Search Results for author: Bo Wu

Found 50 papers, 12 papers with code

A Lightweight NMS-free Framework for Real-time Visual Fault Detection System of Freight Trains

no code implementations25 May 2022 Guodong Sun, Yang Zhou, Huilin Pan, Bo Wu, Ye Hu, Yang Zhang

In this paper, we propose a lightweight NMS-free framework to achieve real-time detection and high accuracy simultaneously.

Fault Detection

DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs

1 code implementation4 May 2022 Jialun Cao, Meiziniu Li, Xiao Chen, Ming Wen, Yongqiang Tian, Bo Wu, Shing-Chi Cheung

Besides, for fault localization, DeepFD also outperforms the existing works, correctly locating 42% faulty programs, which almost doubles the best result (23%) achieved by the existing works.

Fault localization

DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection

1 code implementation CVPR 2022 Yingwei Li, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan Yuille, Mingxing Tan

In this paper, we propose two novel techniques: InverseAug that inverses geometric-related augmentations, e. g., rotation, to enable accurate geometric alignment between lidar points and image pixels, and LearnableAlign that leverages cross-attention to dynamically capture the correlations between image and lidar features during fusion.

3D Object Detection Autonomous Driving +2

STAR: A Benchmark for Situated Reasoning in Real-World Videos

1 code implementation NeurIPS 2021 Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B. Tenenbaum, Chuang Gan

This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR).

Question Answering

NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM

no code implementations NeurIPS 2021 Connor Holmes, Minjia Zhang, Yuxiong He, Bo Wu

In particular, we propose to formulate the NxM sparsity as a constrained optimization problem and use Alternating Direction Method of Multipliers (ADMM) to optimize the downstream tasks while taking the underlying hardware constraints into consideration.

Knowledge Distillation Natural Language Processing +2

HoloFormer: Deep Compression of Pre-Trained Transforms via Unified Optimization of N:M Sparsity and Integer Quantization

no code implementations29 Sep 2021 Minjia Zhang, Connor Holmes, Yuxiong He, Bo Wu

In this work, we propose a unified, systematic approach to learning N:M sparsity and integer quantization for pre-trained Transformers using the Alternating Directions Method of Multipliers (ADMM).

Natural Language Processing Quantization

Is Machine Learning Ready for Traffic Engineering Optimization?

1 code implementation3 Sep 2021 Guillermo Bernárdez, José Suárez-Varela, Albert López, Bo Wu, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio

In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE.

Multi-agent Reinforcement Learning

TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation

no code implementations31 Mar 2021 Helin Wang, Bo Wu, LianWu Chen, Meng Yu, Jianwei Yu, Yong Xu, Shi-Xiong Zhang, Chao Weng, Dan Su, Dong Yu

In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments.

Speech Dereverberation

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

Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration

no code implementations19 Jan 2021 Peng Jiang, Rujia Wang, Bo Wu

Frequent Subgraph Mining (FSM) is the key task in many graph mining and machine learning applications.

Graph Mining Databases Performance

Analogical Reasoning for Visually Grounded Compositional Generalization

no code implementations1 Jan 2021 Bo Wu, Haoyu Qin, Alireza Zareian, Carl Vondrick, Shih-Fu Chang

Children acquire language subconsciously by observing the surrounding world and listening to descriptions.

Language Acquisition

MG-SAGC: A multiscale graph and its self-adaptive graph convolution network for 3D point clouds

no code implementations23 Dec 2020 Bo Wu, Bo Lang

To enhance the ability of neural networks to extract local point cloud features and improve their quality, in this paper, we propose a multiscale graph generation method and a self-adaptive graph convolution method.

Graph Generation

The Bright Side and the Dark Side of Hybrid Organic Inorganic Perovskites

no code implementations23 Oct 2020 Wladek Walukiewicz, Shu Wang, Xinchun Wu, Rundong Li, Matthew P. Sherburne, Bo Wu, Tze Chien Sun, Joel W. Ager, Mark D. Asta

The previously developed bistable amphoteric native defect (BAND) model is used for a comprehensive explanation of the unique photophysical properties and for understanding the remarkable performance of perovskites as photovoltaic materials.

Applied Physics Materials Science

Byzantine-Resilient Distributed Hypothesis Testing With Time-Varying Network Topology

no code implementations1 Aug 2020 Bo Wu, Steven Carr, Suda Bharadwaj, Zhe Xu, Ufuk Topcu

We study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively.

Analogical Reasoning for Visually Grounded Language Acquisition

no code implementations22 Jul 2020 Bo Wu, Haoyu Qin, Alireza Zareian, Carl Vondrick, Shih-Fu Chang

Children acquire language subconsciously by observing the surrounding world and listening to descriptions.

Language Acquisition

Reward Machines for Cooperative Multi-Agent Reinforcement Learning

2 code implementations3 Jul 2020 Cyrus Neary, Zhe Xu, Bo Wu, Ufuk Topcu

In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal.

Multi-agent Reinforcement Learning Q-Learning +1

Temporal-Logic-Based Reward Shaping for Continuing Learning Tasks

no code implementations3 Jul 2020 Yuqian Jiang, Sudarshanan Bharadwaj, Bo Wu, Rishi Shah, Ufuk Topcu, Peter Stone

Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy.

reinforcement-learning

GAIA: A Fine-grained Multimedia Knowledge Extraction System

no code implementations ACL 2020 Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman

We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.

Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples

no code implementations28 Jun 2020 Zhe Xu, Bo Wu, Aditya Ojha, Daniel Neider, Ufuk Topcu

We compare our algorithm with the state-of-the-art RL algorithms for non-Markovian reward functions, such as Joint Inference of Reward machines and Policies for RL (JIRP), Learning Reward Machine (LRM), and Proximal Policy Optimization (PPO2).

Active Learning Q-Learning +1

Learning the Compositional Visual Coherence for Complementary Recommendations

no code implementations8 Jun 2020 Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, Tao Mei

Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents.

Dynamic Spatiotemporal Graph Neural Network with Tensor Network

no code implementations12 Mar 2020 Chengcheng Jia, Bo Wu, Xiao-Ping Zhang

Dynamic spatial graph construction is a challenge in graph neural network (GNN) for time series data problems.

graph construction Time Series

Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients

no code implementations1 Feb 2020 Xiaojun Cai, Huizhu Song, Zheng Jiao, Hang Yang, Min Zhu, Chengyu Wang, Dong Wei, Lingzhi Shi, Bo Wu, Jinyu Chen

Given the nonlinear kinetics of tacrolimus and large variability, population pharmacokinetic model should be combined with therapeutic drug monitoring to optimize individualized therapy.

Audio-visual Recognition of Overlapped speech for the LRS2 dataset

no code implementations6 Jan 2020 Jianwei Yu, Shi-Xiong Zhang, Jian Wu, Shahram Ghorbani, Bo Wu, Shiyin Kang, Shansong Liu, Xunying Liu, Helen Meng, Dong Yu

Experiments on overlapped speech simulated from the LRS2 dataset suggest the proposed AVSR system outperformed the audio only baseline LF-MMI DNN system by up to 29. 98\% absolute in word error rate (WER) reduction, and produced recognition performance comparable to a more complex pipelined system.

Audio-Visual Speech Recognition Automatic Speech Recognition +3

General Partial Label Learning via Dual Bipartite Graph Autoencoder

no code implementations5 Jan 2020 Brian Chen, Bo Wu, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang

Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervision assumption from instance-level -- a label set partially labels an instance -- to group-level: 1) a label set partially labels a group of instances, where the within-group instance-label link annotations are missing, and 2) cross-group links are allowed -- instances in a group may be partially linked to the label set from another group.

Partial Label Learning

Theme-Matters: Fashion Compatibility Learning via Theme Attention

no code implementations12 Dec 2019 Jui-Hsin Lai, Bo Wu, Xin Wang, Dan Zeng, Tao Mei, Jingen Liu

This model associates themes with the pairwise compatibility with attention, and thus compute the outfit-wise compatibility.

Fashion Compatibility Learning

SMP Challenge: An Overview of Social Media Prediction Challenge 2019

no code implementations4 Oct 2019 Bo Wu, Wen-Huang Cheng, Peiye Liu, Bei Liu, Zhaoyang Zeng, Jiebo Luo

In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction task Temporal Popularity Prediction, which focuses on predicting future interaction or attractiveness (in terms of clicks, views or likes etc.)

Joint Inference of Reward Machines and Policies for Reinforcement Learning

no code implementations12 Sep 2019 Zhe Xu, Ivan Gavran, Yousef Ahmad, Rupak Majumdar, Daniel Neider, Ufuk Topcu, Bo Wu

The experiments show that learning high-level knowledge in the form of reward machines can lead to fast convergence to optimal policies in RL, while standard RL methods such as q-learning and hierarchical RL methods fail to converge to optimal policies after a substantial number of training steps in many tasks.

Q-Learning reinforcement-learning

Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network

1 code implementation26 Jul 2019 Xin Wang, Bo Wu, Yun Ye, Yueqi Zhong

Existing works about fashion outfit compatibility focus on predicting the overall compatibility of a set of fashion items with their information from different modalities.

Fashion Compatibility Learning

Hard-Aware Fashion Attribute Classification

no code implementations25 Jul 2019 Yun Ye, Yixin Li, Bo Wu, Wei zhang, Ling-Yu Duan, Tao Mei

For "hard" attributes with insufficient training data, Deact brings more stable synthetic samples for training and further improve the performance.

Classification General Classification

MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning

no code implementations22 Jul 2019 Peiye Liu, Bo Wu, Huadong Ma, Mingoo Seok

Recent studies on automatic neural architectures search have demonstrated significant performance, competitive to or even better than hand-crafted neural architectures.

Neural Architecture Search

Improving Reverberant Speech Training Using Diffuse Acoustic Simulation

no code implementations9 Jul 2019 Zhenyu Tang, Lian-Wu Chen, Bo Wu, Dong Yu, Dinesh Manocha

We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks.

Keyword Spotting Speech Recognition

Reward-Based Deception with Cognitive Bias

no code implementations25 Apr 2019 Bo Wu, Murat Cubuktepe, Suda Bharadwaj, Ufuk Topcu

In this paper, we consider deceiving adversaries with bounded rationality and in terms of expected rewards.

Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning

no code implementations20 Mar 2018 Li He, Liang Wang, Kaipeng Liu, Bo Wu, Wei-Nan Zhang

From the advertisers' side, participating in ranking the search results by paying for the sponsored search advertisement to attract more awareness and purchase facilitates their commercial goal.

online learning reinforcement-learning

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

no code implementations29 Dec 2017 Su Yan, Wei. Lin, Tianshu Wu, Daorui Xiao, Xu Zheng, Bo Wu, Kaipeng Liu

Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes.

Time Matters: Multi-scale Temporalization of Social Media Popularity

no code implementations12 Dec 2017 Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Tao Mei

We evaluate our approach on two large-scale Flickr image datasets with over 1. 8 million photos in total, for the task of popularity prediction.

Social Media Popularity Prediction

Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

1 code implementation12 Dec 2017 Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, Tao Mei

With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space.

Social Media Popularity Prediction

DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model

no code implementations10 Dec 2017 Bo Wu, Yang Liu, Bo Lang, Lei Huang

Convolutional neural networks (CNNs) can be applied to graph similarity matching, in which case they are called graph CNNs.

Graph Classification Graph Similarity

Learning Unified Embedding for Apparel Recognition

no code implementations19 Jul 2017 Yang Song, Yuan Li, Bo Wu, Chao-Yeh Chen, Xiao Zhang, Hartwig Adam

To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss.

A Learning Based Optimal Human Robot Collaboration with Linear Temporal Logic Constraints

no code implementations31 May 2017 Bo Wu, Bin Hu, Hai Lin

This paper considers an optimal task allocation problem for human robot collaboration in human robot systems with persistent tasks.

Feature Selection Parallel Technique for Remotely Sensed Imagery Classification

no code implementations11 Apr 2017 Nhien-An Le-Khac, M-Tahar Kechadi, Bo Wu, C. Chen

Remote sensing research focusing on feature selection has long attracted the attention of the remote sensing community because feature selection is a prerequisite for image processing and various applications.

Classification feature selection +1

Bearing fault diagnosis based on spectrum images of vibration signals

no code implementations8 Nov 2015 Wei Li, Mingquan Qiu, Zhencai Zhu, Bo Wu, Gongbo Zhou

Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery, and it's receiving more and more attention.

General Classification

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