Search Results for author: Bo Chen

Found 94 papers, 41 papers with code

BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging

1 code implementation ECCV 2020 Ziheng Cheng, Ruiying Lu, Zhengjue Wang, Hao Zhang, Bo Chen, Ziyi Meng, Xin Yuan

This measurement and the modulation masks are fed into our Recurrent Neural Network (RNN) to reconstruct the desired high-speed frames.

Friendly Topic Assistant for Transformer Based Abstractive Summarization

no code implementations EMNLP 2020 Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen, Mingyuan Zhou

Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance.

Abstractive Text Summarization Document Summarization +1

Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation

no code implementations11 Aug 2022 Yuxiang Shi, Yue Ding, Bo Chen, YuYang Huang, Ruiming Tang, Dong Wang

In this paper, we propose a Task aligned Meta-learning based Augmented Graph (TMAG) to address cold-start recommendation.

Meta-Learning Recommendation Systems

Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints

no code implementations3 Aug 2022 Rusheng Wang, Bo Chen, Zhongyao Hu, Li Yu

This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical characteristics.

Dimensionality Reduction

Distributed Estimation for Interconnected Systems with Arbitrary Coupling Structures

no code implementations1 Jun 2022 Yuchen Zhang, Bo Chen, Li Yu, Daniel W. C. Ho

By merging these subsystem-level stability conditions and the optimization-based estimator gain design, the distributed, stable and optimal estimators are proposed.

Automated Machine Learning for Deep Recommender Systems: A Survey

no code implementations4 Apr 2022 Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, Ruiming Tang

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences.

AutoML BIG-bench Machine Learning +2

Update Compression for Deep Neural Networks on the Edge

no code implementations9 Mar 2022 Bo Chen, Ali Bakhshi, Gustavo Batista, Brian Ng, Tat-Jun Chin

In this paper, we consider the scenario where retraining can be done on the server side based on a copy of the DNN model, with only the necessary data transmitted to the edge to update the deployed model.

Federated Learning

Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings

1 code implementation ICLR 2022 Dongsheng Wang, Dandan Guo, He Zhao, Huangjie Zheng, Korawat Tanwisuth, Bo Chen, Mingyuan Zhou

This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space.

Word Embeddings

Asynchronous Optimisation for Event-based Visual Odometry

no code implementations2 Mar 2022 Daqi Liu, Alvaro Parra, Yasir Latif, Bo Chen, Tat-Jun Chin, Ian Reid

Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range.

Event-based vision Visual Odometry

Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing

no code implementations1 Mar 2022 Ruiying Lu, Ziheng Cheng, Bo Chen, Xin Yuan

Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture sequential video frames and compresses them into a single measurement.

Compressive Sensing Video Compressive Sensing

Neural Re-ranking in Multi-stage Recommender Systems: A Review

1 code implementation14 Feb 2022 Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, Ruiming Tang

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications.

Recommendation Systems Re-Ranking

Binary Neural Networks as a general-propose compute paradigm for on-device computer vision

no code implementations8 Feb 2022 Guhong Nie, Lirui Xiao, Menglong Zhu, Dongliang Chu, Yue Shen, Peng Li, Kang Yang, Li Du, Bo Chen

For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and establish a similar degree of general applicability in vision tasks.

Quantization Super-Resolution

A Variational Edge Partition Model for Supervised Graph Representation Learning

no code implementations7 Feb 2022 Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, Mingyuan Zhou

This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities, each of which contributes to the edges via a logical OR mechanism.

Classification Graph Representation Learning +1

Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector

no code implementations3 Dec 2021 Andrew Du, Yee Wei Law, Michele Sasdelli, Bo Chen, Ken Clarke, Michael Brown, Tat-Jun Chin

In fact, advanced EO satellites perform deep learning-based cloud detection on board the satellites and downlink only clear-sky data to save precious bandwidth.

Cloud Detection

MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction

no code implementations30 Nov 2021 Wei Guo, Can Zhang, ZhiCheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Ruiming Tang, Xiuqiang He, Rui Zhang

With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item).

Click-Through Rate Prediction Contrastive Learning +3

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

1 code implementation5 Nov 2021 Chenxu Zhu, Bo Chen, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

To address these three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), to select significant feature interactions, appropriate interaction functions and necessary embedding dimensions automatically in a unified framework.

Click-Through Rate Prediction

TopicNet: Semantic Graph-Guided Topic Discovery

1 code implementation NeurIPS 2021 Zhibin Duan, Yishi Xu, Bo Chen, Dongsheng Wang, Chaojie Wang, Mingyuan Zhou

Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic hierarchy.

Inductive Bias Topic Models +1

Kalman-Like Filter under Binary Sensors

no code implementations27 Oct 2021 Zhongyao Hu, Bo Chen, Yuchen Zhang, Li Yu

When considering linear dynamic systems, a conservative estimation error covariance with adjustable parameters is constructed by matrix inequality, and then an optimal filter gain is derived by minimizing its trace.

A Prototype-Oriented Framework for Unsupervised Domain Adaptation

1 code implementation NeurIPS 2021 Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, Mingyuan Zhou

Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space.

Unsupervised Domain Adaptation

Occlusion-Robust Object Pose Estimation with Holistic Representation

1 code implementation22 Oct 2021 Bo Chen, Tat-Jun Chin, Marius Klimavicius

State-of-the-art (SOTA) object pose estimators take a two-stage approach, where the first stage predicts 2D landmarks using a deep network and the second stage solves for 6DOF pose from 2D-3D correspondences.

6D Pose Estimation using RGB Representation Learning

Enhanced Sequential Covariance Intersection Fusion

no code implementations13 Oct 2021 Zhongyao Hu, Bo Chen, Wen-An Zhang, Li Yu

For this criterion, it is proved that the fusion results are not affected by the fusion structure, and thus the fusion performance can be guaranteed.

Crossformer: Transformer with Alternated Cross-Layer Guidance

no code implementations29 Sep 2021 Shujian Zhang, Zhibin Duan, Huangjie Zheng, Pengcheng He, Bo Chen, Weizhu Chen, Mingyuan Zhou

Crossformer with states sharing not only provides the desired cross-layer guidance and regularization but also reduces the memory requirement.

Inductive Bias Machine Translation +3

Edge Partition Modulated Graph Convolutional Networks

no code implementations29 Sep 2021 Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, Mingyuan Zhou

In this paper, we introduce a relational graph generative process to model how the observed edges are generated by aggregating the node interactions over multiple overlapping node communities, each of which represents a particular type of relation that contributes to the edges via a logical OR mechanism.

Variational Inference

Extracting Attentive Social Temporal Excitation for Sequential Recommendation

no code implementations28 Sep 2021 Yunzhe Li, Yue Ding, Bo Chen, Xin Xin, Yule Wang, Yuxiang Shi, Ruiming Tang, Dong Wang

In this paper, we propose a novel time-aware sequential recommendation framework called Social Temporal Excitation Networks (STEN), which introduces temporal point processes to model the fine-grained impact of friends' behaviors on the user s dynamic interests in an event-level direct paradigm.

Collaborative Filtering Graph Embedding +2

Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network

1 code implementation11 Sep 2021 Ruiying Lu, Bo Chen, Guanliang Liu, Ziheng Cheng, Mu Qiao, Xin Yuan

In this paper, we propose an optical flow-aided recurrent neural network for dual video SCI systems, which provides high-quality decoding in seconds.

Compressive Sensing Optical Flow Estimation

Physical Adversarial Attacks on an Aerial Imagery Object Detector

1 code implementation26 Aug 2021 Andrew Du, Bo Chen, Tat-Jun Chin, Yee Wei Law, Michele Sasdelli, Ramesh Rajasegaran, Dillon Campbell

In this work, we demonstrate one of the first efforts on physical adversarial attacks on aerial imagery, whereby adversarial patches were optimised, fabricated and installed on or near target objects (cars) to significantly reduce the efficacy of an object detector applied on overhead images.

GCCAD: Graph Contrastive Coding for Anomaly Detection

1 code implementation17 Aug 2021 Bo Chen, Jing Zhang, Xiaokang Zhang, Yuxiao Dong, Jian Song, Peng Zhang, Kaibo Xu, Evgeny Kharlamov, Jie Tang

To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.

Anomaly Detection Feature Engineering

Practical Challenges in Real-time Demand Response

no code implementations10 Aug 2021 Chao Duan, Guna Bharati, Pratyush Chakraborty, Bo Chen, Takashi Nishikawa, Adilson E. Motter

We report on a real-time demand response experiment with 100 controllable devices.

EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering

1 code implementation ACL 2021 Zhibin Duan, Hao Zhang, Chaojie Wang, Zhengjue Wang, Bo Chen, Mingyuan Zhou

As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features.

Language Modelling Natural Language Processing

Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network

1 code implementation30 Jun 2021 Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren, Mingyuan Zhou

However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers.

Topic Models Variational Inference

A Spacecraft Dataset for Detection, Segmentation and Parts Recognition

no code implementations15 Jun 2021 Dung Anh Hoang, Bo Chen, Tat-Jun Chin

We also provide evaluations with state-of-the-art methods in object detection and instance segmentation as a benchmark for the dataset.

Instance Segmentation object-detection +2

From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding

no code implementations ACL 2021 Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai

During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly.

Unsupervised semantic parsing

Bayesian Attention Belief Networks

no code implementations9 Jun 2021 Shujian Zhang, Xinjie Fan, Bo Chen, Mingyuan Zhou

Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks.

Machine Translation Question Answering +2

Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning

1 code implementation10 May 2021 Dandan Guo, Ruiying Lu, Bo Chen, Zequn Zeng, Mingyuan Zhou

Inspired by recent successes in integrating semantic topics into this task, this paper develops a plug-and-play hierarchical-topic-guided image paragraph generation framework, which couples a visual extractor with a deep topic model to guide the learning of a language model.

Image Paragraph Captioning Language Modelling +1

Wide-Beam Array Antenna Power Gain Maximization via ADMM Framework

no code implementations21 Apr 2021 Shiwen Lei, Jing Tian, Zhipeng Lin, Haoquan Hu, Bo Chen, Wei Yang, Pu Tang, Xiangdong Qiu

This paper proposes two algorithms to maximize the minimum array power gain in a wide-beam mainlobe by solving the power gain pattern synthesis (PGPS) problem with and without sidelobe constraints.

Memory-Efficient Network for Large-scale Video Compressive Sensing

2 code implementations CVPR 2021 Ziheng Cheng, Bo Chen, Guanliang Liu, Hao Zhang, Ruiying Lu, Zhengjue Wang, Xin Yuan

With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.

Compressive Sensing Demosaicking +1

MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing

2 code implementations CVPR 2021 Zhengjue Wang, Hao Zhang, Ziheng Cheng, Bo Chen, Xin Yuan

To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement.

Compressive Sensing Video Compressive Sensing

Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners

no code implementations18 Feb 2021 Parham Gohari, Bo Chen, Bo Wu, Matthew Hale, Ufuk Topcu

We then develop a kickstarted deep reinforcement learning algorithm for the student that is privacy-aware because we calibrate its objective with the parameters of the teacher's privacy mechanism.

Privacy Preserving reinforcement-learning

Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation

no code implementations4 Jan 2021 Ning Bian, Xianpei Han, Bo Chen, Le Sun

Experiments show that: (1) Our knowledge-to-text framework is effective and achieves state-of-the-art performance on CommonsenseQA dataset, providing a simple and strong knowledge-enhanced baseline for CQA; (2) The potential of knowledge is still far from being fully exploited in CQA -- there is a significant performance gap from current models to our models with golden knowledge; and (3) Context-sensitive knowledge selection, heterogeneous knowledge exploitation, and commonsense-rich language models are promising CQA directions.

Question Answering

Topic-aware Contextualized Transformers

no code implementations1 Jan 2021 Ruiying Lu, Bo Chen, Dan dan Guo, Dongsheng Wang, Mingyuan Zhou

Moving beyond conventional Transformers that ignore longer-range word dependencies and contextualize their word representations at the segment level, the proposed method not only captures global semantic coherence of all segments and global word concurrence patterns, but also enriches the representation of each token by adapting it to its local context, which is not limited to the segment it resides in and can be flexibly defined according to the task.

Word Embeddings

An Embedding Learning Framework for Numerical Features in CTR Prediction

1 code implementation16 Dec 2020 Huifeng Guo, Bo Chen, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He

In this paper, we propose a novel embedding learning framework for numerical features in CTR prediction (AutoDis) with high model capacity, end-to-end training and unique representation properties preserved.

Click-Through Rate Prediction Feature Engineering +1

CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

2 code implementations14 Dec 2020 Bo Chen, Jing Zhang, Xiaokang Zhang, Xiaobin Tang, Lingfan Cai, Hong Chen, Cuiping Li, Peng Zhang, Jie Tang

In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.

Active Learning Contrastive Learning +2

Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network

no code implementations NeurIPS 2020 Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou

To analyze a collection of interconnected documents, relational topic models (RTMs) have been developed to describe both the link structure and document content, exploring their underlying relationships via a single-layer latent representation with limited expressive capability.

Topic Models

Bidirectional Convolutional Poisson Gamma Dynamical Systems

1 code implementation NeurIPS 2020 Wenchao Chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou

Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but also sentence-level stochastic temporal transitions.

Bayesian Inference Variational Inference

Bayesian Attention Modules

1 code implementation NeurIPS 2020 Xinjie Fan, Shujian Zhang, Bo Chen, Mingyuan Zhou

Attention modules, as simple and effective tools, have not only enabled deep neural networks to achieve state-of-the-art results in many domains, but also enhanced their interpretability.

Image Captioning Machine Translation +4

Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs

no code implementations12 Oct 2020 Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, Haipeng Ding

On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning.

Information Retrieval Question Answering

Variational Temporal Deep Generative Model for Radar HRRP Target Recognition

no code implementations28 Sep 2020 Dandan Guo, Bo Chen, Wenchao Chen, Chaojie Wang, Hongwei Liu, Mingyuan Zhou

We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP.

Variational Inference

Can weight sharing outperform random architecture search? An investigation with TuNAS

1 code implementation CVPR 2020 Gabriel Bender, Hanxiao Liu, Bo Chen, Grace Chu, Shuyang Cheng, Pieter-Jan Kindermans, Quoc Le

Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models.

Image Classification Neural Architecture Search

Optimizing Voting Order on Sequential Juries: A Median Voter Theorem and Beyond

no code implementations24 Jun 2020 Steve Alpern, Bo Chen

Each juror has an ability in [0, 1], which is proportional to the probability of A given a positive signal, an analog of Condorcet's p for binary signals.

Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference

no code implementations15 Jun 2020 Hao Zhang, Bo Chen, Yulai Cong, Dandan Guo, Hongwei Liu, Mingyuan Zhou

Given a posterior sample of the global parameters, in order to efficiently infer the local latent representations of a document under DATM across all stochastic layers, we propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a Weibull distribution based stochastic downward generative model.

Bayesian Inference

Comparison and Benchmark of Graph Clustering Algorithms

1 code implementation10 May 2020 Lizhen Shi, Bo Chen

Graph clustering is widely used in analysis of biological networks, social networks and etc.

Graph Clustering

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

4 code implementations CVPR 2021 Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen

By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators.

Neural Architecture Search object-detection +1

Topological Sweep for Multi-Target Detection of Geostationary Space Objects

no code implementations21 Mar 2020 Daqi Liu, Bo Chen, Tat-Jun Chin, Mark Rutten

In this paper, we propose a novel multi-target detection technique based on topological sweep, to find GEO objects from a short sequence of optical images.

object-detection Object Detection

Robust Synthesis of Wind Turbine Generators to Support Microgrid Frequency Considering Linearization-Induced Uncertainty

no code implementations5 Mar 2020 Yichen Zhang, Chen Chen, Tianqi Hong, Bai Cui, Bo Chen, Feng Qiu

The capability to switch between grid-connected and islanded modes has promoted adoption of microgrid technology for powering remote locations.

Global weak solutions for Landau-Lifshitz flows and heat flows associated to micromagnetic energy functional

no code implementations16 Jan 2020 Bo Chen, Youde Wang

We follow the idea of Wang \cite{W} to show the existence of global weak solutions to the Cauchy problems of Landau-Lifshtiz type equations and related heat flows from a $n$-dimensional Euclidean domain $\Om$ or a $n$-dimensional closed Riemannian manifold $M$ into a 2-dimensional unit sphere $\U^{2}$.

Analysis of PDEs

Recurrent Hierarchical Topic-Guided RNN for Language Generation

1 code implementation ICML 2020 Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou

To simultaneously capture syntax and global semantics from a text corpus, we propose a new larger-context recurrent neural network (RNN) based language model, which extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation.

Language Modelling Text Generation

MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices

2 code implementations CVPR 2020 Bo Chen, Golnaz Ghiasi, Hanxiao Liu, Tsung-Yi Lin, Dmitry Kalenichenko, Hartwig Adams, Quoc V. Le

We propose MnasFPN, a mobile-friendly search space for the detection head, and combine it with latency-aware architecture search to produce efficient object detection models.

object-detection Object Detection

SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction

no code implementations22 Nov 2019 Bo Chen, Decai Li, Yuqing He, Chunsheng Hua

In temporal dimension, we designed a knowledge graph based causal reasoning module and map the past actions to temporal causal features through Diffusion RNN.

Autonomous Driving Graph Attention +1

Recurrent Hierarchical Topic-Guided Neural Language Models

no code implementations25 Sep 2019 Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou

To simultaneously capture syntax and semantics from a text corpus, we propose a new larger-context language model that extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation.

Language Modelling Text Generation

End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization

2 code implementations CVPR 2020 Bo Chen, Alvaro Parra, Jiewei Cao, Nan Li, Tat-Jun Chin

To seamlessly combine deep learning and geometric vision, it is vital to perform learning and geometric optimization end-to-end.

 Ranked #1 on 6D Pose Estimation using RGB on LineMOD (Accuracy metric)

6D Pose Estimation 6D Pose Estimation using RGB

Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement

1 code implementation30 Aug 2019 Bo Chen, Jiewei Cao, Alvaro Parra, Tat-Jun Chin

We propose an approach to estimate the 6DOF pose of a satellite, relative to a canonical pose, from a single image.

BIG-bench Machine Learning Pose Estimation

Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling

1 code implementation ICLR 2020 Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou

For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework.

Convolutional Poisson Gamma Belief Network

1 code implementation14 May 2019 Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou

For text analysis, one often resorts to a lossy representation that either completely ignores word order or embeds each word as a low-dimensional dense feature vector.

VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning

no code implementations ICLR 2019 Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou

To extract and relate visual and linguistic concepts from images and textual descriptions for text-based zero-shot learning (ZSL), we develop variational hetero-encoder (VHE) that decodes text via a deep probabilisitic topic model, the variational posterior of whose local latent variables is encoded from an image via a Weibull distribution based inference network.

Image Generation Text Generation +2

Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution

no code implementations12 Mar 2019 Chen Feng, Tao Sheng, Zhiyu Liang, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Matthew Ardi, Alexander C. Berg, Yiran Chen, Bo Chen, Kent Gauen, Yung-Hsiang Lu

The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.


Sentence Rewriting for Semantic Parsing

no code implementations ACL 2016 Bo Chen, Le Sun, Xianpei Han, Bo An

A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology.

Semantic Parsing Sentence ReWriting

Deep Poisson gamma dynamical systems

no code implementations NeurIPS 2018 Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarchical latent structure from the data, but also capturing both first-order and long-range temporal dependencies.

Data Augmentation Time Series

Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

1 code implementation ACL 2018 Bo Chen, Le Sun, Xianpei Han

This paper proposes a neural semantic parsing approach -- Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process.

Graph Generation Representation Learning +1

MnasNet: Platform-Aware Neural Architecture Search for Mobile

16 code implementations CVPR 2019 Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.

Image Classification Neural Architecture Search +2

Accurate Text-Enhanced Knowledge Graph Representation Learning

no code implementations NAACL 2018 Bo An, Bo Chen, Xianpei Han, Le Sun

Previous representation learning techniques for knowledge graph representation usually represent the same entity or relation in different triples with the same representation, without considering the ambiguity of relations and entities.

General Classification Graph Representation Learning +3

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

4 code implementations ECCV 2018 Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam

This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget.

Image Classification

WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling

1 code implementation ICLR 2018 Hao Zhang, Bo Chen, Dandan Guo, Mingyuan Zhou

To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and autoencoding variational Bayes.

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

21 code implementations CVPR 2018 Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, Dmitry Kalenichenko

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes.

Arithmetic General Classification +1

Investigating the content and form of referring expressions in Mandarin: introducing the Mtuna corpus

no code implementations WS 2017 Kees van Deemter, Le Sun, Rint Sybesma, Xiao Li, Bo Chen, Muyun Yang

East Asian languages are thought to handle reference differently from languages such as English, particularly in terms of the marking of definiteness and number.

Text Generation

Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC

no code implementations ICML 2017 Yulai Cong, Bo Chen, Hongwei Liu, Mingyuan Zhou

It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers.

Data Augmentation

Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

no code implementations30 Apr 2017 Ganbin Zhou, Ping Luo, Rongyu Cao, Yijun Xiao, Fen Lin, Bo Chen, Qing He

Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output.

Seeing into Darkness: Scotopic Visual Recognition

1 code implementation CVPR 2017 Bo Chen, Pietro Perona

Images are formed by counting how many photons traveling from a given set of directions hit an image sensor during a given time interval.

Astronomy General Classification

Gamma Belief Networks

no code implementations9 Dec 2015 Mingyuan Zhou, Yulai Cong, Bo Chen

To infer multilayer deep representations of high-dimensional discrete and nonnegative real vectors, we propose an augmentable gamma belief network (GBN) that factorizes each of its hidden layers into the product of a sparse connection weight matrix and the nonnegative real hidden units of the next layer.

The Poisson Gamma Belief Network

no code implementations NeurIPS 2015 Mingyuan Zhou, Yulai Cong, Bo Chen

Example results on text analysis illustrate interesting relationships between the width of the first layer and the inferred network structure, and demonstrate that the PGBN, whose hidden units are imposed with correlated gamma priors, can add more layers to increase its performance gains over Poisson factor analysis, given the same limit on the width of the first layer.

Learning Fine-grained Image Similarity with Deep Ranking

7 code implementations CVPR 2014 Jiang Wang, Yang song, Thomas Leung, Chuck Rosenberg, Jinbin Wang, James Philbin, Bo Chen, Ying Wu

This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features.

General Classification

On the Analysis of Multi-Channel Neural Spike Data

no code implementations NeurIPS 2011 Bo Chen, David E. Carlson, Lawrence Carin

Nonparametric Bayesian methods are developed for analysis of multi-channel spike-train data, with the feature learning and spike sorting performed jointly.

Dictionary Learning Spike Sorting

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