261 citations · 471 across the 20 of their papers we have counts for
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cs.LG2023★ 4 cited
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Sheng Liu, Haotian Ye, Lei Xing +1
Large language models (LLMs) demonstrate emergent in-context learning capabilities, where they adapt to new tasks based on example demonstrations. However, in-context learning has…
cs.LG2023★ 1 cited
Channel-Wise Contrastive Learning for Learning with Noisy Labels
Hui Kang, Sheng Liu, Huaxi Huang +1
In real-world datasets, noisy labels are pervasive. The challenge of learning with noisy labels (LNL) is to train a classifier that discerns the actual classes from given instances…
cs.LG2023★ 2 cited
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels
Hui Kang, Sheng Liu, Huaxi Huang +4
In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean da…