan Wen, Jierui Lin, Trevor Darrell +2
Imitation learning trains policies to map from input observations to the actions that an expert would choose. In this setting, distribution shift frequently exacerbates the effect…
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation
Sicheng Zhao, Yezhen Wang, Bo Li +5
Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data…
Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation
Huazhe Xu, Boyuan Chen, Yang Gao +1
It is a long-standing challenge to enable an intelligent agent to learn in one environment and generalize to an unseen environment without further data collection and finetuning. I…
Risk Averse Robust Adversarial Reinforcement Learning
Xinlei Pan, Daniel Seita, Yang Gao +1
Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the tra…
Reinforcement Learning from Imperfect Demonstrations
Yang Gao, Huazhe Xu, Ji Lin +3
Robust real-world learning should benefit from both demonstrations and interactions with the environment. Current approaches to learning from demonstration and reward perform super…
Generalized orderless pooling performs implicit salient matching
Marcel Simon, Yang Gao, Trevor Darrell +2
Most recent CNN architectures use average pooling as a final feature encoding step. In the field of fine-grained recognition, however, recent global representations like bilinear p…
End-to-end Learning of Driving Models from Large-scale Video Datasets
Huazhe Xu, Yang Gao, Fisher Yu +1
Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learn…
Fine-grained pose prediction, normalization, and recognition
Ning Zhang, Evan Shelhamer, Yang Gao +1
Pose variation and subtle differences in appearance are key challenges to fine-grained classification. While deep networks have markedly improved general recognition, many approach…
Deep Learning for Tactile Understanding From Visual and Haptic Data
Yang Gao, Lisa Anne Hendricks, Katherine J. Kuchenbecker +1
Robots which interact with the physical world will benefit from a fine-grained tactile understanding of objects and surfaces. Additionally, for certain tasks, robots may need to kn…
Compact Bilinear Pooling
Yang Gao, Oscar Beijbom, Ning Zhang +1
Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. Howe…