7 citations · 14 across the 8 of their papers we have counts for
9 papers
Rethinking Genomic Modeling Through Optical Character Recognition
Hongxin Xiang, Pengsen Ma, Yunkang Cao +4
Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However, exhaustive sequential reading is stru…
EFPC: Towards Efficient and Flexible Prompt Compression
Yun-Hao Cao, Yangsong Wang, Shuzheng Hao +4
The emergence of large language models (LLMs) like GPT-4 has revolutionized natural language processing (NLP), enabling diverse, complex tasks. However, extensive token counts lead…
On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning
Yun-Hao Cao, Jianxin Wu
Self-supervised learning (SSL) has developed rapidly in recent years. However, most of the mainstream methods are computationally expensive and rely on two (or more) augmentations…
Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
Yun-Hao Cao, Peiqin Sun, Shuchang Zhou
We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different de…
Synergistic Self-supervised and Quantization Learning
Yun-Hao Cao, Peiqin Sun, Yechang Huang +2
With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream t…
Worst Case Matters for Few-Shot Recognition
Minghao Fu, Yun-Hao Cao, Jianxin Wu
Few-shot recognition learns a recognition model with very few (e.g., 1 or 5) images per category, and current few-shot learning methods focus on improving the average accuracy over…
Training Vision Transformers with Only 2040 Images
Yun-Hao Cao, Hao Yu, Jianxin Wu
Vision Transformers (ViTs) is emerging as an alternative to convolutional neural networks (CNNs) for visual recognition. They achieve competitive results with CNNs but the lack of…
Rethinking the Route Towards Weakly Supervised Object Localization
Chen-Lin Zhang, Yun-Hao Cao, Jianxin Wu
Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels. Previous methods often try to utilize feature maps and classification weights to…
Neural Random Subspace
Yun-Hao Cao, Jianxin Wu, Hanchen Wang +1
The random subspace method, known as the pillar of random forests, is good at making precise and robust predictions. However, there is not a straightforward way yet to combine it w…