Search Results for author: Yaqing Wang

Found 28 papers, 5 papers with code

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

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization

no code implementations6 May 2022 Quanming Yao, Yaqing Wang, Bo Han, James Kwok

While the optimization problem is nonconvex and nonsmooth, we show that its critical points still have good statistical performance on the tensor completion problem.

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

Exploring the Common Principal Subspace of Deep Features in Neural Networks

no code implementations6 Oct 2021 Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou

Specifically, we design a new metric $\mathcal{P}$-vector to represent the principal subspace of deep features learned in a DNN, and propose to measure angles between the principal subspaces using $\mathcal{P}$-vectors.

Image Reconstruction Self-Supervised Learning

FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning

no code implementations12 Sep 2021 Liwei Che, Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, Fenglong Ma

In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set.

Federated 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

FedCon: A Contrastive Framework for Federated Semi-Supervised Learning

no code implementations9 Sep 2021 Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, Fenglong Ma

Most existing FedSSL methods focus on the classical scenario, i. e, the labeled and unlabeled data are stored at the client side.

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

Empirical Studies on the Convergence of Feature Spaces in Deep Learning

no code implementations1 Jan 2021 Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou

While deep learning is effective to learn features/representations from data, the distributions of samples in feature spaces learned by various architectures for different training tasks (e. g., latent layers of AEs and feature vectors in CNN classifiers) have not been well-studied or compared.

Image Reconstruction Self-Supervised Learning

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

FedSiam: Towards Adaptive Federated Semi-Supervised Learning

no code implementations6 Dec 2020 Zewei Long, Liwei Che, Yaqing Wang, Muchao Ye, Junyu Luo, Jinze Wu, Houping Xiao, Fenglong Ma

In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.

Federated Learning

A Benchmark Dataset for Understandable Medical Language Translation

no code implementations4 Dec 2020 Junyu Luo, Zifei Zheng, Hanzhong Ye, Muchao Ye, Yaqing Wang, Quanzeng You, Cao Xiao, Fenglong Ma

In this paper, we introduce MedLane -- a new human-annotated Medical Language translation dataset, to align professional medical sentences with layperson-understandable expressions.

Benchmark Machine Translation +2

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

A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion

no code implementations14 Aug 2020 Yaqing Wang, Quanming Yao, James T. Kwok

Extensive low-rank matrix completion experiments on a number of synthetic and real-world data sets show that the proposed method obtains state-of-the-art recovery performance while being the fastest in comparison to existing low-rank matrix learning methods.

Collaborative Filtering Low-Rank Matrix Completion

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.

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

Generalizing from a Few Examples: A Survey on Few-Shot Learning

4 code implementations10 Apr 2019 Yaqing Wang, Quanming Yao, James Kwok, Lionel M. Ni

Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small.

Few-Shot Learning

General Convolutional Sparse Coding with Unknown Noise

no code implementations8 Mar 2019 Yaqing Wang, James T. Kwok, Lionel M. Ni

However, existing CSC methods can only model noises from Gaussian distribution, which is restrictive and unrealistic.

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.

Online Convolutional Sparse Coding with Sample-Dependent Dictionary

no code implementations ICML 2018 Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni

Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing.

online learning

Scalable Online Convolutional Sparse Coding

no code implementations21 Jun 2017 Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni

Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data.

online learning

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