233 citations · 519 across the 15 of their papers we have counts for
17 papers
GenScale: A Benchmark for Relative Object Scale in Image Generation and Editing
Lingxiao Li, Max Whitton, Ledell Wu +1
Modern image generation and editing systems can produce photorealistic, prompt-aligned images, but still often render familiar objects at implausible relative sizes. To measure thi…
AltDiffusion: A Multilingual Text-to-Image Diffusion Model
Fulong Ye, Guang Liu, Xinya Wu +1
Large Text-to-Image(T2I) diffusion models have shown a remarkable capability to produce photorealistic and diverse images based on text inputs. However, existing works only support…
UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
Yazheng Yang, Yuqi Wang, Guang Liu +2
Recent advancements in NLP have witnessed the groundbreaking impact of pretrained models, yielding impressive outcomes across various tasks. This study seeks to extend the power of…
EVA-CLIP: Improved Training Techniques for CLIP at Scale
Quan Sun, Yuxin Fang, Ledell Wu +2
Contrastive language-image pre-training, CLIP for short, has gained increasing attention for its potential in various scenarios. In this paper, we propose EVA-CLIP, a series of mod…
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…
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
Yujia Zhou, Jing Yao, Zhicheng Dou +3
Document retrieval has been extensively studied within the index-retrieve framework for decades, which has withstood the test of time. Unfortunately, such a pipelined framework lim…
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…
Multi-Dimensional Gender Bias Classification
Emily Dinan, Angela Fan, Ledell Wu +3
Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we…
Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min +5
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de…
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
Ledell Wu, Fabio Petroni, Martin Josifoski +2
This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off. We…
PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen +4
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial appl…
StarSpace: Embed All The Things!
Ledell Wu, Adam Fisch, Sumit Chopra +3
We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as informat…
Inapproximability of Treewidth, One-Shot Pebbling, and Related Layout Problems
Per Austrin, Toniann Pitassi, Yu Wu
We study the approximability of a number of graph problems: treewidth and pathwidth of graphs, one-shot black (and black-white) pebbling costs of directed acyclic graphs, and a var…