36 citations · 100 across the 9 of their papers we have counts for
9 papers
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Qiying Yu, Zheng Zhang, Ruofei Zhu +32
Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning. However, key technical details…
Emu3: Next-Token Prediction is All You Need
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo +22
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diff…
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Quan Sun, Jinsheng Wang, Qiying Yu +4
Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models. We present EVA-CLIP-18B, the largest and most powerful open-s…
Generative Multimodal Models are In-Context Learners
Quan Sun, Yufeng Cui, Xiaosong Zhang +8
The human ability to easily solve multimodal tasks in context (i.e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggl…
CapsFusion: Rethinking Image-Text Data at Scale
Qiying Yu, Quan Sun, Xiaosong Zhang +5
Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute funda…
Multimodal Molecular Pretraining via Modality Blending
Qiying Yu, Yudi Zhang, Yuyan Ni +4
Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery. Current studies consider leveraging bot…
Emu: Generative Pretraining in Multimodality
Quan Sun, Qiying Yu, Yufeng Cui +7
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-mo…
Multimodal Federated Learning via Contrastive Representation Ensemble
Qiying Yu, Yang Liu, Yimu Wang +2
With the increasing amount of multimedia data on modern mobile systems and IoT infrastructures, harnessing these rich multimodal data without breaching user privacy becomes a criti…
Adversarial Contrastive Learning via Asymmetric InfoNCE
Qiying Yu, Jieming Lou, Xianyuan Zhan +4
Contrastive learning (CL) has recently been applied to adversarial learning tasks. Such practice considers adversarial samples as additional positive views of an instance, and by m…