Search Results for author: Chong Wang

Found 95 papers, 32 papers with code

Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features

no code implementations24 May 2024 Lichuan Ji, Yingqi Lin, Zhenhua Huang, Yan Han, Xiaogang Xu, Jiafei Wu, Chong Wang, Zhe Liu

Current datasets lack a varied and comprehensive repository of real and generated content for effective discrimination.

Semantic-guided Prompt Organization for Universal Goal Hijacking against LLMs

no code implementations23 May 2024 Yihao Huang, Chong Wang, Xiaojun Jia, Qing Guo, Felix Juefei-Xu, Jian Zhang, Geguang Pu, Yang Liu

With the rising popularity of Large Language Models (LLMs), assessing their trustworthiness through security tasks has gained critical importance.

Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI

1 code implementation15 Mar 2024 Chong Wang, Lanqing Guo, YuFei Wang, Hao Cheng, Yi Yu, Bihan Wen

Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI, we present a rigorous derivation of the proposed PDAC framework, which could be further unfolded into an end-to-end trainable network.

MRI Reconstruction

Recurrent Drafter for Fast Speculative Decoding in Large Language Models

no code implementations14 Mar 2024 Aonan Zhang, Chong Wang, Yi Wang, Xuanyu Zhang, Yunfei Cheng

In this paper, we introduce an improved approach of speculative decoding aimed at enhancing the efficiency of serving large language models.

Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection

no code implementations2 Mar 2024 Chenchen Tao, Chong Wang, Yuexian Zou, Xiaohao Peng, Jiafei Wu, Jiangbo Qian

Most models for weakly supervised video anomaly detection (WS-VAD) rely on multiple instance learning, aiming to distinguish normal and abnormal snippets without specifying the type of anomaly.

Anomaly Detection Multiple Instance Learning +1

Security Code Review by LLMs: A Deep Dive into Responses

no code implementations29 Jan 2024 Jiaxin Yu, Peng Liang, Yujia Fu, Amjed Tahir, Mojtaba Shahin, Chong Wang, Yangxiao Cai

To explore the challenges of applying LLMs in practical code review for security defect detection, this study compared the detection performance of three state-of-the-art LLMs (Gemini Pro, GPT-4, and GPT-3. 5) under five prompts on 549 code files that contain security defects from real-world code reviews.

Defect Detection

Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable Image Classification

no code implementations30 Nov 2023 Chong Wang, Yuanhong Chen, Fengbei Liu, Davis James McCarthy, Helen Frazer, Gustavo Carneiro

Such an approach enables the learning of more powerful prototype representations since each learned prototype will own a measure of variability, which naturally reduces the sparsity given the spread of the distribution around each prototype, and we also integrate a prototype diversity objective function into the GMM optimisation to reduce redundancy.

Decision Making Image Classification

Wildfire Smoke Detection with Cross Contrast Patch Embedding

1 code implementation16 Nov 2023 Chong Wang, Cheng Xu, Adeel Akram, Zhilin Shan, Qixing Zhang

By using two different negative instance sampling strategies on positive images and negative images respectively, the problem of supervision signal confusion caused by label diversity in the process of network training is alleviated.

Spec-NeRF: Multi-spectral Neural Radiance Fields

1 code implementation14 Sep 2023 Jiabao Li, Yuqi Li, Ciliang Sun, Chong Wang, Jinhui Xiang

We propose Multi-spectral Neural Radiance Fields(Spec-NeRF) for jointly reconstructing a multispectral radiance field and spectral sensitivity functions(SSFs) of the camera from a set of color images filtered by different filters.

Spectral Reconstruction

Partial Label Supervision for Agnostic Generative Noisy Label Learning

1 code implementation2 Aug 2023 Fengbei Liu, Chong Wang, Yuanhong Chen, Yuyuan Liu, Gustavo Carneiro

Second, we introduce a new Partial Label Supervision (PLS) for noisy label learning that accounts for both clean label coverage and uncertainty.

Image Generation Learning with noisy labels +1

Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations

no code implementations13 Feb 2023 Mimee Xu, Jiankai Sun, Xin Yang, Kevin Yao, Chong Wang

Without incurring the cost of re-training, and without degrading the model unnecessarily, we develop Unlearn-ALS by making a few key modifications to the fine-tuning procedure under Alternating Least Squares optimisation, thus applicable to any bi-linear models regardless of the training procedure.

Machine Unlearning Matrix Completion

Efficient Attention via Control Variates

1 code implementation9 Feb 2023 Lin Zheng, Jianbo Yuan, Chong Wang, Lingpeng Kong

Built upon previous progress of RFA, we characterize this gap through the lens of control variates and show that RFA can be decomposed into a sum of multiple control variate estimators for each element in the sequence.

BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete Annotations

no code implementations31 Jan 2023 Yuanhong Chen, Yuyuan Liu, Chong Wang, Michael Elliott, Chun Fung Kwok, Carlos Pena-Solorzano, Yu Tian, Fengbei Liu, Helen Frazer, Davis J. McCarthy, Gustavo Carneiro

Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it.

Lesion Detection

Learning Support and Trivial Prototypes for Interpretable Image Classification

1 code implementation ICCV 2023 Chong Wang, Yuyuan Liu, Yuanhong Chen, Fengbei Liu, Yu Tian, Davis J. McCarthy, Helen Frazer, Gustavo Carneiro

Prototypical part network (ProtoPNet) methods have been designed to achieve interpretable classification by associating predictions with a set of training prototypes, which we refer to as trivial prototypes because they are trained to lie far from the classification boundary in the feature space.

Explainable Artificial Intelligence (XAI) Image Classification +1

Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning

no code implementations1 Jan 2023 Fengbei Liu, Yuanhong Chen, Chong Wang, Yu Tain, Gustavo Carneiro

Also, the new sample selection is based on multi-view consensus, which uses the label views from training labels and model predictions to divide the training set into clean and noisy for training the multi-class model and to re-label the training samples with multiple top-ranked labels for training the multi-label model.

Learning with noisy labels Multi-Label Learning

Supervised Pretraining for Molecular Force Fields and Properties Prediction

no code implementations23 Nov 2022 Xiang Gao, Weihao Gao, Wenzhi Xiao, Zhirui Wang, Chong Wang, Liang Xiang

Experiments show that, compared to training from scratch, fine-tuning the pretrained model can significantly improve the performance for seven molecular property prediction tasks and two force field tasks.

Molecular Property Prediction Property Prediction

Learning Regularized Positional Encoding for Molecular Prediction

no code implementations23 Nov 2022 Xiang Gao, Weihao Gao, Wenzhi Xiao, Zhirui Wang, Chong Wang, Liang Xiang

To model the complex nonlinearity in predicting molecular properties in an more end-to-end approach, we propose to encode the positional quantities with a learnable embedding that is continuous and differentiable.

Learning to Counterfactually Explain Recommendations

no code implementations17 Nov 2022 Yuanshun Yao, Chong Wang, Hang Li

The key idea is to train a surrogate model to learn the effect of removing a subset of user history on the recommendation.

counterfactual Recommendation Systems +1

Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models

no code implementations26 Sep 2022 Chong Wang, Yuanhong Chen, Yuyuan Liu, Yu Tian, Fengbei Liu, Davis J. McCarthy, Michael Elliott, Helen Frazer, Gustavo Carneiro

On the other hand, prototype-based models improve interpretability by associating predictions with training image prototypes, but they are less accurate than global models and their prototypes tend to have poor diversity.

Knowledge Distillation

Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation

1 code implementation21 Sep 2022 Yuanhong Chen, Hu Wang, Chong Wang, Yu Tian, Fengbei Liu, Michael Elliott, Davis J. McCarthy, Helen Frazer, Gustavo Carneiro

When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-oblique (MLO) views.

Collaborative Anomaly Detection

no code implementations20 Sep 2022 Ke Bai, Aonan Zhang, Zhizhong Li, Ricardo Heano, Chong Wang, Lawrence Carin

In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item.

Anomaly Detection Density Estimation +1

DPAUC: Differentially Private AUC Computation in Federated Learning

1 code implementation25 Aug 2022 Jiankai Sun, Xin Yang, Yuanshun Yao, Junyuan Xie, Di wu, Chong Wang

Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants.

Federated Learning

REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration

no code implementations25 Jul 2022 Chong Wang, Rongkai Zhang, Saiprasad Ravishankar, Bihan Wen

To this end, we propose a novel deep reinforcement learning (DRL) based PnP framework, dubbed RePNP, by leveraging a light-weight DRL-based denoiser for robust image restoration tasks.

Deblurring Image Deblurring +4

Differentially Private Multi-Party Data Release for Linear Regression

no code implementations16 Jun 2022 Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q. Weinberger, Chong Wang

Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects.

regression

Differentially Private AUC Computation in Vertical Federated Learning

no code implementations24 May 2022 Jiankai Sun, Xin Yang, Yuanshun Yao, Junyuan Xie, Di wu, Chong Wang

In this work, we propose two evaluation algorithms that can more accurately compute the widely used AUC (area under curve) metric when using label DP in vFL.

Vertical Federated Learning

Linear Complexity Randomized Self-attention Mechanism

1 code implementation10 Apr 2022 Lin Zheng, Chong Wang, Lingpeng Kong

By combining the expressiveness in RA and the efficiency in RFA, we develop a novel linear complexity self-attention mechanism called linear randomized attention (LARA).

Translation Consistent Semi-supervised Segmentation for 3D Medical Images

1 code implementation28 Mar 2022 Yuyuan Liu, Yu Tian, Chong Wang, Yuanhong Chen, Fengbei Liu, Vasileios Belagiannis, Gustavo Carneiro

The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data.

Brain Tumor Segmentation Image Segmentation +5

Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

1 code implementation23 Mar 2022 Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W Verjans, Gustavo Carneiro

Current polyp detection methods from colonoscopy videos use exclusively normal (i. e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps.

Multiple Instance Learning Supervised Anomaly Detection +1

Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

1 code implementation22 Mar 2022 Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W Verjans, Mengyu Wang, Gustavo Carneiro

Our UAD approach, the memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder.

Image Reconstruction Unsupervised Anomaly Detection

Differentially Private Label Protection in Split Learning

no code implementations4 Mar 2022 Xin Yang, Jiankai Sun, Yuanshun Yao, Junyuan Xie, Chong Wang

Split learning is a distributed training framework that allows multiple parties to jointly train a machine learning model over vertically partitioned data (partitioned by attributes).

BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification

2 code implementations ICCV 2023 Yuanhong Chen, Fengbei Liu, Hu Wang, Chong Wang, Yu Tian, Yuyuan Liu, Gustavo Carneiro

Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels.

Multi-Label Classification

Label Leakage and Protection from Forward Embedding in Vertical Federated Learning

no code implementations2 Mar 2022 Jiankai Sun, Xin Yang, Yuanshun Yao, Chong Wang

As the raw labels often contain highly sensitive information, some recent work has been proposed to prevent the label leakage from the backpropagated gradients effectively in vFL.

Vertical Federated Learning

Learning to Simulate Unseen Physical Systems with Graph Neural Networks

no code implementations NeurIPS Workshop AI4Scien 2021 Ce Yang, Weihao Gao, Di wu, Chong Wang

Simulation of the dynamics of physical systems is essential to the development of both science and engineering.

Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks

no code implementations NeurIPS Workshop AI4Scien 2021 Tianze Zheng, Weihao Gao, Chong Wang

Molecular dynamics (MD) simulation predicts the trajectory of atoms by solving Newton's equation of motion with a numeric integrator.

Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data

no code implementations NeurIPS 2021 Haiying Wang, Aonan Zhang, Chong Wang

We first prove that, with imbalanced data, the available information about unknown parameters is only tied to the relatively small number of positive instances, which justifies the usage of negative sampling.

Heterogeneous relational message passing networks for molecular dynamics simulations

no code implementations2 Sep 2021 Zun Wang, Chong Wang, Sibo Zhao, Yong Xu, Shaogang Hao, Chang Yu Hsieh, Bing-Lin Gu, Wenhui Duan

With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of computational sciences underpinning physics, material science, chemistry, and biology.

BIG-bench Machine Learning

Dynamic Relevance Learning for Few-Shot Object Detection

1 code implementation4 Aug 2021 Weijie Liu, Chong Wang, Haohe Li, Shenghao Yu, Jiafei Wu

By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly.

Few-Shot Object Detection Meta-Learning +2

Defending against Reconstruction Attack in Vertical Federated Learning

no code implementations21 Jul 2021 Jiankai Sun, Yuanshun Yao, Weihao Gao, Junyuan Xie, Chong Wang

Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared gradient.

Privacy Preserving Reconstruction Attack +1

AutoLoss: Automated Loss Function Search in Recommendations

no code implementations12 Jun 2021 Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang

Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.

Recommendation Systems

Vertical Federated Learning without Revealing Intersection Membership

no code implementations10 Jun 2021 Jiankai Sun, Xin Yang, Yuanshun Yao, Aonan Zhang, Weihao Gao, Junyuan Xie, Chong Wang

In this paper, we propose a vFL framework based on Private Set Union (PSU) that allows each party to keep sensitive membership information to itself.

Vertical Federated Learning

Efficient state and parameter estimation for high-dimensional nonlinear system identification with application to MEG brain network modeling

no code implementations6 Apr 2021 Matthew F. Singh, Chong Wang, Michael W. Cole, ShiNung Ching

Intuitively, our approach consists of solving for the parameters that generate the most accurate state estimator (Extended Kalman Filter).

NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification

1 code implementation6 Mar 2021 Fengbei Liu, Yuanhong Chen, Yu Tian, Yuyuan Liu, Chong Wang, Vasileios Belagiannis, Gustavo Carneiro

In this paper, we propose a new training module called Non-Volatile Unbiased Memory (NVUM), which non-volatility stores running average of model logits for a new regularization loss on noisy multi-label problem.

Image Classification with Label Noise Learning with noisy labels +1

Abrupt switching of the anomalous Hall effect by field-rotation in nonmagnetic ZrTe5

no code implementations7 Jan 2021 Joshua Mutch, Xuetao Ma, Chong Wang, Paul Malinowski, Joss Ayres-Sims, Qianni Jiang, Zhaoyu Liu, Di Xiao, Matthew Yankowitz, Jiun-Haw Chu

The angular dependence of the Hall resistivity approaches a signum function, persisting down to an extremely low field of 0. 03 T. By varying the carrier density of ZrTe5 over three orders of magnitude, we show that this singular behavior is due to the anomalous Hall effect generated by the ultra-dilute massive Dirac carriers in the quantum limit of Pauli paramagnetism when the Zeeman energy exceeds the Fermi energy.

Mesoscale and Nanoscale Physics

Deep Retrieval: An End-to-End Structure Model for Large-Scale Recommendations

1 code implementation1 Jan 2021 Weihao Gao, Xiangjun Fan, Jiankai Sun, Kai Jia, Wenzhi Xiao, Chong Wang, Xiaobing Liu

With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates.

Retrieval

Theory of Dirac spin liquids on spin-$S$ triangular lattice: possible application to $α$-CrOOH(D)

no code implementations17 Dec 2020 Vladimir Calvera, Chong Wang

We argue that in the most natural scenario, a spin-$S$ system realizes a $U(2S)$ DSL, described at low energy by gapless Dirac fermions coupled with an emergent $U(2S)$ gauge field (also known as $U(2S)$ QCD$_3$).

Strongly Correlated Electrons

Towards Earnings Call and Stock Price Movement

no code implementations23 Aug 2020 Zhiqiang Ma, Grace Bang, Chong Wang, Xiaomo Liu

Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors.

Management Stock Price Prediction

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations

no code implementations26 Feb 2020 Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, Jiliang Tang

Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.

AutoML Recommendation Systems

Learning to Structure Long-term Dependence for Sequential Recommendation

no code implementations30 Jan 2020 Renqin Cai, Qinglei Wang, Chong Wang, Xiaobing Liu

To better model the long-term dependence structure, we propose a GatedLongRec solution in this work.

Sequential Recommendation

Short-Term Temporal Convolutional Networks for Dynamic Hand Gesture Recognition

no code implementations31 Dec 2019 Yi Zhang, Chong Wang, Ye Zheng, Jieyu Zhao, Yuqi Li, Xijiong Xie

Subsequently, in temporal analysis, we use TCNs to extract temporal features and employ improved Squeeze-and-Excitation Networks (SENets) to strengthen the representational power of temporal features from each TCNs' layers.

Hand Gesture Recognition Hand-Gesture Recognition

Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing

1 code implementation ACL 2020 Haoming Jiang, Chen Liang, Chong Wang, Tuo Zhao

To overcome this limitation, we propose a novel multi-domain NMT model using individual modules for each domain, on which we apply word-level, adaptive and layer-wise domain mixing.

Machine Translation NMT +3

Feature Partitioning for Efficient Multi-Task Architectures

no code implementations ICLR 2020 Alejandro Newell, Lu Jiang, Chong Wang, Li-Jia Li, Jia Deng

Multi-task learning holds the promise of less data, parameters, and time than training of separate models.

Multi-Task Learning

Neural Logic Machines

2 code implementations ICLR 2019 Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou

We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning.

Decision Making Inductive logic programming +1

Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference

no code implementations ICLR 2019 Shun Liao, Ting Chen, Tian Lin, Denny Zhou, Chong Wang

In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes.

Image Classification Language Modelling +2

Neural Phrase-to-Phrase Machine Translation

no code implementations6 Nov 2018 Jiangtao Feng, Lingpeng Kong, Po-Sen Huang, Chong Wang, Da Huang, Jiayuan Mao, Kan Qiao, Dengyong Zhou

We also design an efficient dynamic programming algorithm to decode segments that allows the model to be trained faster than the existing neural phrase-based machine translation method by Huang et al. (2018).

Decoder Machine Translation +1

Fully Supervised Speaker Diarization

1 code implementation10 Oct 2018 Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang

In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN).

Clustering speaker-diarization +1

Rate Distortion For Model Compression: From Theory To Practice

no code implementations9 Oct 2018 Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh

Theoretically, we prove that the proposed scheme is optimal for compressing one-hidden-layer ReLU neural networks.

Data Compression Model Compression +1

Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling

no code implementations NIPS Workshop CDNNRIA 2018 Ting Chen, Ji Lin, Tian Lin, Song Han, Chong Wang, Denny Zhou

Modern deep neural networks have a large amount of weights, which make them difficult to deploy on computation constrained devices such as mobile phones.

Image Classification Language Modelling

Subgoal Discovery for Hierarchical Dialogue Policy Learning

no code implementations EMNLP 2018 Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara

Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals.

Hierarchical Reinforcement Learning

Attention-based Graph Neural Network for Semi-supervised Learning

1 code implementation ICLR 2018 Kiran K. Thekumparampil, Chong Wang, Sewoong Oh, Li-Jia Li

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches.

Graph Regression

Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes

no code implementations NeurIPS 2017 Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng

In sequential decision making, it is often important and useful for end users to understand the underlying patterns or causes that lead to the corresponding decisions.

Decision Making Q-Learning +2

Thoracic Disease Identification and Localization with Limited Supervision

1 code implementation CVPR 2018 Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, Li Fei-Fei

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.

General Classification

Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification

no code implementations ICLR 2018 Chong Wang, Xipeng Lan, Yangang Zhang

The idea is to make a small student network imitate the target of a large teacher network, then the student network can be competitive to the teacher one.

Classification Face Recognition +5

How to Train Triplet Networks with 100K Identities?

no code implementations9 Sep 2017 Chong Wang, Xue Zhang, Xipeng Lan

However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found.

Face Recognition Image Retrieval +1

Scaffolding Networks: Incremental Learning and Teaching Through Questioning

no code implementations28 Feb 2017 Asli Celikyilmaz, Li Deng, Lihong Li, Chong Wang

We introduce a new paradigm of learning for reasoning, understanding, and prediction, as well as the scaffolding network to implement this paradigm.

Incremental Learning Sentence

Sequence Modeling via Segmentations

2 code implementations ICML 2017 Chong Wang, Yining Wang, Po-Sen Huang, Abdel-rahman Mohamed, Dengyong Zhou, Li Deng

The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks.

Segmentation speech-recognition +3

TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency

1 code implementation5 Nov 2016 Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley

The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics.

Language Modelling Sentiment Analysis +1

Scalable Modeling of Conversational-role based Self-presentation Characteristics in Large Online Forums

no code implementations10 Dec 2015 Abhimanu Kumar, Shriphani Palakodety, Chong Wang, Carolyn P. Rose, Eric P. Xing, Miaomiao Wen

Online discussion forums are complex webs of overlapping subcommunities (macrolevel structure, across threads) in which users enact different roles depending on which subcommunity they are participating in within a particular time point (microlevel structure, within threads).

Topic Models Variational Inference

A General Method for Robust Bayesian Modeling

no code implementations17 Oct 2015 Chong Wang, David M. Blei

Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions.

regression Topic Models

Embarrassingly Parallel Variational Inference in Nonconjugate Models

no code implementations14 Oct 2015 Willie Neiswanger, Chong Wang, Eric Xing

We develop a parallel variational inference (VI) procedure for use in data-distributed settings, where each machine only has access to a subset of data and runs VI independently, without communicating with other machines.

Variational Inference

Dynamic Language Models for Streaming Text

no code implementations TACL 2014 Dani Yogatama, Chong Wang, Bryan R. Routledge, Noah A. Smith, Eric P. Xing

We present a probabilistic language model that captures temporal dynamics and conditions on arbitrary non-linguistic context features.

Language Modelling Machine Translation +1

Modeling Overlapping Communities with Node Popularities

no code implementations NeurIPS 2013 Prem K. Gopalan, Chong Wang, David Blei

We evaluate the link prediction accuracy of our algorithm on eight real-world networks with up to 60, 000 nodes, and 24 benchmark networks.

Link Prediction Variational Inference

Variance Reduction for Stochastic Gradient Optimization

no code implementations NeurIPS 2013 Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing

We demonstrate how to construct the control variate for two practical problems using stochastic gradient optimization.

Variational Inference

Asymptotically Exact, Embarrassingly Parallel MCMC

no code implementations19 Nov 2013 Willie Neiswanger, Chong Wang, Eric Xing

This embarrassingly parallel algorithm allows each machine to act independently on a subset of the data (without communication) until the final combination stage.

Nested Hierarchical Dirichlet Processes

no code implementations25 Oct 2012 John Paisley, Chong Wang, David M. Blei, Michael. I. Jordan

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling.

Variational Inference

Stochastic Variational Inference

2 code implementations29 Jun 2012 Matt Hoffman, David M. Blei, Chong Wang, John Paisley

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions.

Topic Models Variational Inference

Continuous Time Dynamic Topic Models

no code implementations13 Jun 2012 Chong Wang, David Blei, David Heckerman

In contrast to the cDTM, the original discrete-time dynamic topic model (dDTM) requires that time be discretized.

Topic Models Variational Inference

Fast Large-scale Mixture Modeling with Component-specific Data Partitions

no code implementations NeurIPS 2010 Bo Thiesson, Chong Wang

Remarkably easy implementation and guaranteed convergence has made the EM algorithm one of the most used algorithms for mixture modeling.

Variational Inference for the Nested Chinese Restaurant Process

no code implementations NeurIPS 2009 Chong Wang, David M. Blei

The nested Chinese restaurant process (nCRP) is a powerful nonparametric Bayesian model for learning tree-based hierarchies from data.

Variational Inference

Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process

no code implementations NeurIPS 2009 Chong Wang, David M. Blei

We present a nonparametric hierarchical Bayesian model of document collections that decouples sparsity and smoothness in the component distributions (i. e., the ``topics).

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