activity
20122023
most citedDynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

311 citations · 1.1k across the 79 of their papers we have counts for

collaborators

81 papers

cs.CV2023★ 1 cited

TCOVIS: Temporally Consistent Online Video Instance Segmentation

Junlong Li, Bingyao Yu, Yongming Rao +2

In recent years, significant progress has been made in video instance segmentation (VIS), with many offline and online methods achieving state-of-the-art performance. While offline…

cs.CL2023★ 23 cited

Large Language Models Are Not Robust Multiple Choice Selectors

Chujie Zheng, Hao Zhou, Fandong Meng +2

Multiple choice questions (MCQs) serve as a common yet important task format in the evaluation of large language models (LLMs). This work shows that modern LLMs are vulnerable to o…

physics.app-ph2023★ 3 cited

Initial demonstration of AlGaAs-GaAsP-beta-Ga2O3 n-p-n double heterojunctions

Jie Zhou, Ashok Dheenan, Jiarui Gong +13

Beta phase gallium oxides, an ultrawide-bandgap semiconductor, has great potential for future power and RF electronics applications but faces challenges in bipolar device applicati…

physics.app-ph2023

Monocrystalline Si/-GaO p-n heterojunction diodes fabricated via grafting

Jiarui Gong, Donghyeok Kim, Hokyung Jang +19

The -GaO has exceptional electronic properties with vast potential in power and RF electronics. Despite the excellent demonstrations of high-performance unipolar devices…

math.NA2023

A convergence analysis of a structure-preserving gradient flow method for the all-electron Kohn-Sham model

Yedan Shen, Ting Wang, Jie Zhou +1

In [Dai et al, Multi. Model. Simul., 2020], a structure-preserving gradient flow method was proposed for the ground state calculation in Kohn-Sham density functional theory, based…

cs.CL2023★ 11 cited

Transformer-Patcher: One Mistake worth One Neuron

Zeyu Huang, Yikang Shen, Xiaofeng Zhang +3

Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For…

physics.app-ph2022★ 2 cited

Deep learning for size-agnostic inverse design of random-network 3D printed mechanical metamaterials

Helda Pahlavani, Kostas Tsifoutis-Kazolis, Prerak Mody +3

Practical applications of mechanical metamaterials often involve solving inverse problems where the objective is to find the (multiple) microarchitectures that give rise to a given…

cs.CL2022★ 1 cited

Rephrasing the Reference for Non-Autoregressive Machine Translation

Chenze Shao, Jinchao Zhang, Jie Zhou +1

Non-autoregressive neural machine translation (NAT) models suffer from the multi-modality problem that there may exist multiple possible translations of a source sentence, so the r…

cs.AI2022★ 3 cited

AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning

Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang +2

Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizabili…

cs.CL2022★ 1 cited

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

Xiaozhi Wang, Yulin Chen, Ning Ding +9

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two…

cs.CV2022★ 2 cited

Demystify Transformers & Convolutions in Modern Image Deep Networks

Xiaowei Hu, Min Shi, Weiyun Wang +9

Vision transformers have gained popularity recently, leading to the development of new vision backbones with improved features and consistent performance gains. However, these adva…

cs.CL2022

Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues

Jiao Ou, Jinchao Zhang, Yang Feng +1

The construction of open-domain dialogue systems requires high-quality dialogue datasets. The dialogue data admits a wide variety of responses for a given dialogue history, especia…

cs.CL2022

Exploring Mode Connectivity for Pre-trained Language Models

Yujia Qin, Cheng Qian, Jing Yi +6

Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, st…

cs.CL2022★ 2 cited

Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuning

Jing Yi, Weize Chen, Yujia Qin +6

Delta tuning (DET, also known as parameter-efficient tuning) is deemed as the new paradigm for using pre-trained language models (PLMs). Up to now, various DETs with distinct desig…

cs.CL2022

ROSE: Robust Selective Fine-tuning for Pre-trained Language Models

Lan Jiang, Hao Zhou, Yankai Lin +3

Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks. A large body of defense methods has been proposed. H…

cs.CL2022

From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models

Lei Li, Yankai Lin, Xuancheng Ren +4

Investigating better ways to reuse the released pre-trained language models (PLMs) can significantly reduce the computational cost and the potential environmental side-effects. Thi…

cs.CL2022★ 3 cited

Mixture of Attention Heads: Selecting Attention Heads Per Token

Xiaofeng Zhang, Yikang Shen, Zeyu Huang +3

Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly…

cs.CL2022★ 2 cited

Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models

Zichun Yu, Tianyu Gao, Zhengyan Zhang +4

Prompting, which casts downstream applications as language modeling tasks, has shown to be sample efficient compared to standard fine-tuning with pre-trained models. However, one p…

cs.CL2022

Selecting Stickers in Open-Domain Dialogue through Multitask Learning

Zhexin Zhang, Yeshuang Zhu, Zhengcong Fei +2

With the increasing popularity of online chatting, stickers are becoming important in our online communication. Selecting appropriate stickers in open-domain dialogue requires a co…

cs.CV2022★ 70 cited

Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense

Bowen Peng, Bo Peng, Jie Zhou +2

Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable to adversarial perturbations that are…

cs.CL2022

Manual-Guided Dialogue for Flexible Conversational Agents

Ryuichi Takanobu, Hao Zhou, Yankai Lin +3

How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be two critical issues in building a task-oriented dialogue system. In th…

cs.CR2022★ 16 cited

Fat Pointers for Temporal Memory Safety of C

Jie Zhou, John Criswell, Michael Hicks

Temporal memory safety bugs, especially use-after-free and double free bugs, pose a major security threat to C programs. Real-world exploits utilizing these bugs enable attackers t…

cond-mat.mtrl-sci2022

Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials

H. Pahlavani, M. Amani, M. Cruz Saldívar +3

Emerging multi-material 3D printing techniques have paved the way for the rational design of metamaterials with not only complex geometries but also arbitrary distributions of mult…

cs.CL2022

CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation

Pei Ke, Hao Zhou, Yankai Lin +4

Existing reference-free metrics have obvious limitations for evaluating controlled text generation models. Unsupervised metrics can only provide a task-agnostic evaluation result w…