14 citations · 42 across the 18 of their papers we have counts for
4 papers · 1 filter
Diffusion Models For Multi-Modal Generative Modeling
Changyou Chen, Han Ding, Bunyamin Sisman +5
Diffusion-based generative modeling has been achieving state-of-the-art results on various generation tasks. Most diffusion models, however, are limited to a single-generation mode…
VidLA: Video-Language Alignment at Scale
Mamshad Nayeem Rizve, Fan Fei, Jayakrishnan Unnikrishnan +5
In this paper, we propose VidLA, an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First, they do…
Robust Multi-Task Learning with Excess Risks
Yifei He, Shiji Zhou, Guojun Zhang +5
Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing met…
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Yue Xing, Xiaofeng Lin, Qifan Song +3
Pre-training is known to generate universal representations for downstream tasks in large-scale deep learning such as large language models. Existing literature, e.g., \cite{kim202…