Search Results for author: Shengyu Zhang

Found 38 papers, 10 papers with code

Contrastive Learning with Positive-Negative Frame Mask for Music Representation

no code implementations17 Mar 2022 Dong Yao, Zhou Zhao, Shengyu Zhang, Jieming Zhu, Yudong Zhu, Rui Zhang, Xiuqiang He

We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music.

Contrastive Learning Cover song identification +3

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding

no code implementations ACL 2022 Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, ShiLiang Pu, Fei Wu

To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.

Frame Representation Learning

BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation

1 code implementation4 Mar 2022 Wenqiao Zhang, Lei Zhu, James Hallinan, Andrew Makmur, Shengyu Zhang, Qingpeng Cai, Beng Chin Ooi

In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the potential of medical image SSL models: (1) BoostMIS can adaptively leverage the cluster assumption and consistency regularization of the unlabeled data according to the current learning status.

Active Learning

A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation

no code implementations21 Feb 2022 Dongqi Wang, Shengyu Zhang, Zhipeng Di, Xin Lin, Weihua Zhou, Fei Wu

A common problem in both pruning and distillation is to determine compressed architecture, i. e., the exact number of filters per layer and layer configuration, in order to preserve most of the original model capacity.

Knowledge Distillation Model Compression +1

Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer

no code implementations29 Jan 2022 Yue Wan, Benben Liao, Chang-Yu Hsieh, Shengyu Zhang

In this paper, we propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction without relying on any cheminformatics tools for molecule editing.

SPLDExtraTrees: Robust machine learning approach for predicting kinase inhibitor resistance

no code implementations15 Nov 2021 ZiYi Yang, Zhaofeng Ye, Yijia Xiao, ChangYu Hsieh, Shengyu Zhang

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development.

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey

no code implementations11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.


MIC: Model-agnostic Integrated Cross-channel Recommenders

no code implementations22 Oct 2021 Yujie Lu, Ping Nie, Shengyu Zhang, Ming Zhao, Ruobing Xie, William Yang Wang, Yi Ren

However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions.

Recommendation Systems Semantic Similarity +1

Stable Prediction on Graphs with Agnostic Distribution Shift

no code implementations8 Oct 2021 Shengyu Zhang, Kun Kuang, Jiezhong Qiu, Jin Yu, Zhou Zhao, Hongxia Yang, Zhongfei Zhang, Fei Wu

The results demonstrate that our method outperforms various SOTA GNNs for stable prediction on graphs with agnostic distribution shift, including shift caused by node labels and attributes.

Graph Learning Recommendation Systems

Multi-trends Enhanced Dynamic Micro-video Recommendation

no code implementations8 Oct 2021 Yujie Lu, Yingxuan Huang, Shengyu Zhang, Wei Han, Hui Chen, Zhou Zhao, Fei Wu

In this paper, we propose the DMR framework to explicitly model dynamic multi-trends of users' current preference and make predictions based on both the history and future potential trends.

Recommendation Systems

Why Do We Click: Visual Impression-aware News Recommendation

1 code implementation26 Sep 2021 Jiahao Xun, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Qi Zhang, Jingjie Li, Xiuqiang He, Xiaofei He, Tat-Seng Chua, Fei Wu

In this work, inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news, we propose to capture such visual impression information with visual-semantic modeling for news recommendation.

Decision Making News Recommendation

Fast Extraction of Word Embedding from Q-contexts

no code implementations15 Sep 2021 Junsheng Kong, Weizhao Li, Zeyi Liu, Ben Liao, Jiezhong Qiu, Chang-Yu Hsieh, Yi Cai, Shengyu Zhang

In this work, we show that with merely a small fraction of contexts (Q-contexts)which are typical in the whole corpus (and their mutual information with words), one can construct high-quality word embedding with negligible errors.

CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation

no code implementations11 Sep 2021 Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, Fei Wu

In this paper, we propose to learn accurate and robust user representations, which are required to be less sensitive to (attack on) noisy behaviors and trust more on the indispensable ones, by modeling counterfactual data distribution.

Representation Learning Sequential Recommendation

Neural Predictor based Quantum Architecture Search

no code implementations11 Mar 2021 Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, Hong Yao

For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning.

Neural Architecture Search Quantum Physics

TrimNet: learning molecular representation from triplet messages for biomedicine

1 code implementation4 Nov 2020 Pengyong Li, Yuquan Li, Chang-Yu Hsieh, Shengyu Zhang, Xianggen Liu, Huanxiang Liu, Sen Song, Xiaojun Yao

These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning.

Drug Discovery Molecular Property Prediction +1

Future-Aware Diverse Trends Framework for Recommendation

no code implementations1 Nov 2020 Yujie Lu, Shengyu Zhang, Yingxuan Huang, Luyao Wang, Xinyao Yu, Zhou Zhao, Fei Wu

By diverse trends, supposing the future preferences can be diversified, we propose the diverse trends extractor and the time-aware mechanism to represent the possible trends of preferences for a given user with multiple vectors.

Representation Learning Sequential Recommendation

Differentiable Quantum Architecture Search

1 code implementation16 Oct 2020 Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, Hong Yao

Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion.

Quantum Physics

MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination

no code implementations2 Oct 2020 Shengyu Zhang, Donghui Wang, Zhou Zhao, Siliang Tang, Di Xie, Fei Wu

In this paper, we investigate the problem of text-to-pedestrian synthesis, which has many potential applications in art, design, and video surveillance.

Image Generation

DeVLBert: Learning Deconfounded Visio-Linguistic Representations

1 code implementation16 Aug 2020 Shengyu Zhang, Tan Jiang, Tan Wang, Kun Kuang, Zhou Zhao, Jianke Zhu, Jin Yu, Hongxia Yang, Fei Wu

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned.

Image Retrieval Question Answering +1

Poet: Product-oriented Video Captioner for E-commerce

1 code implementation16 Aug 2020 Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Jie Liu, Jingren Zhou, Hongxia Yang, Fei Wu

Then, based on the aspects of the video-associated product, we perform knowledge-enhanced spatial-temporal inference on those graphs for capturing the dynamic change of fine-grained product-part characteristics.

Video Captioning

Comprehensive Information Integration Modeling Framework for Video Titling

1 code implementation24 Jun 2020 Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Tan Jiang, Jingren Zhou, Hongxia Yang, Fei Wu

In e-commerce, consumer-generated videos, which in general deliver consumers' individual preferences for the different aspects of certain products, are massive in volume.

Video Captioning

Adaptive Double-Exploration Tradeoff for Outlier Detection

no code implementations13 May 2020 Xiaojin Zhang, Honglei Zhuang, Shengyu Zhang, Yuan Zhou

We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold.

Outlier Detection

Grounded and Controllable Image Completion by Incorporating Lexical Semantics

no code implementations29 Feb 2020 Shengyu Zhang, Tan Jiang, Qinghao Huang, Ziqi Tan, Zhou Zhao, Siliang Tang, Jin Yu, Hongxia Yang, Yi Yang, Fei Wu

Existing image completion procedure is highly subjective by considering only visual context, which may trigger unpredictable results which are plausible but not faithful to a grounded knowledge.

Contextual Combinatorial Conservative Bandits

no code implementations26 Nov 2019 Xiaojin Zhang, Shuai Li, Weiwen Liu, Shengyu Zhang

The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range of practical scenarios.

Multi-Armed Bandits

DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow

no code implementations30 Oct 2019 Xiang Pan, Tianyu Zhao, Minghua Chen, Shengyu Zhang

We then directly reconstruct the phase angles from the generations and loads by using the power flow equations.

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

1 code implementation22 Jun 2019 Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard Zemel, Shengyu Zhang

We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science.

A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR

no code implementations13 Jun 2019 Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang

Most recent efforts have been devoted to defending noisy labels by discarding noisy samples from the training set or assigning weights to training samples, where the weight associated with a noisy sample is expected to be small.

Data Augmentation

Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization

no code implementations13 Jun 2019 Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh, Guangyong Chen, Shengyu Zhang

The IGNN model is based on an elegant and fundamental idea in information theory as explained in the main text, and it could be easily generalized beyond the contexts of molecular graphs considered in this work.

Drug Discovery Quantum Chemistry Regression

Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks

1 code implementation15 May 2019 Guangyong Chen, Pengfei Chen, Yujun Shi, Chang-Yu Hsieh, Benben Liao, Shengyu Zhang

Our work is based on an excellent idea that whitening the inputs of neural networks can achieve a fast convergence speed.

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

1 code implementation13 May 2019 Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang

Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the noisy labels.

Image Classification

Quantum algorithms for feedforward neural networks

no code implementations7 Dec 2018 Jonathan Allcock, Chang-Yu Hsieh, Iordanis Kerenidis, Shengyu Zhang

The running times of our algorithms can be quadratically faster in the size of the network than their standard classical counterparts since they depend linearly on the number of neurons in the network, as opposed to the number of connections between neurons as in the classical case.

Log Hyperbolic Cosine Loss Improves Variational Auto-Encoder

no code implementations27 Sep 2018 Pengfei Chen, Guangyong Chen, Shengyu Zhang

In Variational Auto-Encoder (VAE), the default choice of reconstruction loss function between the decoded sample and the input is the squared $L_2$.

Learning to Aggregate Ordinal Labels by Maximizing Separating Width

no code implementations ICML 2017 Guangyong Chen, Shengyu Zhang, Di Lin, Hui Huang, Pheng Ann Heng

While crowdsourcing has been a cost and time efficient method to label massive samples, one critical issue is quality control, for which the key challenge is to infer the ground truth from noisy or even adversarial data by various users.

Networked Fairness in Cake Cutting

no code implementations7 Jul 2017 Xiaohui Bei, Youming Qiao, Shengyu Zhang

We introduce a graphical framework for fair division in cake cutting, where comparisons between agents are limited by an underlying network structure.


On the Complexity of Trial and Error

no code implementations6 May 2012 Xiaohui Bei, Ning Chen, Shengyu Zhang

On one hand, despite the seemingly very little information provided by the verification oracle, efficient algorithms do exist for a number of important problems.

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