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Jinghuan Shang
Jinghuan Shang
Stony Brook University, Ph.D. student
Verified email at cs.stonybrook.edu - Homepage
Title
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Cited by
Year
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning
J Shang, K Kahatapitiya, X Li, MS Ryoo
European Conference on Computer Vision, 2022
262022
Does self-supervised learning really improve reinforcement learning from pixels?
X Li, J Shang, S Das, M Ryoo
Advances in Neural Information Processing Systems 35, 30865-30881, 2022
242022
Self-supervised disentangled representation learning for third-person imitation learning
J Shang, MS Ryoo
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021
202021
Learning viewpoint-agnostic visual representations by recovering tokens in 3d space
J Shang, S Das, M Ryoo
Advances in Neural Information Processing Systems 35, 31031-31044, 2022
112022
StARformer: Transformer with State-Action-Reward Representations for Robot Learning
J Shang, X Li, K Kahatapitiya, YC Lee, MS Ryoo
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
72022
Crossway diffusion: Improving diffusion-based visuomotor policy via self-supervised learning
X Li, V Belagali, J Shang, MS Ryoo
arXiv preprint arXiv:2307.01849, 2023
62023
TRITON: Neural Neural Textures for Better Sim2Real
RD Burgert, J Shang, X Li, MS Ryoo
6th Annual Conference on Robot Learning, 2022
6*2022
Learning for Expressive Task-Related Sentence Representations
X Bai, J Shang, Y Sun, N Balasubramanian
arXiv, 2022
12022
Active Vision Reinforcement Learning under Limited Visual Observability
J Shang, MS Ryoo
Advances in Neural Information Processing Systems 36, 2024
2024
Continual Learning with Global Prototypes: Beyond the Scope of Task Supervision
X Bai, J Shang, Y Sun, N Balasubramanian
2023
Enhancing Continual Learning with Global Prototypes: Counteracting Negative Representation Drift
X Bai, J Shang, Y Sun, N Balasubramanian
arXiv preprint arXiv:2205.12186, 2022
2022
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning–Supplementary Material
J Shang, K Kahatapitiya, X Li, MS Ryoo
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