42 citations · 57 across the 25 of their papers we have counts for
7 papers · 1 filter
Open Ad-hoc Categorization with Contextualized Feature Learning
Zilin Wang, Sangwoo Mo, Stella X. Yu +2
Adaptive categorization of visual scenes is essential for AI agents to handle changing tasks. Unlike fixed common categories for plants or animals, ad-hoc categories are created dy…
Rethinking the Role of Dynamic Sparse Training for Scalable Deep Reinforcement Learning
Guozheng Ma, Lu Li, Zilin Wang +4
Scaling neural networks has driven breakthrough advances in machine learning, yet this paradigm fails in deep reinforcement learning (DRL), where larger models often degrade perfor…
HyperVLA: Efficient Inference in Vision-Language-Action Models via Hypernetworks
Zheng Xiong, Kang Li, Zilin Wang +3
Built upon language and vision foundation models with strong generalization ability and trained on large-scale robotic data, Vision-Language-Action (VLA) models have recently emerg…
Free-Grained Hierarchical Visual Recognition
Seulki Park, Zilin Wang, Stella X. Yu
Hierarchical image recognition seeks to predict class labels along a semantic taxonomy, from broad categories to specific ones, typically under the tidy assumption that every train…
How Should We Meta-Learn Reinforcement Learning Algorithms?
Alexander David Goldie, Zilin Wang, Jaron Cohen +2
The process of meta-learning algorithms from data, instead of relying on manual design, is growing in popularity as a paradigm for improving the performance of machine learning sys…
Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
Guozheng Ma, Lu Li, Zilin Wang +3
Effectively scaling up deep reinforcement learning models has proven notoriously difficult due to network pathologies during training, motivating various targeted interventions suc…
Point Cloud Neural Operator for Parametric PDEs on Complex and Variable Geometries
Chenyu Zeng, Yanshu Zhang, Jiayi Zhou +5
Surrogate models are critical for accelerating computationally expensive simulations in science and engineering, particularly for solving parametric partial differential equations…