Search Results for author: Shiwan Zhao

Found 26 papers, 6 papers with code

S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search

no code implementations ECCV 2020 Zhihang Yuan, Bingzhe Wu, Guangyu Sun, Zheng Liang, Shiwan Zhao, Weichen Bi

To this end, based on a given CNN model, we first generate a CNN architecture space in which each architecture is a multi-stage CNN generated from the given model using some predefined transformations.

Neural Architecture Search

Language Resource Efficient Learning for Captioning

no code implementations Findings (EMNLP) 2021 Jia Chen, Yike Wu, Shiwan Zhao, Qin Jin

Our analysis of caption models with SC loss shows that the performance degradation is caused by the increasingly noisy estimation of reward and baseline with fewer language resources.

Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition

no code implementations31 Aug 2023 Xuechen Wang, Shiwan Zhao, Yong Qin

This approach increases the inter-class distances and decreases the intra-class distances, mitigating the issue of blurred boundaries.

Contrastive Learning Speech Emotion Recognition

RAMP: Retrieval-Augmented MOS Prediction via Confidence-based Dynamic Weighting

no code implementations31 Aug 2023 Hui Wang, Shiwan Zhao, Xiguang Zheng, Yong Qin

Automatic Mean Opinion Score (MOS) prediction is crucial to evaluate the perceptual quality of the synthetic speech.

Retrieval Self-Supervised Learning

Better Zero-Shot Reasoning with Role-Play Prompting

1 code implementation15 Aug 2023 Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou

This highlights its potential to augment the reasoning capabilities of LLMs.

Uncertainty in Natural Language Processing: Sources, Quantification, and Applications

no code implementations5 Jun 2023 Mengting Hu, Zhen Zhang, Shiwan Zhao, Minlie Huang, Bingzhe Wu

Therefore, in this survey, we provide a comprehensive review of uncertainty-relevant works in the NLP field.

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

1 code implementation29 May 2023 Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments.

named-entity-recognition Named Entity Recognition +1

MADI: Inter-domain Matching and Intra-domain Discrimination for Cross-domain Speech Recognition

no code implementations22 Feb 2023 Jiaming Zhou, Shiwan Zhao, Ning Jiang, Guoqing Zhao, Yong Qin

Unsupervised domain adaptation (UDA) aims to improve the performance on the unlabeled target domain by transferring knowledge from the source to the target domain.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation

1 code implementation19 Oct 2022 Mengting Hu, Yike Wu, Hang Gao, Yinhao Bai, Shiwan Zhao

By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.

Aspect-Based Sentiment Analysis (ABSA) Data Augmentation +1

Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances

1 code implementation COLING 2022 Yike Wu, Yu Zhao, Shiwan Zhao, Ying Zhang, Xiaojie Yuan, Guoqing Zhao, Ning Jiang

In this work, we define the training instances with the same question type but different answers as \textit{superficially similar instances}, and attribute the language priors to the confusion of VQA model on such instances.

Question Answering Visual Question Answering

Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning

no code implementations24 Jul 2020 Yiqin Yu, Xu Min, Shiwan Zhao, Jing Mei, Fei Wang, Dongsheng Li, Kenney Ng, Shaochun Li

In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions.

Knowledge Distillation Transfer Learning

S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search

no code implementations16 Nov 2019 Zhihang Yuan, Bingzhe Wu, Zheng Liang, Shiwan Zhao, Weichen Bi, Guangyu Sun

Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN).

Neural Architecture Search

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

no code implementations5 Oct 2019 Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan YAO, Guangyu Sun, Li Wang, Xiaolu Zhang, Jun Zhou

Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.

Generalization Bounds

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

no code implementations NeurIPS 2019 Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.

Improving Captioning for Low-Resource Languages by Cycle Consistency

no code implementations21 Aug 2019 Yike Wu, Shiwan Zhao, Jia Chen, Ying Zhang, Xiaojie Yuan, Zhong Su

Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years.


Infinite Curriculum Learning for Efficiently Detecting Gastric Ulcers in WCE Images

no code implementations7 Sep 2018 Xiaolu Zhang, Shiwan Zhao, Lingxi Xie

This paper considers WCE-based gastric ulcer detection, in which the major challenge is to detect the lesions in a local region.

Binary Classification

G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

no code implementations30 Jun 2018 Bingzhe Wu, Xiaolu Zhang, Shiwan Zhao, Lingxi Xie, Caihong Zeng, Zhihong Liu, Guangyu Sun

Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has).

Classification Decision Making +2

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