Search Results for author: Feng Chen

Found 119 papers, 31 papers with code

Task Understanding from Confusing Multi-task Data

no code implementations ICML 2020 Xin Su, Yizhou Jiang, Shangqi Guo, Feng Chen

Beyond machine learning's success in the specific tasks, research for learning multiple tasks simultaneously is referred to as multi-task learning.

Multi-Task Learning

CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning

no code implementations20 Feb 2024 Feng Chen

Achieving a universally high accuracy in object detection is quite challenging, and the mainstream focus in the industry currently lies on detecting specific classes of objects.

Object object-detection +2

Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness

no code implementations19 Feb 2024 Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen

Theoretical analysis yields sub-linear upper bounds for both loss regret and the cumulative violation of fairness constraints.

Fairness Meta-Learning

Preserving Silent Features for Domain Generalization

no code implementations6 Jan 2024 Chujie Zhao, Tianren Zhang, Feng Chen

In light of this, we propose a simple yet effective method termed STEP (Silent Feature Preservation) to improve the generalization performance of the self-supervised contrastive learning pre-trained model by alleviating the suppression of silent features during the supervised fine-tuning process.

Contrastive Learning Domain Generalization +1

FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology Segmentation

no code implementations20 Dec 2023 Yuan Zhang, Yaolei Qi, Xiaoming Qi, Lotfi Senhadji, Yongyue Wei, Feng Chen, Guanyu Yang

Federated learning (FL) for histopathology image segmentation involving multiple medical sites plays a crucial role in advancing the field of accurate disease diagnosis and treatment.

Federated Learning Image Segmentation +2

DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics

no code implementations NeurIPS 2023 Zhiao Huang, Feng Chen, Yewen Pu, Chunru Lin, Hao Su, Chuang Gan

Combining gradient-based trajectory optimization with differentiable physics simulation is an efficient technique for solving soft-body manipulation problems.


Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts

no code implementations9 Dec 2023 Xiruo Ding, Zhecheng Sheng, Brian Hur, Feng Chen, Serguei V. S. Pakhomov, Trevor Cohen

We focus on confounding by provenance, a form of distribution shift that emerges in the context of multi-institutional datasets when there are differences in source-specific language use and class distributions.

Technical Report for Argoverse Challenges on 4D Occupancy Forecasting

no code implementations27 Nov 2023 Pengfei Zheng, Kanokphan Lertniphonphan, Feng Chen, Siwei Chen, Bingchuan Sun, Jun Xie, Zhepeng Wang

This report presents our Le3DE2E_Occ solution for 4D Occupancy Forecasting in Argoverse Challenges at CVPR 2023 Workshop on Autonomous Driving (WAD).

Autonomous Driving

Fairness-Aware Domain Generalization under Covariate and Dependence Shifts

no code implementations23 Nov 2023 Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen

Achieving the generalization of an invariant classifier from source domains to shifted target domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning.

Domain Generalization Fairness

Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization

1 code implementation10 Nov 2023 Russell Alan Hart, Linlin Yu, Yifei Lou, Feng Chen

A large number of literature relies on uncertainty quantification to evaluate the reliability of a learning model, which is particularly important for applications of out-of-distribution (OOD) detection and misclassification detection.

Node Classification Out of Distribution (OOD) Detection +1

RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

no code implementations2 Nov 2023 YuFei Wang, Zhou Xian, Feng Chen, Tsun-Hsuan Wang, Yian Wang, Zackory Erickson, David Held, Chuang Gan

We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.

Motion Planning

Efficient Human-AI Coordination via Preparatory Language-based Convention

no code implementations1 Nov 2023 Cong Guan, Lichao Zhang, Chunpeng Fan, Yichen Li, Feng Chen, Lihe Li, Yunjia Tian, Lei Yuan, Yang Yu

Developing intelligent agents capable of seamless coordination with humans is a critical step towards achieving artificial general intelligence.

Language Modelling Large Language Model

Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation

no code implementations24 Oct 2023 Yinjie Lei, Zixuan Wang, Feng Chen, Guoqing Wang, Peng Wang, Yang Yang

Multi-modal 3D scene understanding has gained considerable attention due to its wide applications in many areas, such as autonomous driving and human-computer interaction.

Autonomous Driving Scene Understanding

Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation

1 code implementation2 Oct 2023 Dong H. Jeong, Jin-Hee Cho, Feng Chen, Audun Josang, Soo-Yeon Ji

In this paper, to improve users' learning and understanding of NNs, an interactive learning system is designed to create digit patterns and recognize them in real time.

Active Learning

Adapting Vision Foundation Models for Plant Phenotyping

no code implementations ICCV 2023 Feng Chen, Mario Valerio Giuffrida, Sotirios A. Tsaftaris

The experimental results show that a foundation model can be efficiently adapted to multiple plant phenotyping tasks, yielding similar performance as the state-of-the-art (SoTA) models specifically designed or trained for each task.

Instance Segmentation Plant Phenotyping +1

Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective

no code implementations18 Sep 2023 Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen

In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts.

Domain Generalization

Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction

2 code implementations25 Jun 2023 Feng Chen, Yujian Feng

Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE) necessitate the fundamental reasoning capacity for intricate linguistic and multimodal comprehension.

Data Augmentation Domain Generalization +5

Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks

1 code implementation NeurIPS 2023 Feng Chen, Daniel Kunin, Atsushi Yamamura, Surya Ganguli

In this work, we reveal a strong implicit bias of stochastic gradient descent (SGD) that drives overly expressive networks to much simpler subnetworks, thereby dramatically reducing the number of independent parameters, and improving generalization.

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms

1 code implementation2 Jun 2023 Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu

We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels.

counterfactual Disentanglement

Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features

no code implementations1 Jun 2023 Kunal Mukherjee, Joshua Wiedemeier, Tianhao Wang, Muhyun Kim, Feng Chen, Murat Kantarcioglu, Kangkook Jee

PROVEXPLAINER allowed simple DT models to achieve 95% fidelity to the GNN on program classification tasks with general graph structural features, and 99% fidelity on malware detection tasks with a task-specific feature package tailored for direct interpretation.

Decision Making Descriptive +2

Towards Fair Disentangled Online Learning for Changing Environments

no code implementations31 May 2023 Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Grant, Feng Chen

To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor.


Multiresolution Feature Guidance Based Transformer for Anomaly Detection

no code implementations24 May 2023 Shuting Yan, Pingping Chen, Honghui Chen, Huan Mao, Feng Chen, Zhijian Lin

Under the tacit knowledge guidance of the AGN, the anomaly detection network named Trans utilizes Transformer to effectively establish a relationship between features with multiresolution, enhancing the ability of the Trans in fitting the normal data manifold.

Unsupervised Anomaly Detection

Rethinking Speech Recognition with A Multimodal Perspective via Acoustic and Semantic Cooperative Decoding

no code implementations23 May 2023 Tian-Hao Zhang, Hai-Bo Qin, Zhi-Hao Lai, Song-Lu Chen, Qi Liu, Feng Chen, Xinyuan Qian, Xu-Cheng Yin

The experimental results show that ASCD significantly improves the performance by leveraging both the acoustic and semantic information cooperatively.

speech-recognition Speech Recognition

Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers

1 code implementation10 May 2023 Lei Yuan, Zi-Qian Zhang, Ke Xue, Hao Yin, Feng Chen, Cong Guan, Li-He Li, Chao Qian, Yang Yu

Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity.


Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation

no code implementations9 May 2023 Lei Yuan, Feng Chen, Zhongzhang Zhang, Yang Yu

In specific, we introduce a novel message-attacking approach that models the learning of the auxiliary attacker as a cooperative problem under a shared goal to minimize the coordination ability of the ego system, with which every information channel may suffer from distinct message attacks.

Multi-agent Reinforcement Learning

Robust Multi-agent Communication via Multi-view Message Certification

no code implementations7 May 2023 Lei Yuan, Tao Jiang, Lihe Li, Feng Chen, Zongzhang Zhang, Yang Yu

Many multi-agent scenarios require message sharing among agents to promote coordination, hastening the robustness of multi-agent communication when policies are deployed in a message perturbation environment.

Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information

no code implementations19 Feb 2023 Zhen Guo, Qi Zhang, Xinwei An, Qisheng Zhang, Audun Jøsang, Lance M. Kaplan, Feng Chen, Dong H. Jeong, Jin-Hee Cho

Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches.

Decision Making intent-classification +1

Uncertainty-guided Learning for Improving Image Manipulation Detection

no code implementations ICCV 2023 Kaixiang Ji, Feng Chen, Xin Guo, Yadong Xu, Jian Wang, Jingdong Chen

Image manipulation detection (IMD) is of vital importance as faking images and spreading misinformation can be malicious and harm our daily life.

Image Manipulation Image Manipulation Detection +1

Neural network quantum state with proximal optimization: a ground-state searching scheme based on variational Monte Carlo

no code implementations29 Oct 2022 Feng Chen, Ming Xue

Neural network quantum states (NQS), incorporating with variational Monte Carlo (VMC) method, are shown to be a promising way to investigate quantum many-body physics.

Variational Monte Carlo

Block-Structured Optimization for Subgraph Detection in Interdependent Networks

no code implementations6 Oct 2022 Fei Jie, Chunpai Wang, Feng Chen, Lei LI, Xindong Wu

We propose a generalized framework for block-structured nonconvex optimization, which can be applied to structured subgraph detection in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others.

Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?

no code implementations6 Oct 2022 Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite

Third, we show how the flatness of the error landscape at the end of training determines a limit on the fraction of weights that can be pruned at each iteration of IMP.

Framing Algorithmic Recourse for Anomaly Detection

no code implementations29 Jun 2022 Debanjan Datta, Feng Chen, Naren Ramakrishnan

We present an approach -- Context preserving Algorithmic Recourse for Anomalies in Tabular data (CARAT), that is effective, scalable, and agnostic to the underlying anomaly detection model.

Anomaly Detection

Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs

no code implementations26 Jun 2022 Chunpai Wang, Daniel B. Neill, Feng Chen

We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs.

Tree Decomposition

A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning

no code implementations12 Jun 2022 Zhen Guo, Zelin Wan, Qisheng Zhang, Xujiang Zhao, Feng Chen, Jin-Hee Cho, Qi Zhang, Lance M. Kaplan, Dong H. Jeong, Audun Jøsang

We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty.

Decision Making

Adaptive Fairness-Aware Online Meta-Learning for Changing Environments

no code implementations20 May 2022 Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen

Furthermore, to determine a good model parameter at each round, we propose a novel adaptive fairness-aware online meta-learning algorithm, namely FairSAOML, which is able to adapt to changing environments in both bias control and model precision.

Fairness Meta-Learning

S4OD: Semi-Supervised learning for Single-Stage Object Detection

no code implementations9 Apr 2022 Yueming Zhang, Xingxu Yao, Chao Liu, Feng Chen, Xiaolin Song, Tengfei Xing, Runbo Hu, Hua Chai, Pengfei Xu, Guoshan Zhang

In this paper, we design a dynamic self-adaptive threshold (DSAT) strategy in classification branch, which can automatically select pseudo labels to achieve an optimal trade-off between quality and quantity.

Object object-detection +3

Multi-Agent Policy Transfer via Task Relationship Modeling

no code implementations9 Mar 2022 Rongjun Qin, Feng Chen, Tonghan Wang, Lei Yuan, Xiaoran Wu, Zongzhang Zhang, Chongjie Zhang, Yang Yu

We demonstrate that the task representation can capture the relationship among tasks, and can generalize to unseen tasks.

Transfer Learning

Layer Adaptive Deep Neural Networks for Out-of-distribution Detection

1 code implementation1 Mar 2022 Haoliang Wang, Chen Zhao, Xujiang Zhao, Feng Chen

During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

SEED: Sound Event Early Detection via Evidential Uncertainty

no code implementations5 Feb 2022 Xujiang Zhao, Xuchao Zhang, Wei Cheng, Wenchao Yu, Yuncong Chen, Haifeng Chen, Feng Chen

Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes.

Event Detection Sound Event Detection

PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information

1 code implementation30 Jan 2022 Changbin Li, Suraj Kothawade, Feng Chen, Rishabh Iyer

Meta learning has proven to be able to learn a parametrized model for FSC by training on various other classification tasks.


Transfering Hierarchical Structure with Dual Meta Imitation Learning

no code implementations28 Jan 2022 Chongkai Gao, Yizhou Jiang, Feng Chen

Hierarchical Imitation Learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations.

Few-Shot Imitation Learning Imitation Learning +1

Learning Invariable Semantical Representation from Language for Extensible Policy Generalization

no code implementations26 Jan 2022 Yihan Li, Jinsheng Ren, Tianrun Xu, Tianren Zhang, Haichuan Gao, Feng Chen

Recently, incorporating natural language instructions into reinforcement learning (RL) to learn semantically meaningful representations and foster generalization has caught many concerns.

Reinforcement Learning (RL)

Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning

no code implementations6 Dec 2021 Chunlin Ji, Hanchu Shen, Zhan Xiong, Feng Chen, Meiying Zhang, Huiwen Yang

Then We propose three information-theoretic loss functions for deterministic GZSL model: a mutual information loss to bridge seen data and target classes; an uncertainty-aware entropy constraint loss to prevent overfitting when using seen data to learn the embedding of target classes; a semantic preserving cross entropy loss to preserve the semantic relation when mapping the semantic representations to the common space.

Generalized Zero-Shot Learning Relation +1

Transferring Hierarchical Structure with Dual Meta Imitation Learning

no code implementations29 Sep 2021 Chongkai Gao, Yizhou Jiang, Feng Chen

Hierarchical Imitation learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations.

Few-Shot Imitation Learning Imitation Learning +1

Memory Regulation and Alignment toward Generalizer RGB-Infrared Person

1 code implementation18 Sep 2021 Feng Chen, Fei Wu, Qi Wu, Zhiguo Wan

The domain shift, coming from unneglectable modality gap and non-overlapped identity classes between training and test sets, is a major issue of RGB-Infrared person re-identification.

Attribute Metric Learning +1

Homogeneous and Heterogeneous Relational Graph for Visible-infrared Person Re-identification

1 code implementation18 Sep 2021 Yujian Feng, Feng Chen, Jian Yu, Yimu Ji, Fei Wu, Shangdong Liu, Xiao-Yuan Jing

Existing VI Re-ID methods mainly focus on extracting homogeneous structural relationships in an image, i. e. the relations between local features, while ignoring the heterogeneous correlation of local features in different modalities.

Person Re-Identification

Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition

no code implementations14 Sep 2021 Chuan-Fei Zhang, Yan Liu, Tian-Hao Zhang, Song-Lu Chen, Feng Chen, Xu-Cheng Yin

To tackle the above problems, we propose a new non-autoregressive transformer with a unified bidirectional decoder (NAT-UBD), which can simultaneously utilize left-to-right and right-to-left contexts.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation

1 code implementation EMNLP 2021 Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen, Jinho D. Choi

Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages.

Cross-Lingual Transfer named-entity-recognition +4

Subjective Learning for Open-Ended Data

no code implementations27 Aug 2021 Tianren Zhang, Yizhou Jiang, Xin Su, Shangqi Guo, Feng Chen

In this paper, we present a novel supervised learning framework of learning from open-ended data, which is modeled as data implicitly sampled from multiple domains with the data in each domain obeying a domain-specific target function.

Fairness-Aware Online Meta-learning

no code implementations21 Aug 2021 Chen Zhao, Feng Chen, Bhavani Thuraisingham

To overcome such issues and bridge the gap, in this paper for the first time we proposed a novel online meta-learning algorithm, namely FFML, which is under the setting of unfairness prevention.

Classification Fairness +2

Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach

no code implementations19 Aug 2021 Jinlei Zhang, Feng Chen, Lixing Yang, Wei Ma, Guangyin Jin, Ziyou Gao

This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understand the system-wide real-time operation state.

CRIL: Continual Robot Imitation Learning via Generative and Prediction Model

1 code implementation17 Jun 2021 Chongkai Gao, Haichuan Gao, Shangqi Guo, Tianren Zhang, Feng Chen

Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations.

Generative Adversarial Network Imitation Learning

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning

1 code implementation NeurIPS 2021 KrishnaTeja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh Iyer

In this work, we propose RETRIEVE, a coreset selection framework for efficient and robust semi-supervised learning.

Local Aggressive Adversarial Attacks on 3D Point Cloud

1 code implementation19 May 2021 Yiming Sun, Feng Chen, Zhiyu Chen, Mingjie Wang

However, the perturbations of global point are not effective for misleading the victim model.

Adversarial Attack Image to 3D

FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting

no code implementations6 May 2021 Bi-Ying Yan, Chao Yang, Feng Chen, Kohei Takeda, Changjun Wang

To the best of our knowledge, this is the first network architecture with flow and deformation separation to model the evolution of radar echoes for precipitation nowcasting.

Optical Flow Estimation

Success-Rate Targeted Reinforcement Learning by Disorientation Penalty

no code implementations1 Jan 2021 Haichuan Gao, Zhile Yang, Tian Tan, Feng Chen

Unfortunately, applying traditional Bellman updates to value function learning can be problematic for learning undiscounted return, and thus not suitable for optimizing success rate.

Decision Making Q-Learning +2

Multidimensional Uncertainty-Aware Evidential Neural Networks

1 code implementation26 Dec 2020 Yibo Hu, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho, Feng Chen

By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem.

Generative Adversarial Network Multi-class Classification +3

Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning

no code implementations NeurIPS 2020 Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu

We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data.

A Nested Bi-level Optimization Framework for Robust Few Shot Learning

no code implementations13 Nov 2020 KrishnaTeja Killamsetty, Changbin Li, Chen Zhao, Rishabh Iyer, Feng Chen

Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal.

Few-Shot Learning

End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining

1 code implementation27 Oct 2020 Song-Lu Chen, Shu Tian, Jia-Wei Ma, Qi Liu, Chun Yang, Feng Chen, Xu-Cheng Yin

Second, we propose to predict the quadrilateral bounding box in the local region by regressing the four corners of the license plate to robustly detect oblique license plates.

License Plate Detection License Plate Recognition +1

Uncertainty Aware Semi-Supervised Learning on Graph Data

1 code implementation NeurIPS 2020 Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho

To clarify the reasons behind the results, we provided the theoretical proof that explains the relationships between different types of uncertainties considered in this work.

Node Classification Out of Distribution (OOD) Detection

How Out-of-Distribution Data Hurts Semi-Supervised Learning

1 code implementation7 Oct 2020 Xujiang Zhao, Killamsetty Krishnateja, Rishabh Iyer, Feng Chen

This work addresses the following question: How do out-of-distribution (OOD) data adversely affect semi-supervised learning algorithms?

Hyperparameter Optimization

AI Centered on Scene Fitting and Dynamic Cognitive Network

no code implementations2 Oct 2020 Feng Chen

This paper briefly analyzes the advantages and problems of AI mainstream technology and puts forward: To achieve stronger Artificial Intelligence, the end-to-end function calculation must be changed and adopt the technology system centered on scene fitting.

A Primal-Dual Subgradient Approachfor Fair Meta Learning

1 code implementation26 Sep 2020 Chen Zhao, Feng Chen, Zhuoyi Wang, Latifur Khan

In this work, we propose a Primal-Dual Fair Meta-learning framework, namely PDFM, which learns to train fair machine learning models using only a few examples based on data from related tasks.

Fairness Few-Shot Learning

Unfairness Discovery and Prevention For Few-Shot Regression

no code implementations23 Sep 2020 Chen Zhao, Feng Chen

In this work, we first discover discrimination from data using a causal Bayesian knowledge graph which not only demonstrates the dependency of the protected variable on target but also indicates causal effects between all variables.

Fairness Meta-Learning +1

Rank-Based Multi-task Learning for Fair Regression

no code implementations23 Sep 2020 Chen Zhao, Feng Chen

In this work, we develop a novel fairness learning approach for multi-task regression models based on a biased training dataset, using a popular rank-based non-parametric independence test, i. e., Mann Whitney U statistic, for measuring the dependency between target variable and protected variables.

Fairness Multi-Task Learning +1

Fair Meta-Learning For Few-Shot Classification

no code implementations23 Sep 2020 Chen Zhao, Changbin Li, Jincheng Li, Feng Chen

Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems.

BIG-bench Machine Learning Classification +3

Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method

no code implementations8 Aug 2020 Jinlei Zhang, Hongshu Che, Feng Chen, Wei Ma, Zhengbing He

The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.

Benchmarking Management

SiENet: Siamese Expansion Network for Image Extrapolation

1 code implementation8 Jul 2020 Xiaofeng Zhang, Feng Chen, Cailing Wang, Songsong Wu, Ming Tao, Guoping Jiang

In this paper, a novel two-stage siamese adversarial model for image extrapolation, named Siamese Expansion Network (SiENet) is proposed.

Image Inpainting Image Outpainting

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

1 code implementation NeurIPS 2020 Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, Feng Chen

In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a $k$-step adjacent region of the current state using an adjacency constraint.

Continuous Control Hierarchical Reinforcement Learning +2

Brain-inspired global-local learning incorporated with neuromorphic computing

no code implementations5 Jun 2020 Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, Jing Pei, Youhui Zhang, Mingguo Zhao, Luping Shi

We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.

Continual Learning Few-Shot Learning

Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

no code implementations27 Feb 2020 Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu

Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.

Image Classification Natural Language Understanding +1

Multi-Graph Convolutional Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit

no code implementations1 Jan 2020 Jinlei Zhang, Feng Chen, Yinan Guo

Short-term passenger flow forecasting is a crucial task in the operation of urban rail transit.

Physics and Society

Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit

1 code implementation29 Dec 2019 Jinlei Zhang, Feng Chen, Zhiyong Cui, Yinan Guo, Yadi Zhu

Finally, ResLSTM is applied to the Beijing subway using three time granularities (10, 15, and 30 min) to conduct short-term passenger flow forecasting.

Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN

no code implementations12 Nov 2019 Feng Chen, Yunkai Shang, Bo Xu, Jincheng Hu

In comparison with the previous non-learning adversarial example attack approaches, the GAN-based adversarial attack example approach can generate the adversarial samples quickly using the GAN architecture every time facing a new sample after training, but meanwhile needs to perturb the attack samples in great quantities, which results in the unpractical application in reality.

Adversarial Attack BIG-bench Machine Learning

Quantifying Classification Uncertainty using Regularized Evidential Neural Networks

no code implementations15 Oct 2019 Xujiang Zhao, Yuzhe Ou, Lance Kaplan, Feng Chen, Jin-Hee Cho

However, an ENN is trained as a black box without explicitly considering different types of inherent data uncertainty, such as vacuity (uncertainty due to a lack of evidence) or dissonance (uncertainty due to conflicting evidence).

Classification General Classification

Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data

1 code implementation12 Oct 2019 Xujiang Zhao, Feng Chen, Jin-Hee Cho

Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions.

Decision Making

Evidence-Aware Entropy Decomposition For Active Deep Learning

no code implementations25 Sep 2019 Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu

We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data.

Density Estimation

Solving single-objective tasks by preference multi-objective reinforcement learning

no code implementations25 Sep 2019 Jinsheng Ren, Shangqi Guo, Feng Chen

We analyzed the feasibility of our algorithm in theory, and further proved in experiments its better performance compared to those that design the reward function by experts.

Multi-Objective Reinforcement Learning reinforcement-learning

Uncertainty-Aware Prediction for Graph Neural Networks

no code implementations25 Sep 2019 Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho

In this work, we propose a Bayesian deep learning framework reflecting various types of uncertainties for classification predictions by leveraging the powerful modeling and learning capabilities of GNNs.

Classification Node Classification +1

Subjective Reinforcement Learning for Open Complex Environments

no code implementations25 Sep 2019 Zhile Yang*, Haichuan Gao*, Xin Su, Shangqi Guo, Feng Chen

In this paper, Subjective Reinforcement Learning Framework is proposed to state the problem from a broader and systematic view, and subjective policy is proposed to represent existing related algorithms in general.

reinforcement-learning Reinforcement Learning (RL)

Subjectivity Learning Theory towards Artificial General Intelligence

no code implementations9 Sep 2019 Xin Su, Shangqi Guo, Feng Chen

The construction of artificial general intelligence (AGI) was a long-term goal of AI research aiming to deal with the complex data in the real world and make reasonable judgments in various cases like a human.

Learning Theory

Dual Averaging Method for Online Graph-structured Sparsity

1 code implementation26 May 2019 Baojian Zhou, Feng Chen, Yiming Ying

Online learning algorithms update models via one sample per iteration, thus efficient to process large-scale datasets and useful to detect malicious events for social benefits, such as disease outbreak and traffic congestion on the fly.

Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization

1 code implementation9 May 2019 Baojian Zhou, Feng Chen, Yiming Ying

Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis.

Stochastic Optimization

Convolution with even-sized kernels and symmetric padding

1 code implementation NeurIPS 2019 Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi

Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training process.

Continual Learning Image Classification

NeuroTreeNet: A New Method to Explore Horizontal Expansion Network

no code implementations22 Nov 2018 Shenlong Lou, Yan Luo, Qiancong Fan, Feng Chen, Yiping Chen, Cheng Wang, Jonathan Li

It is widely recognized that the deeper networks or networks with more feature maps have better performance.


Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks

no code implementations6 Nov 2018 Qi Yan, Yajing Zheng, Shanshan Jia, Yichen Zhang, Zhaofei Yu, Feng Chen, Yonghong Tian, Tiejun Huang, Jian. K. Liu

When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.

Transfer Learning

Layerwise Recurrent Autoencoder for General Real-world Traffic Flow Forecasting

no code implementations27 Sep 2018 Peize Zhao, Danfeng Cai, Shaokun Zhang, Feng Chen, Zhemin Zhang, Cheng Wang, Jonathan Li

To forecast the traffic flow across a wide area and overcome the mentioned challenges, we design and propose a promising forecasting model called Layerwise Recurrent Autoencoder (LRA), in which a three-layer stacked autoencoder (SAE) architecture is used to obtain temporal traffic correlations and a recurrent neural networks (RNNs) model for prediction.


Abstraction Learning

no code implementations11 Sep 2018 Fei Deng, Jinsheng Ren, Feng Chen

Specifically, we propose a partition structure that contains pre-allocated abstraction neurons; we formulate abstraction learning as a constrained optimization problem, which integrates abstraction properties; we develop a network evolution algorithm to solve this problem.

Water Disaggregation via Shape Features based Bayesian Discriminative Sparse Coding

no code implementations26 Aug 2018 Bingsheng Wang, Xuchao Zhang, Chang-Tien Lu, Feng Chen

As the issue of freshwater shortage is increasing daily, it is critical to take effective measures for water conservation.

A Deep Learning Approach for Privacy Preservation in Assisted Living

no code implementations22 Feb 2018 Ismini Psychoula, Erinc Merdivan, Deepika Singh, Liming Chen, Feng Chen, Sten Hanke, Johannes Kropf, Andreas Holzinger, Matthieu Geist

In the era of Internet of Things (IoT) technologies the potential for privacy invasion is becoming a major concern especially in regards to healthcare data and Ambient Assisted Living (AAL) environments.

Training and Inference with Integers in Deep Neural Networks

2 code implementations ICLR 2018 Shuang Wu, Guoqi Li, Feng Chen, Luping Shi

Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics.

Continual Learning

Revealing structure components of the retina by deep learning networks

no code implementations8 Nov 2017 Qi Yan, Zhaofei Yu, Feng Chen, Jian. K. Liu

By training CNNs with white noise images to predicate neural responses, we found that convolutional filters learned in the end are resembling to biological components of the retinal circuit.

A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks

no code implementations15 Sep 2017 Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao

As a case study, we specialize SG-Pursuit to optimize a number of well-known score functions for two typical tasks, including detection of coherent dense and anomalous connected subspace clusters in real-world networks.

feature selection

Experimental comparison of single-pixel imaging algorithms

no code implementations11 Jul 2017 Liheng Bian, Jinli Suo, Qionghai Dai, Feng Chen

Various algorithms have been proposed for SPI reconstruction, including the linear correlation methods, the alternating projection method (AP), and the compressive sensing based methods.

Compressive Sensing

SAFETY: Secure gwAs in Federated Environment Through a hYbrid solution with Intel SGX and Homomorphic Encryption

1 code implementation7 Mar 2017 Md Nazmus Sadat, Md Momin Al Aziz, Noman Mohammed, Feng Chen, Shuang Wang, Xiaoqian Jiang

In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure high efficiency and privacy at the same time.

Cryptography and Security

Technical Report: A Generalized Matching Pursuit Approach for Graph-Structured Sparsity

no code implementations11 Dec 2016 Feng Chen, Baojian Zhou

Sparsity-constrained optimization is an important and challenging problem that has wide applicability in data mining, machine learning, and statistics.

Technical Report: Graph-Structured Sparse Optimization for Connected Subgraph Detection

no code implementations30 Sep 2016 Baojian Zhou, Feng Chen

Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, social networks, and astronomy.


CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling

no code implementations1 Jun 2016 Zhaofei Yu, David Kappel, Robert Legenstein, Sen Song, Feng Chen, Wolfgang Maass

Our theoretical analysis shows that stochastic search could in principle even attain optimal network configurations by emulating one of the most well-known nonlinear optimization methods, simulated annealing.

Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient

no code implementations1 Mar 2016 Liheng Bian, Jinli Suo, Jaebum Chung, Xiaoze Ou, Changhuei Yang, Feng Chen, Qionghai Dai

Fourier ptychographic microscopy (FPM) is a novel computational coherent imaging technique for high space-bandwidth product imaging.


Sampling-based Causal Inference in Cue Combination and its Neural Implementation

no code implementations3 Sep 2015 Zhaofei Yu, Feng Chen, Jianwu Dong, Qionghai Dai

Although the Bayesian causal inference model explains the problem of causal inference in cue combination successfully, how causal inference in cue combination could be implemented by neural circuits, is unclear.

Causal Inference

Multi-frame denoising of high speed optical coherence tomography data using inter-frame and intra-frame priors

no code implementations6 Dec 2013 Liheng Bian, Jinli Suo, Feng Chen, Qionghai Dai

Optical coherence tomography (OCT) is an important interferometric diagnostic technique which provides cross-sectional views of the subsurface microstructure of biological tissues.


Variational Planning for Graph-based MDPs

no code implementations NeurIPS 2013 Qiang Cheng, Qiang Liu, Feng Chen, Alexander T. Ihler

The KL divergence is optimized using the belief propagation algorithm, with complexity exponential in only the cluster size of the graph.

Decision Making

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