no code implementations • EMNLP 2020 • Wenqing Chen, Jidong Tian, Liqiang Xiao, Hao He, Yaohui Jin
In the field of causal inference, GS in our model is essentially a counterfactual reasoning process, trying to estimate the causal effect between tasks and utilize it to improve MTL.
no code implementations • EMNLP 2020 • Liqiang Xiao, Lu Wang, Hao He, Yaohui Jin
Previous work is mostly based on statistical methods that estimate word-level salience, which does not consider semantics and larger context when quantifying importance.
no code implementations • EMNLP 2021 • Jidong Tian, Yitian Li, Wenqing Chen, Liqiang Xiao, Hao He, Yaohui Jin
Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area.
no code implementations • COLING 2022 • Yitian Li, Jidong Tian, Wenqing Chen, Caoyun Fan, Hao He, Yaohui Jin
In this paper, we propose a systematic method to diagnose the correlations between an NLU dataset and a specific skill, and then take a fundamental reasoning skill, logical reasoning, as an example for analysis.
no code implementations • 21 Oct 2024 • Hao He, Yixun Liang, Luozhou Wang, Yuanhao Cai, Xinli Xu, Hao-Xiang Guo, Xiang Wen, Yingcong Chen
Recent large reconstruction models have made notable progress in generating high-quality 3D objects from single images.
no code implementations • 10 Oct 2024 • Zhengyang Liang, Hao He, Ceyuan Yang, Bo Dai
Diffusion transformers (DiT) have already achieved appealing synthesis and scaling properties in content recreation, e. g., image and video generation.
no code implementations • 27 May 2024 • Zhengfei Kuang, Shengqu Cai, Hao He, Yinghao Xu, Hongsheng Li, Leonidas Guibas, Gordon Wetzstein
Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images.
no code implementations • 20 May 2024 • Hao He, Chao Li, Wolfgang Ganglberger, Kaileigh Gallagher, Rumen Hristov, Michail Ouroutzoglou, Haoqi Sun, Jimeng Sun, Brandon Westover, Dina Katabi
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful.
no code implementations • 9 May 2024 • Yitian Li, Jidong Tian, Hao He, Yaohui Jin
Combining different forms of prompts with pre-trained large language models has yielded remarkable results on reasoning tasks (e. g. Chain-of-Thought prompting).
no code implementations • 8 May 2024 • Yitian Li, Jidong Tian, Hao He, Yaohui Jin
To solve the problem, we propose a simple but effective method, Negation Augmenting and Negation Debiasing (NAND), which introduces negative propositions to prompt-based methods without updating parameters.
1 code implementation • 2 Apr 2024 • Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, Ceyuan Yang
Controllability plays a crucial role in video generation since it allows users to create desired content.
no code implementations • 24 Feb 2024 • Haoran Liao, Jidong Tian, Shaohua Hu, Hao He, Yaohui Jin
Large language models (LLMs) still grapple with complex tasks like mathematical reasoning.
no code implementations • 4 Jan 2024 • Yiheng Liu, Hao He, Tianle Han, Xu Zhang, Mengyuan Liu, Jiaming Tian, Yutong Zhang, Jiaqi Wang, Xiaohui Gao, Tianyang Zhong, Yi Pan, Shaochen Xu, Zihao Wu, Zhengliang Liu, Xin Zhang, Shu Zhang, Xintao Hu, Tuo Zhang, Ning Qiang, Tianming Liu, Bao Ge
Low-cost training and deployment of LLMs represent the future development trend.
1 code implementation • 14 Dec 2023 • Haoran Liao, Qinyi Du, Shaohua Hu, Hao He, Yanyan Xu, Jidong Tian, Yaohui Jin
Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales.
no code implementations • 12 Dec 2023 • Caoyun Fan, Jidong Tian, Yitian Li, Hao He, Yaohui Jin
In-Context Learning (ICL) is an important paradigm for adapting Large Language Models (LLMs) to downstream tasks through a few demonstrations.
no code implementations • 9 Dec 2023 • Caoyun Fan, Jindou Chen, Yaohui Jin, Hao He
With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs as substitutes for humans in game experiments, enabling social science research.
no code implementations • 10 Nov 2023 • Zhengliang Liu, Hanqi Jiang, Tianyang Zhong, Zihao Wu, Chong Ma, Yiwei Li, Xiaowei Yu, Yutong Zhang, Yi Pan, Peng Shu, Yanjun Lyu, Lu Zhang, Junjie Yao, Peixin Dong, Chao Cao, Zhenxiang Xiao, Jiaqi Wang, Huan Zhao, Shaochen Xu, Yaonai Wei, Jingyuan Chen, Haixing Dai, Peilong Wang, Hao He, Zewei Wang, Xinyu Wang, Xu Zhang, Lin Zhao, Yiheng Liu, Kai Zhang, Liheng Yan, Lichao Sun, Jun Liu, Ning Qiang, Bao Ge, Xiaoyan Cai, Shijie Zhao, Xintao Hu, Yixuan Yuan, Gang Li, Shu Zhang, Xin Zhang, Xi Jiang, Tuo Zhang, Dinggang Shen, Quanzheng Li, Wei Liu, Xiang Li, Dajiang Zhu, Tianming Liu
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
no code implementations • 18 Oct 2023 • Caoyun Fan, Jidong Tian, Yitian Li, Wenqing Chen, Hao He, Yaohui Jin
From the perspective of CoT, CoTT's two-step framework enables MLMs to implement task decomposition; CoTT's prompt tuning allows intermediate steps to be used in natural language form.
no code implementations • 11 Oct 2023 • Caoyun Fan, Wenqing Chen, Jidong Tian, Yitian Li, Hao He, Yaohui Jin
In this study, we attribute the bias to the model's misuse of label dependency, i. e., the model tends to utilize the correlation shortcut in label dependency rather than fusing text information and label dependency for prediction.
no code implementations • 10 Oct 2023 • Caoyun Fan, Wenqing Chen, Jidong Tian, Yitian Li, Hao He, Yaohui Jin
Counterfactually-Augmented Data (CAD) -- minimal editing of sentences to flip the corresponding labels -- has the potential to improve the Out-Of-Distribution (OOD) generalization capability of language models, as CAD induces language models to exploit domain-independent causal features and exclude spurious correlations.
1 code implementation • 7 Aug 2023 • Allegra Laro, Yanqing Chen, Hao He, Babak Aghazadeh
Personalized recommendations form an important part of today's internet ecosystem, helping artists and creators to reach interested users, and helping users to discover new and engaging content.
2 code implementations • 13 Jun 2023 • Tianyi Liu, Zihao Xu, Hao He, Guang-Yuan Hao, Guang-He Lee, Hao Wang
Domain adaptation aims to mitigate distribution shifts among different domains.
no code implementations • 4 Apr 2023 • Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Lin Zhao, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, Bao Ge
This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.
no code implementations • 19 Mar 2023 • Yixun Liang, Hao He, Shishi Xiao, Hao Lu, Yingcong Chen
In this paper, we propose a principled approach that supports learning from heterogeneous datasets with different label sets.
2 code implementations • CVPR 2023 • Jierun Chen, Shiu-hong Kao, Hao He, Weipeng Zhuo, Song Wen, Chul-Ho Lee, S. -H. Gary Chan
To achieve faster networks, we revisit popular operators and demonstrate that such low FLOPS is mainly due to frequent memory access of the operators, especially the depthwise convolution.
no code implementations • 18 Feb 2023 • Caoyun Fan, Wenqing Chen, Jidong Tian, Yitian Li, Hao He, Yaohui Jin
Counterfactually-Augmented Data (CAD) has the potential to improve language models' Out-Of-Distribution (OOD) generalization capability, as CAD induces language models to exploit causal features and exclude spurious correlations.
no code implementations • 18 Feb 2023 • Caoyun Fan, Wenqing Chen, Jidong Tian, Yitian Li, Hao He, Yaohui Jin
A series of studies point out that too much gradient noise would lead to performance degradation in STL, however, in the MTL scenario, Inter-Task Gradient Noise (ITGN) is an additional source of gradient noise for each task, which can also affect the optimization process.
4 code implementations • 6 Feb 2023 • Zihao Xu, Guang-Yuan Hao, Hao He, Hao Wang
To address this challenge, we first provide a formal definition of domain index from the probabilistic perspective, and then propose an adversarial variational Bayesian framework that infers domain indices from multi-domain data, thereby providing additional insight on domain relations and improving domain adaptation performance.
no code implementations • 13 Jan 2023 • Chunhui Du, Hao He, Yaohui Jin
Federated medical relation extraction enables multiple clients to train a deep network collaboratively without sharing their raw medical data.
no code implementations • 6 Dec 2022 • Hao He, Yuan Yuan, Ying-Cong Chen, Peng Cao, Dina Katabi
With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead.
no code implementations • 7 Oct 2022 • Hao Wang, WanYu Lin, Hao He, Di Wang, Chengzhi Mao, Muhan Zhang
Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe.
no code implementations • 8 Sep 2022 • Aoxiao Zhong, Hao He, Zhaolin Ren, Na Li, Quanzheng Li
To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients.
no code implementations • 6 Mar 2022 • Hao He, Yuzhe Yang, Hao Wang
In this paper, we propose a new assumption, Factorizable Joint Shift (FJS), to handle the co-existence of sampling bias in covariates and labels.
1 code implementation • 22 Feb 2022 • Hao He, Kaiwen Zha, Dina Katabi
We propose Contrastive Poisoning (CP), the first effective such attack on CL.
1 code implementation • ICLR 2022 • Zihao Xu, Hao He, Guang-He Lee, Yuyang Wang, Hao Wang
In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e. g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure.
no code implementations • 8 Feb 2022 • Hao He, Fan Zhang, Steve Pieper, Nikos Makris, Yogesh Rathi, William Wells III, Lauren J. O'Donnell
The human brain's white matter (WM) structure is of immense interest to the scientific community.
1 code implementation • ICLR 2022 • Yilun Xu, Hao He, Tianxiao Shen, Tommi Jaakkola
We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer.
no code implementations • 17 Jan 2022 • Yimin Huang, Xinyu Feng, Wanwan Wang, Hao He, Yukun Wang, Ming Yao
In most VFL frameworks, to protect the security and privacy of the participants' local data, a third party is needed to generate homomorphic encryption key pairs and perform decryption operations.
no code implementations • EMNLP 2021 • Liqiang Xiao, Jun Ma2, Xin Luna Dong, Pascual Martinez-Gomez, Nasser Zalmout, Wei Chen, Tong Zhao, Hao He, Yaohui Jin
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
no code implementations • ACL 2021 • Wenqing Chen, Jidong Tian, Yitian Li, Hao He, Yaohui Jin
The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text.
1 code implementation • 28 Jul 2021 • Xiangtai Li, Hao He, Yibo Yang, Henghui Ding, Kuiyuan Yang, Guangliang Cheng, Yunhai Tong, DaCheng Tao
To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames.
no code implementations • 15 Jul 2021 • Hao He, Tian Zhou, Lihua Ren, Niklas Karlsson, Aaron Flores
For Verizon MediaDemand Side Platform(DSP), forecasting of ad campaign performance not only feeds key information to the optimization server to allow the system to operate on a high-performance mode, but also produces actionable insights to the advertisers.
no code implementations • 12 Jul 2021 • Tian Zhou, Hao He, Shengjun Pan, Niklas Karlsson, Bharatbhushan Shetty, Brendan Kitts, Djordje Gligorijevic, San Gultekin, Tingyu Mao, Junwei Pan, Jianlong Zhang, Aaron Flores
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.
1 code implementation • 25 May 2021 • Hao He, Xiangtai Li, Yibo Yang, Guangliang Cheng, Yunhai Tong, Lubin Weng, Zhouchen Lin, Shiming Xiang
This module is used to squeeze the object boundary from both inner and outer directions, which contributes to precise mask representation.
no code implementations • 18 May 2021 • Wenqing Chen, Jidong Tian, Caoyun Fan, Hao He, Yaohui Jin
The intermediate task would help the model better understand the visual features and thus alleviate the content inconsistency problem.
1 code implementation • ICCV 2021 • Hao He, Xiangtai Li, Guangliang Cheng, Jianping Shi, Yunhai Tong, Gaofeng Meng, Véronique Prinet, Lubin Weng
We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours.
Ranked #20 on Thermal Image Segmentation on RGB-T-Glass-Segmentation
1 code implementation • eLife 2021 • Brian Q Geuther, Asaf Peer, Hao He, Gautam Sabnis, Vivek M Philip, Vivek Kumar
Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions.
1 code implementation • CVPR 2021 • Xiangtai Li, Hao He, Xia Li, Duo Li, Guangliang Cheng, Jianping Shi, Lubin Weng, Yunhai Tong, Zhouchen Lin
Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
no code implementations • 1 Jan 2021 • Hao He, Ying-Cong Chen, Yuan Yuan, Dina Katabi
Further, since breathing can be monitored without body contact by analyzing the radio signal in the environment, we show that oxygen too can be monitored without any wearable devices.
no code implementations • 17 Dec 2020 • Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Rogerio Feris, Piotr Indyk, Dina Katabi
However, contrastive learning is susceptible to feature suppression, i. e., it may discard important information relevant to the task of interest, and learn irrelevant features.
no code implementations • COLING 2020 • Wenqing Chen, Jidong Tian, Liqiang Xiao, Hao He, Yaohui Jin
In this paper, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic latent variable be semantic-free.
no code implementations • 19 Sep 2020 • Shengjun Pan, Brendan Kitts, Tian Zhou, Hao He, Bharatbhushan Shetty, Aaron Flores, Djordje Gligorijevic, Junwei Pan, Tingyu Mao, San Gultekin, Jianlong Zhang
We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.
1 code implementation • ICML 2020 • Hao Wang, Hao He, Dina Katabi
Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
Ranked #1 on Domain Adaptation on Rotating MNIST
1 code implementation • ICML 2020 • Hao Wang, Hao He, Dina Katabi
Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
no code implementations • 6 May 2020 • Hao He, Hengchen Dai, Naiyan Wang
In contrast to existing methods which heavily rely on recurrent neural network for temporal context and hand-crafted structure for spatial context, our method could automatically partition the spatio-temporal space to adapt the data.
no code implementations • ICLR 2020 • Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi, Antonio Torralba
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis.
no code implementations • 28 Sep 2019 • Lu Mi, Hao Wang, Yonglong Tian, Hao He, Nir Shavit
Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.
1 code implementation • 2 Jul 2019 • Honglun Zhang, Wenqing Chen, Hao He, Yaohui Jin
Facial makeup transfer is a widely-used technology that aims to transfer the makeup style from a reference face image to a non-makeup face.
no code implementations • 25 Jun 2019 • Yongfei Li, Dongfang Yang, Shicheng Wang, Hao He
We test all the candidate matching tuples under a hypothesise-and-test framework to search for the best match.
1 code implementation • ICLR 2019 • Hao He, Hao Wang, Guang-He Lee, Yonglong Tian
Probabilistic modelling is a principled framework to perform model aggregation, which has been a primary mechanism to combat mode collapse in the context of Generative Adversarial Networks (GAN).
Ranked #26 on Image Generation on STL-10
1 code implementation • 19 Mar 2019 • Yuhui Wang, Hao He, Chao Wen, Xiaoyang Tan
Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging tasks.
no code implementations • 15 Feb 2019 • Yuhui Wang, Hao He, Xiaoyang Tan
In real-world scenarios, the observation data for reinforcement learning with continuous control is commonly noisy and part of it may be dynamically missing over time, which violates the assumption of many current methods developed for this.
no code implementations • 6 Feb 2019 • Hao Wang, Chengzhi Mao, Hao He, Ming-Min Zhao, Tommi S. Jaakkola, Dina Katabi
We consider the problem of inferring the values of an arbitrary set of variables (e. g., risk of diseases) given other observed variables (e. g., symptoms and diagnosed diseases) and high-dimensional signals (e. g., MRI images or EEG).
2 code implementations • NeurIPS 2019 • Yuhui Wang, Hao He, Xiaoyang Tan, Yaozhong Gan
We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well.
no code implementations • Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018 • Yonglong Tian, Guang-He Lee, Hao He, Chen-Yu Hsu, Dina Katabi
Falls are the top reason for fatal and non-fatal injuries among seniors.
Ranked #2 on RF-based Pose Estimation on RF-MMD
1 code implementation • 27 Sep 2017 • Yuanming Hu, Hao He, Chenxi Xu, Baoyuan Wang, Stephen Lin
Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well.
no code implementations • NeurIPS 2017 • Hao He, Bo Xin, David Wipf
The iterations of many first-order algorithms, when applied to minimizing common regularized regression functions, often resemble neural network layers with pre-specified weights.