92 citations · 156 across the 7 of their papers we have counts for
11 papers
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
Yuxin Fang, Wen Wang, Binhui Xie +6
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to re…
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
Zhongzhi Chen, Guang Liu, Bo-Wen Zhang +3
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model. Starting from the pre-trained multimod…
PTab: Using the Pre-trained Language Model for Modeling Tabular Data
Guang Liu, Jie Yang, Ledell Wu
Tabular data is the foundation of the information age and has been extensively studied. Recent studies show that neural-based models are effective in learning contextual representa…
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index
Yujia Zhou, Jing Yao, Zhicheng Dou +2
Web search provides a promising way for people to obtain information and has been extensively studied. With the surgence of deep learning and large-scale pre-training techniques, v…
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
Zhiyi Ma, Kawin Ethayarajh, Tristan Thrush +6
We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform. Our platform e…
Multilingual Autoregressive Entity Linking
Nicola De Cao, Ledell Wu, Kashyap Popat +7
We present mGENRE, a sequence-to-sequence system for the Multilingual Entity Linking (MEL) problem -- the task of resolving language-specific mentions to a multilingual Knowledge B…