1 code implementation • 28 Feb 2024 • Zhe Wang, Jianping Wu, Mengjun Zheng, Chenchen Geng, Borui Zhen, Wei zhang, Hui Wu, Zhengyang Xu, Gang Xu, Si Chen, Xiang Li
Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking. Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides. The current version supports hydrocarbon-stapled peptides containing 2 non-standard amino acids (norleucine and 2-aminoisobutyric acid) and 6 nonnatural anchoring residues (S3, S5, S8, R3, R5 and R8). Then we established a hand-curated dataset of 201 hydrocarbon-stapled peptides and 384 linear peptides with sequence information and experimental membrane permeability, to showcase StaPep's application in artificial intelligence projects. A machine learning-based predictor utilizing above calculated features was developed with AUC of 0. 85, for identifying cell-penetrating hydrocarbon-stapled peptides. StaPep's pipeline spans data retrieval, cleaning, structure generation, molecular feature calculation, and machine learning model construction for hydrocarbon-stapled peptides. The source codes and dataset are freely available on Github: https://github. com/dahuilangda/stapep_package.
1 code implementation • 13 Dec 2023 • Liuxiang Qiu, Si Chen, Yan Yan, Jing-Hao Xue, Da-Han Wang, Shunzhi Zhu
Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space due to the large modality discrepancy between VIS and IR images.
no code implementations • 25 Oct 2023 • Zixin Ding, Si Chen, Ruoxi Jia, Yuxin Chen
To address these limitations, we propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition.
no code implementations • 29 Jun 2023 • Yu Tian, Bofang Li, Si Chen, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng, Qian Wang, Chenliang Li
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.
no code implementations • 26 Oct 2022 • Stefan Schmollinger, Si Chen, Sabeeha S. Merchant
Accordingly, photosynthetic eukaryotes are of great interest for biotechnological exploitation, carbon sequestration and bioremediation, with many of the applications involving various trace elements and consequently affecting their quota and intracellular distribution.
no code implementations • 14 Sep 2022 • Si Chen, Junfeng Fan
Security concerns about a machine learning model used in a prediction-as-a-service include the privacy of the model, the query and the result.
1 code implementation • 16 Jul 2022 • Xinyi Zou, Yan Yan, Jing-Hao Xue, Si Chen, Hanzi Wang
Extensive experiments on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed CDNet against several state-of-the-art FSL methods.
cross-domain few-shot learning Facial Expression Recognition +1
2 code implementations • 23 Jun 2022 • Chen Lin, Si Chen, Meifang Zeng, Sheng Zhang, Min Gao, Hui Li
Leg-UP learns user behavior patterns from real users in the sampled ``templates'' and constructs fake user profiles.
no code implementations • 21 Jun 2022 • Yi Cui, Wenfeng Shen, Jian Zhang, Weijia Lu, Chuang Liu, Lin Sun, Si Chen
The generator in IDS-EBGAN is responsible for converting the original malicious network traffic in the training set into adversarial malicious examples.
no code implementations • 14 Jun 2022 • Si Chen, Yi Zeng, Jiachen T. Wang, Won Park, Xun Chen, Lingjuan Lyu, Zhuoqing Mao, Ruoxi Jia
Our work is the first to provide a thorough understanding of leveraging model inversion for effective backdoor removal by addressing key questions about reconstructed samples' properties, perceptual similarity, and the potential presence of backdoor triggers.
no code implementations • 9 May 2022 • Si Chen, Chen Lin, Wanxian Guan, Jiayi Wei, Xingyuan Bu, He guo, Hui Li, Xubin Li, Jian Xu, Bo Zheng
In this paper, we present a visual encoding framework for CTR prediction to overcome these problems.
1 code implementation • 15 Apr 2022 • Weiyan Shi, Ryan Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu
Utilizing the fact that sensitive information in language data tends to be sparse, Shi et al. (2021) formalized a DP notion extension called Selective Differential Privacy (SDP) to protect only the sensitive tokens defined by a policy function.
no code implementations • 8 Mar 2022 • Xi Weng, Yan Yan, Si Chen, Jing-Hao Xue, Hanzi Wang
In this paper, we present a novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes.
1 code implementation • CVPR 2022 • Mostafa Kahla, Si Chen, Hoang Anh Just, Ruoxi Jia
In this paper, we introduce an algorithm, Boundary-Repelling Model Inversion (BREP-MI), to invert private training data using only the target model's predicted labels.
no code implementations • 18 Jan 2022 • Xinyi Zou, Yan Yan, Jing-Hao Xue, Si Chen, Hanzi Wang
To alleviate the problem of limited base classes in our FER task, we propose a novel Emotion Guided Similarity Network (EGS-Net), consisting of an emotion branch and a similarity branch, based on a two-stage learning framework.
cross-domain few-shot learning Facial Expression Recognition +1
1 code implementation • 24 Nov 2021 • Yingyan Zeng, Jiachen T. Wang, Si Chen, Hoang Anh Just, Ran Jin, Ruoxi Jia
In this work, we propose ModelPred, a framework that helps to understand the impact of changes in training data on a trained model.
3 code implementations • ICLR 2022 • Yi Zeng, Si Chen, Won Park, Z. Morley Mao, Ming Jin, Ruoxi Jia
Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.
no code implementations • 14 Jul 2021 • Si Chen, Tianhao Wang, Ruoxi Jia
Our algorithm does not rely on any feedback from annotators in the target domain and hence, can be used to perform zero-round active learning or warm-start existing multi-round active learning strategies.
no code implementations • CVPR 2021 • Ying Shu, Yan Yan, Si Chen, Jing-Hao Xue, Chunhua Shen, Hanzi Wang
First, three auxiliary tasks, consisting of a Patch Rotation Task (PRT), a Patch Segmentation Task (PST), and a Patch Classification Task (PCT), are jointly developed to learn the spatial-semantic relationship from large-scale unlabeled facial data.
Ranked #3 on Facial Attribute Classification on LFWA
no code implementations • 23 Apr 2021 • Tianhao Wang, Si Chen, Ruoxi Jia
In this work, we initiate the study of one-round active learning, which aims to select a subset of unlabeled data points that achieve the highest model performance after being labeled with only the information from initially labeled data points.
no code implementations • 13 Feb 2021 • Si Chen, Yuqiu Qian, Hui Li, Chen Lin
We leverage Graph Neural Network and multi-task learning to design M$^3$Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Fanyu Meng, Junlan Feng, Danping Yin, Si Chen, Min Hu
Syntactic information is essential for both sentiment analysis(SA) and aspect-based sentiment analysis(ABSA).
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
2 code implementations • ICCV 2021 • Si Chen, Mostafa Kahla, Ruoxi Jia, Guo-Jun Qi
We present a novel inversion-specific GAN that can better distill knowledge useful for performing attacks on private models from public data.
2 code implementations • 17 May 2020 • Chen Lin, Si Chen, Hui Li, Yanghua Xiao, Lianyun Li, Qian Yang
Recommendation Systems (RS) have become an essential part of many online services.
no code implementations • 7 Feb 2020 • Yihan Du, Yan Yan, Si Chen, Yang Hua
This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods.
no code implementations • 7 Feb 2020 • Wanxiang Yang, Yan Yan, Si Chen
In this paper, we propose a novel person ReID method, which learns the spatial dependencies between the local regions and extracts the discriminative feature representation of the pedestrian image based on Long Short-Term Memory (LSTM), dealing with the problem of occlusions.
no code implementations • 6 Feb 2020 • Yan Yan, Ying Huang, Si Chen, Chunhua Shen, Hanzi Wang
Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions.
Facial Expression Recognition Facial Expression Recognition (FER) +1
no code implementations • 27 Mar 2019 • Si Chen, Guo-Hua Peng, Xing-Shi He, Xin-She Yang
In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria.
no code implementations • 3 May 2018 • Ni Zhuang, Yan Yan, Si Chen, Hanzi Wang
In order to address the above problems, we propose a novel multi-task learning of cas- caded convolutional neural network method, termed MCFA, for predicting multiple facial attributes simultaneously.
no code implementations • 3 May 2018 • Ni Zhuang, Yan Yan, Si Chen, Hanzi Wang, Chunhua Shen
To address the above problem, we propose a novel deep transfer neural network method based on multi-label learning for facial attribute classification, termed FMTNet, which consists of three sub-networks: the Face detection Network (FNet), the Multi-label learning Network (MNet) and the Transfer learning Network (TNet).
no code implementations • 25 Mar 2016 • Yan Yan, Hanzi Wang, Si Chen, Xiaochun Cao, David Zhang
This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes.
no code implementations • 13 Dec 2015 • Meera Hahn, Si Chen, Afshin Dehghan
In this paper, we study a discriminatively trained deep convolutional network for the task of visual tracking.
no code implementations • CVPR 2015 • Mandar Dixit, Si Chen, Dashan Gao, Nikhil Rasiwasia, Nuno Vasconcelos
A semantic FV is then computed as a Gaussian Mixture FV in the space of these natural parameters.