6 citations · 7 across the 6 of their papers we have counts for
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cs.LG2023
Compositional Sculpting of Iterative Generative Processes
Timur Garipov, Sebastiaan De Peuter, Ge Yang +3
High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition. A key challenge in composi…
cs.LG2021★ 1 cited
Learning Task Informed Abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal +1
Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this…
cs.LG2020★ 6 cited
Plan2Vec: Unsupervised Representation Learning by Latent Plans
Ge Yang, Amy Zhang, Ari S. Morcos +3
In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image d…