Search Results for author: Yang Bai

Found 35 papers, 12 papers with code

Automatic Detecting for Health-related Twitter Data with BioBERT

no code implementations SMM4H (COLING) 2020 Yang Bai, Xiaobing Zhou

Social media used for health applications usually contains a large amount of data posted by users, which brings various challenges to NLP, such as spoken language, spelling errors, novel/creative phrases, etc.

HUB@DravidianLangTech-EACL2021: Identify and Classify Offensive Text in Multilingual Code Mixing in Social Media

no code implementations EACL (DravidianLangTech) 2021 Bo Huang, Yang Bai

Among the known tasks related to offensive speech detection, this is the first task to detect offensive comments posted in social media comments in the Dravidian language.

Classification Language Identification

150 Years of Return Predictability Around the World: A Holistic View

no code implementations31 Aug 2022 Yang Bai

The predictive regressions based on the VAR analysis by Cochrane (2008, 2011) suggest that 14 (5) countries have predictable payout growth in the equity (housing) markets, ex., the dividend price predicts the dividend growth in the US.

MOVE: Effective and Harmless Ownership Verification via Embedded External Features

1 code implementation4 Aug 2022 Yiming Li, Linghui Zhu, Xiaojun Jia, Yang Bai, Yong Jiang, Shu-Tao Xia, Xiaochun Cao

In general, we conduct the ownership verification by verifying whether a suspicious model contains the knowledge of defender-specified external features.

Style Transfer

Action Quality Assessment with Temporal Parsing Transformer

1 code implementation19 Jul 2022 Yang Bai, Desen Zhou, Songyang Zhang, Jian Wang, Errui Ding, Yu Guan, Yang Long, Jingdong Wang

Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences.

Action Quality Assessment Action Understanding +1

Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal

1 code implementation17 Jul 2022 Xinwei Liu, Jian Liu, Yang Bai, Jindong Gu, Tao Chen, Xiaojun Jia, Xiaochun Cao

Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning for good.

Residual Local Feature Network for Efficient Super-Resolution

1 code implementation16 May 2022 Fangyuan Kong, Mingxi Li, Songwei Liu, Ding Liu, Jingwen He, Yang Bai, Fangmin Chen, Lean Fu

Moreover, we revisit the popular contrastive loss and observe that the selection of intermediate features of its feature extractor has great influence on the performance.

Image Super-Resolution Single Image Super Resolution +1

Adaptive Frequency Learning in Two-branch Face Forgery Detection

no code implementations27 Mar 2022 Neng Wang, Yang Bai, Kun Yu, Yong Jiang, Shu-Tao Xia, Yan Wang

Face forgery has attracted increasing attention in recent applications of computer vision.

PCL: Proxy-Based Contrastive Learning for Domain Generalization

1 code implementation CVPR 2022 Xufeng Yao, Yang Bai, Xinyun Zhang, Yuechen Zhang, Qi Sun, Ran Chen, Ruiyu Li, Bei Yu

Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.

Contrastive Learning Domain Generalization

Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement

no code implementations11 Dec 2021 Yang Bai, Lixing Chen, Shaolei Ren, Jie Xu

The core of our method is a DNN selection module that learns user QoE patterns on-the-fly and identifies the best-fit DNN for on-thing inference with the learned knowledge.

Transfer Learning

Clustering Effect of (Linearized) Adversarial Robust Models

1 code implementation25 Nov 2021 Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang

Adversarial robustness has received increasing attention along with the study of adversarial examples.

Adversarial Robustness Domain Adaptation

More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering

no code implementations25 Sep 2021 Yang Bai, Daisy Zhe Wang

Textual Question Answering (QA) aims to provide precise answers to user's questions in natural language using unstructured data.

Machine Reading Comprehension Question Answering

Discriminative Latent Semantic Graph for Video Captioning

1 code implementation8 Aug 2021 Yang Bai, Junyan Wang, Yang Long, Bingzhang Hu, Yang song, Maurice Pagnucco, Yu Guan

Video captioning aims to automatically generate natural language sentences that can describe the visual contents of a given video.

Video Captioning Video Summarization

hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense

no code implementations SEMEVAL 2021 Bo Huang, Yang Bai

The final scores of the prediction results of the two subtask test sets submitted by our team are task1a 0. 921 (F1), task1a 0. 9364 (Accuracy), task1b 0. 6288 (RMSE), task1c 0. 5333 (F1), task1c 0. 0. 5591 (Accuracy), and task2 0. 5027 (RMSE) respectively.

Humor Detection regression +2

hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity

no code implementations SEMEVAL 2021 Bo Huang, Yang Bai, Xiaobing Zhou

Use Inception block as a shared layer to learn sentence and word frequency information We described the implementation of our best system and discussed our methods and experiments in the task.

Lexical Complexity Prediction

hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word Frequency

no code implementations SEMEVAL 2021 Bo Huang, Yang Bai, Xiaobing Zhou

What we need to do is to use our method to determine as accurately as possible the meaning of the words in a sentence pair are the same or different.

hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification

no code implementations SEMEVAL 2021 Bo Huang, Yang Bai, Xiaobing Zhou

This article introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 5: Toxic Spans Detection.

Toxic Spans Detection

Improving Adversarial Robustness via Channel-wise Activation Suppressing

1 code implementation ICLR 2021 Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang

The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs).

Adversarial Robustness

Bridge the Gap: High-level Semantic Planning for Image Captioning

no code implementations COLING 2020 Chenxi Yuan, Yang Bai, Chun Yuan

To bridge the gaps we propose a high-level semantic planning (HSP) mechanism that incorporates both a semantic reconstruction and an explicit order planning.

Image Captioning

BYteam at SemEval-2020 Task 5: Detecting Counterfactual Statements with BERT and Ensembles

no code implementations SEMEVAL 2020 Yang Bai, Xiaobing Zhou

We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals).


SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval

no code implementations2 Oct 2020 Yang Bai, Xiaoguang Li, Gang Wang, Chaoliang Zhang, Lifeng Shang, Jun Xu, Zhaowei Wang, Fangshan Wang, Qun Liu

Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching.

Language Modelling Retrieval +1

Improving Query Efficiency of Black-box Adversarial Attack

1 code implementation ECCV 2020 Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, Weiwei Guo

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting).

Adversarial Attack

Query Twice: Dual Mixture Attention Meta Learning for Video Summarization

no code implementations19 Aug 2020 Junyan Wang, Yang Bai, Yang Long, Bingzhang Hu, Zhenhua Chai, Yu Guan, Xiaolin Wei

Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax function.

Meta-Learning Video Summarization

Fatigue Assessment using ECG and Actigraphy Sensors

1 code implementation6 Aug 2020 Yang Bai, Yu Guan, Wan-Fai Ng

For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment.

Decision Making Feature Engineering

Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2

no code implementations17 Mar 2020 Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov, Amine Ahriche, Gaël Alguero, Benjamin C. Allanach, Jack Y. Araz, Alexandre Arbey, Chiara Arina, Peter Athron, Emanuele Bagnaschi, Yang Bai, Michael J. Baker, Csaba Balazs, Daniele Barducci, Philip Bechtle, Aoife Bharucha, Andy Buckley, Jonathan Butterworth, Haiying Cai, Claudio Campagnari, Cari Cesarotti, Marcin Chrzaszcz, Andrea Coccaro, Eric Conte, Jonathan M. Cornell, Louie Dartmoor Corpe, Matthias Danninger, Luc Darmé, Aldo Deandrea, Nishita Desai, Barry Dillon, Caterina Doglioni, Juhi Dutta, John R. Ellis, Sebastian Ellis, Farida Fassi, Matthew Feickert, Nicolas Fernandez, Sylvain Fichet, Jernej F. Kamenik, Thomas Flacke, Benjamin Fuks, Achim Geiser, Marie-Hélène Genest, Akshay Ghalsasi, Tomas Gonzalo, Mark Goodsell, Stefania Gori, Philippe Gras, Admir Greljo, Diego Guadagnoli, Sven Heinemeyer, Lukas A. Heinrich, Jan Heisig, Deog Ki Hong, Tetiana Hryn'ova, Katri Huitu, Philip Ilten, Ahmed Ismail, Adil Jueid, Felix Kahlhoefer, Jan Kalinowski, Deepak Kar, Yevgeny Kats, Charanjit K. Khosa, Valeri Khoze, Tobias Klingl, Pyungwon Ko, Kyoungchul Kong, Wojciech Kotlarski, Michael Krämer, Sabine Kraml, Suchita Kulkarni, Anders Kvellestad, Clemens Lange, Kati Lassila-Perini, Seung J. Lee, Andre Lessa, Zhen Liu, Lara Lloret Iglesias, Jeanette M. Lorenz, Danika MacDonell, Farvah Mahmoudi, Judita Mamuzic, Andrea C. Marini, Pete Markowitz, Pablo Martinez Ruiz del Arbol, David Miller, Vasiliki Mitsou, Stefano Moretti, Marco Nardecchia, Siavash Neshatpour, Dao Thi Nhung, Per Osland, Patrick H. Owen, Orlando Panella, Alexander Pankov, Myeonghun Park, Werner Porod, Darren Price, Harrison Prosper, Are Raklev, Jürgen Reuter, Humberto Reyes-González, Thomas Rizzo, Tania Robens, Juan Rojo, Janusz A. Rosiek, Oleg Ruchayskiy, Veronica Sanz, Kai Schmidt-Hoberg, Pat Scott, Sezen Sekmen, Dipan Sengupta, Elizabeth Sexton-Kennedy, Hua-Sheng Shao, Seodong Shin, Luca Silvestrini, Ritesh Singh, Sukanya Sinha, Jory Sonneveld, Yotam Soreq, Giordon H. Stark, Tim Stefaniak, Jesse Thaler, Riccardo Torre, Emilio Torrente-Lujan, Gokhan Unel, Natascia Vignaroli, Wolfgang Waltenberger, Nicholas Wardle, Graeme Watt, Georg Weiglein, Martin J. White, Sophie L. Williamson, Jonas Wittbrodt, Lei Wu, Stefan Wunsch, Tevong You, Yang Zhang, José Zurita

We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Hilbert-Based Generative Defense for Adversarial Examples

no code implementations ICCV 2019 Yang Bai, Yan Feng, Yisen Wang, Tao Dai, Shu-Tao Xia, Yong Jiang

Adversarial perturbations of clean images are usually imperceptible for human eyes, but can confidently fool deep neural networks (DNNs) to make incorrect predictions.

Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum

1 code implementation3 Jul 2015 Daniel Abercrombie, Nural Akchurin, Ece Akilli, Juan Alcaraz Maestre, Brandon Allen, Barbara Alvarez Gonzalez, Jeremy Andrea, Alexandre Arbey, Georges Azuelos, Patrizia Azzi, Mihailo Backović, Yang Bai, Swagato Banerjee, James Beacham, Alexander Belyaev, Antonio Boveia, Amelia Jean Brennan, Oliver Buchmueller, Matthew R. Buckley, Giorgio Busoni, Michael Buttignol, Giacomo Cacciapaglia, Regina Caputo, Linda Carpenter, Nuno Filipe Castro, Guillelmo Gomez Ceballos, Yangyang Cheng, John Paul Chou, Arely Cortes Gonzalez, Chris Cowden, Francesco D'Eramo, Annapaola De Cosa, Michele De Gruttola, Albert De Roeck, Andrea De Simone, Aldo Deandrea, Zeynep Demiragli, Anthony DiFranzo, Caterina Doglioni, Tristan du Pree, Robin Erbacher, Johannes Erdmann, Cora Fischer, Henning Flaecher, Patrick J. Fox, Benjamin Fuks, Marie-Helene Genest, Bhawna Gomber, Andreas Goudelis, Johanna Gramling, John Gunion, Kristian Hahn, Ulrich Haisch, Roni Harnik, Philip C. Harris, Kerstin Hoepfner, Siew Yan Hoh, Dylan George Hsu, Shih-Chieh Hsu, Yutaro Iiyama, Valerio Ippolito, Thomas Jacques, Xiangyang Ju, Felix Kahlhoefer, Alexis Kalogeropoulos, Laser Seymour Kaplan, Lashkar Kashif, Valentin V. Khoze, Raman Khurana, Khristian Kotov, Dmytro Kovalskyi, Suchita Kulkarni, Shuichi Kunori, Viktor Kutzner, Hyun Min Lee, Sung-Won Lee, Seng Pei Liew, Tongyan Lin, Steven Lowette, Romain Madar, Sarah Malik, Fabio Maltoni, Mario Martinez Perez, Olivier Mattelaer, Kentarou Mawatari, Christopher McCabe, Théo Megy, Enrico Morgante, Stephen Mrenna, Siddharth M. Narayanan, Andy Nelson, Sérgio F. Novaes, Klaas Ole Padeken, Priscilla Pani, Michele Papucci, Manfred Paulini, Christoph Paus, Jacopo Pazzini, Björn Penning, Michael E. Peskin, Deborah Pinna, Massimiliano Procura, Shamona F. Qazi, Davide Racco, Emanuele Re, Antonio Riotto, Thomas G. Rizzo, Rainer Roehrig, David Salek, Arturo Sanchez Pineda, Subir Sarkar, Alexander Schmidt, Steven Randolph Schramm, William Shepherd, Gurpreet Singh, Livia Soffi, Norraphat Srimanobhas, Kevin Sung, Tim M. P. Tait, Timothee Theveneaux-Pelzer, Marc Thomas, Mia Tosi, Daniele Trocino, Sonaina Undleeb, Alessandro Vichi, Fuquan Wang, Lian-Tao Wang, Ren-Jie Wang, Nikola Whallon, Steven Worm, Mengqing Wu, Sau Lan Wu, Hongtao Yang, Yong Yang, Shin-Shan Yu, Bryan Zaldivar, Marco Zanetti, Zhiqing Zhang, Alberto Zucchetta

This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches.

High Energy Physics - Experiment High Energy Physics - Phenomenology

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