Search Results for author: Jing Gao

Found 39 papers, 8 papers with code

Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness

no code implementations EMNLP 2021 Hengtong Zhang, Tianhang Zheng, Yaliang Li, Jing Gao, Lu Su, Bo Li

To address this problem, we propose a training framework with certified robustness to eliminate the causes that trigger the generation of profanity.

Dialogue Generation Style Transfer

SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer

no code implementations12 Jun 2022 Junde Wu, Huihui Fang, Fangxin Shang, Dalu Yang, Zhaowei Wang, Jing Gao, Yehui Yang, Yanwu Xu

To model the segmentation-diagnosis interaction, SeA-block first embeds the diagnosis feature based on the segmentation information via the encoder, and then transfers the embedding back to the diagnosis feature space by a decoder.

AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models

no code implementations24 May 2022 Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao

(ii) We propose a simple merging mechanism to average the weights of multiple adapter components to collapse to a single adapter in each Transformer layer, thereby, keeping the overall parameters also the same but with significant performance improvement.

Natural Language Understanding Sparse Learning

Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification

1 code implementation22 Apr 2022 Jing Gao, Tilo Burghardt, Neill W. Campbell

In particular, for the task of automatic identification of individual Holstein-Friesians in real-world farm CCTV, we show that self-supervision, metric learning, cluster analysis, and active learning can complement each other to significantly reduce the annotation requirements usually needed to train cattle identification frameworks.

Active Learning Metric Learning

Deep Learning for Spatiotemporal Modeling of Urbanization

no code implementations17 Dec 2021 Tang Li, Jing Gao, Xi Peng

Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization.

LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners

1 code implementation12 Oct 2021 Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao

The first is the use of self-training to leverage large amounts of unlabeled data for prompt-based FN in few-shot settings.

Few-Shot Learning

On the regularization landscape for the linear recommendation models

no code implementations29 Sep 2021 Dong Li, Zhenming Liu, Ruoming Jin, Zhi Liu, Jing Gao, Bin Ren

Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders several standard recommendation benchmarks.

Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning

no code implementations29 Sep 2021 Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, Jing Gao

To tackle these challenges, we propose a novel casual graph based fair prediction framework, which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph.

Fairness Graph structure learning

Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework

no code implementations Findings (EMNLP) 2021 Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao

In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings.

Benchmark Few-Shot Learning +3

Multimodal Emergent Fake News Detection via Meta Neural Process Networks

no code implementations22 Jun 2021 Yaqing Wang, Fenglong Ma, Haoyu Wang, Kishlay Jha, Jing Gao

The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods.

Fake News Detection Hard Attention +1

On Sampling Top-K Recommendation Evaluation

no code implementations20 Jun 2021 Dong Li, Ruoming Jin, Jing Gao, Zhi Liu

Recently, Rendle has warned that the use of sampling-based top-$k$ metrics might not suffice.

Towards a Better Understanding of Linear Models for Recommendation

no code implementations27 May 2021 Ruoming Jin, Dong Li, Jing Gao, Zhi Liu, Li Chen, Yang Zhou

Through the derivation and analysis of the closed-form solutions for two basic regression and matrix factorization approaches, we found these two approaches are indeed inherently related but also diverge in how they "scale-down" the singular values of the original user-item interaction matrix.

Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset

2 code implementations5 May 2021 Jing Gao, Tilo Burghardt, William Andrew, Andrew W. Dowsey, Neill W. Campbell

Motivated by the labelling burden involved in constructing visual cattle identification systems, we propose exploiting the temporal coat pattern appearance across videos as a self-supervision signal for animal identity learning.

Contrastive Learning

Fairness-aware Outlier Ensemble

no code implementations17 Mar 2021 Haoyu Liu, Fenglong Ma, Shibo He, Jiming Chen, Jing Gao

Meanwhile, we propose a post-processing framework to tune the original ensemble results through a stacking process so that we can achieve a trade off between fairness and detection performance.

Fairness Fraud Detection +1

On Estimating Recommendation Evaluation Metrics under Sampling

no code implementations2 Mar 2021 Ruoming Jin, Dong Li, Benjamin Mudrak, Jing Gao, Zhi Liu

The proposed approaches either are rather uninformative (linking sampling to metric evaluation) or can only work on simple metrics, such as Recall/Precision (Krichene and Rendle 2020; Li et al. 2020).

Adaptive Self-training for Neural Sequence Labeling with Few Labels

no code implementations1 Jan 2021 Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah

Neural sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and semantic parsing.

Meta-Learning named-entity-recognition +3

Adaptive Self-training for Few-shot Neural Sequence Labeling

no code implementations7 Oct 2020 Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah

While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.

Meta-Learning named-entity-recognition +3

Efficient Knowledge Graph Validation via Cross-Graph Representation Learning

no code implementations16 Aug 2020 Yaqing Wang, Fenglong Ma, Jing Gao

To tackle this challenging task, we propose a cross-graph representation learning framework, i. e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently.

Graph Representation Learning Knowledge Graphs

Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning

2 code implementations16 Jun 2020 William Andrew, Jing Gao, Siobhan Mullan, Neill Campbell, Andrew W Dowsey, Tilo Burghardt

Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems.

Metric Learning

Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data

no code implementations15 Jun 2020 Yaqing Wang, Yifan Ethan Xu, Xi-An Li, Xin Luna Dong, Jing Gao

(1) We formalize the problem of validating the textual attribute values of products from a variety of categories as a natural language inference task in the few-shot learning setting, and propose a meta-learning latent variable model to jointly process the signals obtained from product profiles and textual attribute values.

Few-Shot Learning Natural Language Inference

Decomposed Adversarial Learned Inference

no code implementations21 Apr 2020 Alexander Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao

Effective inference for a generative adversarial model remains an important and challenging problem.

Practical Data Poisoning Attack against Next-Item Recommendation

no code implementations7 Apr 2020 Hengtong Zhang, Yaliang Li, Bolin Ding, Jing Gao

In real-world recommendation systems, the cost of retraining recommendation models is high, and the interaction frequency between users and a recommendation system is restricted. Given these real-world restrictions, we propose to let the agent interact with a recommender simulator instead of the target recommendation system and leverage the transferability of the generated adversarial samples to poison the target system.

Data Poisoning Recommendation Systems

A Survey on Causal Inference

1 code implementation5 Feb 2020 Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, Aidong Zhang

Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.

Causal Inference

Weak Supervision for Fake News Detection via Reinforcement Learning

1 code implementation28 Dec 2019 Yaqing Wang, Weifeng Yang, Fenglong Ma, Jin Xu, Bin Zhong, Qiang Deng, Jing Gao

In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i. e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection.

Fake News Detection reinforcement-learning

Atmospheric turbulence removal using convolutional neural network

no code implementations22 Dec 2019 Jing Gao, N. Anantrasirichai, David Bull

This paper describes a novel deep learning-based method for mitigating the effects of atmospheric distortion.

Data Poisoning Attack against Knowledge Graph Embedding

no code implementations26 Apr 2019 Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, Kui Ren

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently.

Data Poisoning Knowledge Graph Completion +2

Representation Learning for Treatment Effect Estimation from Observational Data

1 code implementation NeurIPS 2018 Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang

Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.

Causal Inference Representation Learning +1

Finding Similar Medical Questions from Question Answering Websites

no code implementations14 Oct 2018 Yaliang Li, Liuyi Yao, Nan Du, Jing Gao, Qi Li, Chuishi Meng, Chenwei Zhang, Wei Fan

Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users.

Question Answering

Towards Differentially Private Truth Discovery for Crowd Sensing Systems

no code implementations10 Oct 2018 Yaliang Li, Houping Xiao, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao, Kui Ren, Bolin Ding

To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic.

Privacy Preserving

AIM: Adversarial Inference by Matching Priors and Conditionals

no code implementations27 Sep 2018 Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao

We propose a novel approach, Adversarial Inference by Matching priors and conditionals (AIM), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model.

Long-Term Memory Networks for Question Answering

no code implementations6 Jul 2017 Fenglong Ma, Radha Chitta, Saurabh Kataria, Jing Zhou, Palghat Ramesh, Tong Sun, Jing Gao

Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task.

Natural Language Processing Question Answering

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

no code implementations19 Jun 2017 Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao

Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results.

Multi-source Hierarchical Prediction Consolidation

no code implementations11 Aug 2016 Chenwei Zhang, Sihong Xie, Yaliang Li, Jing Gao, Wei Fan, Philip S. Yu

We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources.

Multilabel Consensus Classification

no code implementations16 Oct 2013 Sihong Xie, Xiangnan Kong, Jing Gao, Wei Fan, Philip S. Yu

Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time.

Classification General Classification

Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models

no code implementations NeurIPS 2009 Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, Jiawei Han

First, we can boost the diversity of classification ensemble by incorporating multiple clustering outputs, each of which provides grouping constraints for the joint label predictions of a set of related objects.

General Classification

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