109 citations · 164 across the 11 of their papers we have counts for
11 papers
XGen-7B Technical Report
Erik Nijkamp, Tian Xie, Hiroaki Hayashi +22
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LL…
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
Vibashan VS, Ning Yu, Chen Xing +5
Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human…
GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation
Can Qin, Ning Yu, Chen Xing +6
Text-to-image (T2I) models based on diffusion processes have achieved remarkable success in controllable image generation using user-provided captions. However, the tight coupling…
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
Le Xue, Mingfei Gao, Chen Xing +6
The recognition capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D coun…
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning
Xiangyu Peng, Chen Xing, Prafulla Kumar Choubey +2
Prompt tuning approaches, which learn task-specific soft prompts for a downstream task conditioning on frozen pre-trained models, have attracted growing interest due to its paramet…
Open Vocabulary Object Detection with Pseudo Bounding-Box Labels
Mingfei Gao, Chen Xing, Juan Carlos Niebles +4
Despite great progress in object detection, most existing methods work only on a limited set of object categories, due to the tremendous human effort needed for bounding-box annota…
Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting
Zahra Fatemi, Chen Xing, Wenhao Liu +1
Existing studies addressing gender bias of pre-trained language models, usually build a small gender-neutral data set and conduct a second phase pre-training on the model with such…
Learning from Mistakes: Using Mis-predictions as Harm Alerts in Language Pre-Training
Chen Xing, Wenhao Liu, Caiming Xiong
Fitting complex patterns in the training data, such as reasoning and commonsense, is a key challenge for language pre-training. According to recent studies and our empirical observ…
Taking Notes on the Fly Helps BERT Pre-training
Qiyu Wu, Chen Xing, Yatao Li +3
How to make unsupervised language pre-training more efficient and less resource-intensive is an important research direction in NLP. In this paper, we focus on improving the effici…
On Layer Normalization in the Transformer Architecture
Ruibin Xiong, Yunchang Yang, Di He +7
The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is sh…
Distance-Based Learning from Errors for Confidence Calibration
Chen Xing, Sercan Arik, Zizhao Zhang +1
Deep neural networks (DNNs) are poorly calibrated when trained in conventional ways. To improve confidence calibration of DNNs, we propose a novel training method, distance-based l…