Search Results for author: Yi Xu

Found 67 papers, 11 papers with code

Monocular 3D Object Detection via Feature Domain Adaptation

no code implementations ECCV 2020 Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu

Monocular 3D object detection is a challenging task due to unreliable depth, resulting in a distinct performance gap between monocular and LiDAR-based approaches.

Domain Adaptation Monocular 3D Object Detection

Weakly-supervised Text Classification Based on Keyword Graph

1 code implementation6 Oct 2021 Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, Shuigeng Zhou

Among them, keyword-driven methods are the mainstream where user-provided keywords are exploited to generate pseudo-labels for unlabeled texts.

Classification Text Classification

Distribution-sensitive Information Retention for Accurate Binary Neural Network

no code implementations25 Sep 2021 Haotong Qin, Xiangguo Zhang, Ruihao Gong, Yifu Ding, Yi Xu, XianglongLiu

The empirical study shows that binarization causes a great loss of information in the forward and backward propagation which harms the performance of binary neural networks (BNNs), and the limited information representation ability of binarized parameter is one of the bottlenecks of BNN performance.

Binarization Quantization

Dash: Semi-Supervised Learning with Dynamic Thresholding

no code implementations1 Sep 2021 Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin

In this work we develop a simple yet powerful framework, whose key idea is to select a subset of training examples from the unlabeled data when performing existing SSL methods so that only the unlabeled examples with pseudo labels related to the labeled data will be used to train models.

Semi-Supervised Image Classification

Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction

no code implementations4 Aug 2021 Minyi Zhao, Yi Xu, Shuigeng Zhou

A number of deep learning based algorithms have been proposed to recover high-quality videos from low-quality compressed ones.

Video Compression

MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments

no code implementations ICCV 2021 Pan Ji, Runze Li, Bir Bhanu, Yi Xu

The effectiveness of each module is shown through a carefully conducted ablation study and the demonstration of the state-of-the-art performance on three indoor datasets, \ie, EuRoC, NYUv2, and 7-scenes.

Monocular Depth Estimation Pose Estimation

Adapting Stepsizes by Momentumized Gradients Improves Optimization and Generalization

no code implementations22 Jun 2021 Yizhou Wang, Yue Kang, Can Qin, Huan Wang, Yi Xu, Yulun Zhang, Yun Fu

Adaptive gradient methods, such as Adam, have achieved tremendous success in machine learning.

Dialogue-oriented Pre-training

1 code implementation1 Jun 2021 Yi Xu, Hai Zhao

Pre-trained language models (PrLM) has been shown powerful in enhancing a broad range of downstream tasks including various dialogue related ones.

Language Modelling

Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information

no code implementations31 May 2021 Yi Xu, Minyi Zhao, Jing Liu, Xinjian Zhang, Longwen Gao, Shuigeng Zhou, Huyang Sun

Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos.

Video Compression

Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation

no code implementations25 May 2021 Chen Shi, Xiangtai Li, Yanran Wu, Yunhai Tong, Yi Xu

Representation of semantic context and local details is the essential issue for building modern semantic segmentation models.

Semantic Segmentation

NeuLF: Efficient Novel View Synthesis with Neural 4D Light Field

no code implementations15 May 2021 Celong Liu, Zhong Li, Junsong Yuan, Yi Xu

In this paper, we present an efficient and robust deep learning solution for novel view synthesis of complex scenes.

Novel View Synthesis

Why Does Multi-Epoch Training Help?

no code implementations13 May 2021 Yi Xu, Qi Qian, Hao Li, Rong Jin

Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in each updating step, and good performance.

On Stochastic Moving-Average Estimators for Non-Convex Optimization

no code implementations30 Apr 2021 Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

In this paper, we demonstrate the power of a widely used stochastic estimator based on moving average (SEMA) on a range of stochastic non-convex optimization problems, which only requires {\bf a general unbiased stochastic oracle}.

bilevel optimization

A Theoretical Analysis of Learning with Noisily Labeled Data

no code implementations8 Apr 2021 Yi Xu, Qi Qian, Hao Li, Rong Jin

Noisy labels are very common in deep supervised learning.

Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity

1 code implementation9 Feb 2021 Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification.

Federated Learning

A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks

no code implementations12 Jan 2021 Asaf Noy, Yi Xu, Yonathan Aflalo, Lihi Zelnik-Manor, Rong Jin

We show that convergence to a global minimum is guaranteed for networks with widths quadratic in the sample size and linear in their depth at a time logarithmic in both.

Attentional Biased Stochastic Gradient for Imbalanced Classification

no code implementations13 Dec 2020 Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang

In this paper, we present a simple yet effective method (ABSGD) for addressing the data imbalance issue in deep learning.

Classification General Classification +2

WeMix: How to Better Utilize Data Augmentation

no code implementations3 Oct 2020 Yi Xu, Asaf Noy, Ming Lin, Qi Qian, Hao Li, Rong Jin

To this end, we develop two novel algorithms, termed "AugDrop" and "MixLoss", to correct the data bias in the data augmentation.

Data Augmentation

Topic-Aware Multi-turn Dialogue Modeling

no code implementations26 Sep 2020 Yi Xu, Hai Zhao, Zhuosheng Zhang

In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances.

Object Detection in the Context of Mobile Augmented Reality

no code implementations15 Aug 2020 Xiang Li, Yuan Tian, Fuyao Zhang, Shuxue Quan, Yi Xu

Ordinary object detection approaches process information from the images only, and they are oblivious to the camera pose with regard to the environment and the scale of the environment.

Real-Time Object Detection

Talking-head Generation with Rhythmic Head Motion

1 code implementation16 Jul 2020 Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu

When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information.

Talking Head Generation

Towards Understanding Label Smoothing

no code implementations20 Jun 2020 Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin

Label smoothing regularization (LSR) has a great success in training deep neural networks by stochastic algorithms such as stochastic gradient descent and its variants.

An Online Method for Distributionally Deep Robust Optimization

no code implementations17 Jun 2020 Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang

The proposed online stochastic method resembles the practical stochastic Nesterovs method in several perspectives that are widely used for learning deep neural networks.

Evaluating Features and Metrics for High-Quality Simulation of Early Vocal Learning of Vowels

no code implementations20 May 2020 Branislav Gerazov, Daniel van Niekerk, Anqi Xu, Paul Konstantin Krug, Peter Birkholz, Yi Xu

One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target.

Speech Synthesis

Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers

no code implementations12 Apr 2020 Jiancheng Yang, Haoran Deng, Xiaoyang Huang, Bingbing Ni, Yi Xu

In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules.

Multiple Instance Learning Relational Reasoning

Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment

no code implementations CVPR 2020 Qiuyu Chen, Wei zhang, Ning Zhou, Peng Lei, Yi Xu, Yu Zheng, Jianping Fan

Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integers dilated kernels is used to cope with the misalignment of fractional sampling.

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization

no code implementations NeurIPS 2020 Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

In this paper, we bridge this gap by providing a sharp analysis of epoch-wise stochastic gradient descent ascent method (referred to as Epoch-GDA) for solving strongly convex strongly concave (SCSC) min-max problems, without imposing any additional assumption about smoothness or the function's structure.

Occlum: Secure and Efficient Multitasking Inside a Single Enclave of Intel SGX

7 code implementations21 Jan 2020 Youren Shen, Hongliang Tian, Yu Chen, Kang Chen, Runji Wang, Yi Xu, Yubin Xia

SFI is a software instrumentation technique for sandboxing untrusted modules (called domains).

Operating Systems Hardware Architecture Cryptography and Security

Quaternion Product Units for Deep Learning on 3D Rotation Groups

1 code implementation CVPR 2020 Xuan Zhang, Shaofei Qin, Yi Xu, Hongteng Xu

We propose a novel quaternion product unit (QPU) to represent data on 3D rotation groups.

Non-Local ConvLSTM for Video Compression Artifact Reduction

no code implementations ICCV 2019 Yi Xu, Longwen Gao, Kai Tian, Shuigeng Zhou, Huyang Sun

Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos.

Video Compression

Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

no code implementations20 Oct 2019 Jiancheng Yang, Rongyao Fang, Bingbing Ni, Yamin Li, Yi Xu, Linguo Li

The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable.

Probabilistic Deep Learning

On Leveraging the Visual Modality for Neural Machine Translation

no code implementations WS 2019 Vikas Raunak, Sang Keun Choe, Quanyang Lu, Yi Xu, Florian Metze

Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics.

Multimodal Machine Translation Translation

Evaluating and Boosting Uncertainty Quantification in Classification

no code implementations13 Sep 2019 Xiaoyang Huang, Jiancheng Yang, Linguo Li, Haoran Deng, Bingbing Ni, Yi Xu

Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted medical decision making.

Classification Decision Making +1

Stochastic Optimization for Non-convex Inf-Projection Problems

no code implementations ICML 2020 Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang

In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable.

Stochastic Optimization

Optimizing Interim Analysis Timing for Bayesian Adaptive Commensurate Designs

1 code implementation17 May 2019 Xiao Wu, Yi Xu, Bradley P. Carlin

In developing products for rare diseases, statistical challenges arise due to the limited number of patients available for participation in drug trials and other clinical research.

Applications Computation Methodology

Stochastic Primal-Dual Algorithms with Faster Convergence than $O(1/\sqrt{T})$ for Problems without Bilinear Structure

no code implementations23 Apr 2019 Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang

The main contribution of this paper is the design and analysis of new stochastic primal-dual algorithms that use a mixture of stochastic gradient updates and a logarithmic number of deterministic dual updates for solving a family of convex-concave problems with no bilinear structure assumed.

Quaternion Convolutional Neural Networks

no code implementations ECCV 2018 Xuanyu Zhu, Yi Xu, Hongteng Xu, Changjian Chen

Neural networks in the real domain have been studied for a long time and achieved promising results in many vision tasks for recent years.

Denoising Image Classification

Disentangled Deep Autoencoding Regularization for Robust Image Classification

no code implementations27 Feb 2019 Zhenyu Duan, Martin Renqiang Min, Li Erran Li, Mingbo Cai, Yi Xu, Bingbing Ni

In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult to generalize to novel test images with reasonably large geometric transformations.

Classification General Classification +2

Analogy Search Engine: Finding Analogies in Cross-Domain Research Papers

no code implementations17 Dec 2018 Jieli Zhou, Yuntao Zhou, Yi Xu

ASE combines recent theories and methods from Computational Analogy and Natural Language Processing to go beyond keyword-based lexical search and discover the deeper analogical relationships among research paper abstracts.

Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence

no code implementations28 Nov 2018 Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang

In this paper, we propose new stochastic optimization algorithms and study their first-order convergence theories for solving a broad family of DC functions.

Stochastic Optimization

Geometric Constrained Joint Lane Segmentation and Lane Boundary Detection

no code implementations ECCV 2018 Jie Zhang, Yi Xu, Bingbing Ni, Zhenyu Duan

The main contributions of the proposed frame- work are highlighted in two facets: (1) We put forward a multiple-task learning framework with mutually interlinked sub-structures between lane segmentation and lane boundary detection to improve overall performance.

Boundary Detection Lane Detection

SADAGRAD: Strongly Adaptive Stochastic Gradient Methods

no code implementations ICML 2018 Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang

Although the convergence rates of existing variants of ADAGRAD have a better dependence on the number of iterations under the strong convexity condition, their iteration complexities have a explicitly linear dependence on the dimensionality of the problem.

A Variational Prosody Model for the decomposition and synthesis of speech prosody

1 code implementation22 Jun 2018 Branislav Gerazov, Gérard Bailly, Omar Mohammed, Yi Xu, Philip N. Garner

Our work bridges between a comprehensive generative model of intonation and state-of-the-art AI techniques.

Speech Synthesis

Crowd Counting via Adversarial Cross-Scale Consistency Pursuit

1 code implementation CVPR 2018 Zan Shen, Yi Xu, Bingbing Ni, Minsi Wang, Jianguo Hu, Xiaokang Yang

Crowd counting or density estimation is a challenging task in computer vision due to large scale variations, perspective distortions and serious occlusions, etc.

Crowd Counting Density Estimation

Learning with Non-Convex Truncated Losses by SGD

no code implementations21 May 2018 Yi Xu, Shenghuo Zhu, Sen yang, Chi Zhang, Rong Jin, Tianbao Yang

Learning with a {\it convex loss} function has been a dominating paradigm for many years.

NEON+: Accelerated Gradient Methods for Extracting Negative Curvature for Non-Convex Optimization

no code implementations4 Dec 2017 Yi Xu, Rong Jin, Tianbao Yang

Accelerated gradient (AG) methods are breakthroughs in convex optimization, improving the convergence rate of the gradient descent method for optimization with smooth functions.

Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter

no code implementations NeurIPS 2017 Yi Xu, Qihang Lin, Tianbao Yang

The most studied error bound is the quadratic error bound, which generalizes strong convexity and is satisfied by a large family of machine learning problems.

Stochastic Optimization

ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization

no code implementations NeurIPS 2017 Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang

The novelty of the proposed scheme lies at that it is adaptive to a local sharpness property of the objective function, which marks the key difference from previous adaptive scheme that adjusts the penalty parameter per-iteration based on certain conditions on iterates.

Stochastic Optimization

First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time

no code implementations NeurIPS 2018 Yi Xu, Rong Jin, Tianbao Yang

Two classes of methods have been proposed for escaping from saddle points with one using the second-order information carried by the Hessian and the other adding the noise into the first-order information.

Flexible Network Binarization with Layer-wise Priority

no code implementations13 Sep 2017 Lixue Zhuang, Yi Xu, Bingbing Ni, Hongteng Xu

In this work, we reveal an important fact that binarizing different layers has a widely-varied effect on the compression ratio of network and the loss of performance.

Binarization Pedestrian Detection

Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence

no code implementations ICML 2017 Yi Xu, Qihang Lin, Tianbao Yang

In this paper, a new theory is developed for first-order stochastic convex optimization, showing that the global convergence rate is sufficiently quantified by a local growth rate of the objective function in a neighborhood of the optimal solutions.

Stochastic Optimization

Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee

no code implementations6 Dec 2016 Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang

Previously, oblivious random projection based approaches that project high dimensional features onto a random subspace have been used in practice for tackling high-dimensionality challenge in machine learning.

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon)

no code implementations NeurIPS 2016 Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

To the best of our knowledge, this is the lowest iteration complexity achieved so far for the considered non-smooth optimization problems without strong convexity assumption.

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/ε)$

no code implementations NeurIPS 2016 Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

In this work, we will show that the proposed HOPS achieved a lower iteration complexity of $\widetilde O(1/\epsilon^{1-\theta})$\footnote{$\widetilde O()$ suppresses a logarithmic factor.}

Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition

no code implementations4 Jul 2016 Yi Xu, Qihang Lin, Tianbao Yang

In particular, if the objective function $F(\mathbf w)$ in the $\epsilon$-sublevel set grows as fast as $\|\mathbf w - \mathbf w_*\|_2^{1/\theta}$, where $\mathbf w_*$ represents the closest optimal solution to $\mathbf w$ and $\theta\in(0, 1]$ quantifies the local growth rate, the iteration complexity of first-order stochastic optimization for achieving an $\epsilon$-optimal solution can be $\widetilde O(1/\epsilon^{2(1-\theta)})$, which is optimal at most up to a logarithmic factor.

Stochastic Optimization

Feature Selection Based on Confidence Machine

no code implementations20 Oct 2014 Chang Liu, Yi Xu

We propose a filter method for unsupervised feature selection which is based on the Confidence Machine.

Feature Selection

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