Search Results for author: Jia Liu

Found 84 papers, 20 papers with code

Spelling Correction using Phonetics in E-commerce Search

no code implementations ECNLP (ACL) 2022 Fan Yang, Alireza Bagheri Garakani, Yifei Teng, Yan Gao, Jia Liu, Jingyuan Deng, Yi Sun

In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries.

Spelling Correction

Explicit Change Relation Learning for Change Detection in VHR Remote Sensing Images

no code implementations14 Nov 2023 Dalong Zheng, Zebin Wu, Jia Liu, Chih-Cheng Hung, Zhihui Wei

In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively.

Binary Classification Change Detection

Alignment is not sufficient to prevent large language models from generating harmful information: A psychoanalytic perspective

no code implementations14 Nov 2023 Zi Yin, Wei Ding, Jia Liu

Large Language Models (LLMs) are central to a multitude of applications but struggle with significant risks, notably in generating harmful content and biases.

Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments

no code implementations8 Nov 2023 Tianchen Zhou, Jia Liu, Yang Jiao, Chaosheng Dong, Yetian Chen, Yan Gao, Yi Sun

Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years.

Learning-To-Rank Recommendation Systems

SwinV2DNet: Pyramid and Self-Supervision Compounded Feature Learning for Remote Sensing Images Change Detection

no code implementations22 Aug 2023 Dalong Zheng, Zebin Wu, Jia Liu, Zhihui Wei

Therefore, based on swin transformer V2 (Swin V2) and VGG16, we propose an end-to-end compounded dense network SwinV2DNet to inherit the advantages of both transformer and CNN and overcome the shortcomings of existing networks in feature learning.

Change Detection

Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier

1 code implementation22 Jul 2023 Zhixing Zhang, Ziwei Zhao, Dong Wang, Shishuang Zhao, Yuhang Liu, Jia Liu, LiWei Wang

Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases.


Geometric Pooling: maintaining more useful information

no code implementations21 Jun 2023 Hao Xu, Jia Liu, Yang shen, Kenan Lou, Yanxia Bao, Ruihua Zhang, Shuyue Zhou, Hongsen Zhao, Shuai Wang

However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain useful information and can contribute considerably to the final decision.

Node Classification

AdaSelection: Accelerating Deep Learning Training through Data Subsampling

no code implementations19 Jun 2023 Minghe Zhang, Chaosheng Dong, Jinmiao Fu, Tianchen Zhou, Jia Liang, Jia Liu, Bo Liu, Michinari Momma, Bryan Wang, Yan Gao, Yi Sun

In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance.

Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videos

1 code implementation5 Jun 2023 Xinrui Zhou, Yuhao Huang, Wufeng Xue, Xin Yang, Yuxin Zou, Qilong Ying, Yuanji Zhang, Jia Liu, Jie Ren, Dong Ni

First, to avoid the requirement of laborious and unreliable annotation, we propose a novel and effective video classification network for weakly-supervised CSG.

Video Classification

Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusion

1 code implementation21 Mar 2023 Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, LiMin Wang

To fuse dense image features and sparse point features, we propose a learnable operator named bidirectional camera-LiDAR fusion module (Bi-CLFM).

Optical Flow Estimation Scene Flow Estimation +1

PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities

no code implementations5 Mar 2023 Zhuqing Liu, Xin Zhang, Songtao Lu, Jia Liu

Decentralized min-max optimization problems with domain constraints underpins many important ML applications, including multi-agent ML fairness assurance, and policy evaluations in multi-agent reinforcement learning.

Fairness Multi-agent Reinforcement Learning

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

no code implementations20 Jan 2023 Luyao Chen, Zhiqiang Chen, Longsheng Jiang, Xiang Liu, Linlu Xu, Bo Zhang, Xiaolong Zou, Jinying Gao, Yu Zhu, Xizi Gong, Shan Yu, Sen Song, Liangyi Chen, Fang Fang, Si Wu, Jia Liu

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.

Few-Shot Learning Image Classification

AFLGuard: Byzantine-robust Asynchronous Federated Learning

no code implementations13 Dec 2022 Minghong Fang, Jia Liu, Neil Zhenqiang Gong, Elizabeth S. Bentley

Asynchronous FL aims to address this challenge by enabling the server to update the model once any client's model update reaches it without waiting for other clients' model updates.

Federated Learning

FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data

no code implementations13 Dec 2022 Minghong Fang, Jia Liu, Michinari Momma, Yi Sun

In this paper, we propose a new approach called fair recommendation with optimized antidote data (FairRoad), which aims to improve the fairness performances of recommender systems through the construction of a small and carefully crafted antidote dataset.

Fairness Recommendation Systems

DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization

no code implementations5 Dec 2022 Peiwen Qiu, Yining Li, Zhuqing Liu, Prashant Khanduri, Jia Liu, Ness B. Shroff, Elizabeth Serena Bentley, Kurt Turck

Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e. g., multi-agent meta-learning and multi-agent reinforcement learning) over peer-to-peer edge networks.

Bilevel Optimization Meta-Learning +1

Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning

no code implementations3 Oct 2022 Haibo Yang, Peiwen Qiu, Jia Liu

A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in stochastic first-order information has a finite variance.

Federated Learning

SAGDA: Achieving $\mathcal{O}(ε^{-2})$ Communication Complexity in Federated Min-Max Learning

no code implementations2 Oct 2022 Haibo Yang, Zhuqing Liu, Xin Zhang, Jia Liu

To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as in conventional federated learning.

Federated Learning

Robust Domain Adaptation for Machine Reading Comprehension

no code implementations23 Sep 2022 Liang Jiang, Zhenyu Huang, Jia Liu, Zujie Wen, Xi Peng

Such a process will inevitably introduce mismatched pairs (i. e., noisy correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain.

Domain Adaptation Machine Reading Comprehension

SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters

no code implementations17 Aug 2022 Zhuqing Liu, Xin Zhang, Jia Liu

To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both synchronous and asynchronous distributed stochastic variance-reduced optimization methods.

NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data

no code implementations17 Aug 2022 Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, Zhengyuan Zhu

Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question.

Federated Learning Open-Ended Question Answering

INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks

no code implementations27 Jul 2022 Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu

Our main contributions in this paper are two-fold: i) We first propose a deterministic algorithm called INTERACT (inner-gradient-descent-outer-tracked-gradient) that requires the sample complexity of $\mathcal{O}(n \epsilon^{-1})$ and communication complexity of $\mathcal{O}(\epsilon^{-1})$ to solve the bilevel optimization problem, where $n$ and $\epsilon > 0$ are the number of samples at each agent and the desired stationarity gap, respectively.

Bilevel Optimization Meta-Learning +1

Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures

no code implementations12 Jul 2022 Jia Liu, Ran Cheng, Yaochu Jin

First, we formulate the NAS problem for enhancing adversarial robustness of deep neural networks into a multiobjective optimization problem.

Adversarial Robustness Multiobjective Optimization +1

A Dual-fusion Semantic Segmentation Framework With GAN For SAR Images

no code implementations2 Jun 2022 Donghui Li, Jia Liu, Fang Liu, Wenhua Zhang, Andi Zhang, Wenfei Gao, Jiao Shi

With the better representation capability of optical images, we propose to enrich SAR images with generated optical images via the generative adversative network (GAN) trained by numerous SAR and optical images.

Image Segmentation Segmentation +1

Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control

no code implementations12 May 2022 Haibo Yang, Peiwen Qiu, Jia Liu, Aylin Yener

In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device.

Federated Learning

An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

1 code implementation1 Apr 2022 Jia Liu, Wenjie Xuan, Yuhang Gan, Juhua Liu, Bo Du

In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions.

Change Detection Change detection for remote sensing images +1

Probabilistic Mass Mapping with Neural Score Estimation

no code implementations14 Jan 2022 Benjamin Remy, Francois Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, Tim Schrabback

We introduce a novel methodology allowing for efficient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, and relying on simulations for defining a fully non-Gaussian prior.

Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons

1 code implementation NeurIPS 2021 Wenbo Ren, Jia Liu, Ness Shroff

Here, a multi-wise comparison takes $m$ items as input and returns a (noisy) result about the best item (the winner feedback) or the order of these items (the full-ranking feedback).

Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning

no code implementations NeurIPS 2021 Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu

To our knowledge, this paper is the first work that achieves both $\mathcal{O}(\epsilon^{-2})$ sample complexity and $\mathcal{O}(\epsilon^{-2})$ communication complexity in decentralized policy evaluation for cooperative MARL.

Multi-agent Reinforcement Learning Reinforcement Learning (RL) +1

A global convergence theory for deep ReLU implicit networks via over-parameterization

no code implementations ICLR 2022 Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, Hongyang Gao

Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectures.

Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward

no code implementations ICLR 2022 FNU Hairi, Jia Liu, Songtao Lu

In this paper, we establish the first finite-time convergence result of the actor-critic algorithm for fully decentralized multi-agent reinforcement learning (MARL) problems with average reward.

Multi-agent Reinforcement Learning reinforcement-learning +1

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

1 code implementation17 Sep 2021 Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather

What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field.

Compiler Optimization OpenAI Gym

Anarchic Federated Learning

no code implementations23 Aug 2021 Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu

To satisfy the need for flexible worker participation, we consider a new FL paradigm called "Anarchic Federated Learning" (AFL) in this paper.

Federated Learning

Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach

no code implementations25 Jul 2021 Fengjiao Li, Jia Liu, Bo Ji

Considering the achieved training accuracy of the global model as the utility of the selected workers, which is typically a monotone submodular function, we formulate the worker selection problem as a new multi-round monotone submodular maximization problem with cardinality and fairness constraints.

Fairness Federated Learning

The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest

no code implementations4 Jul 2021 Ziwei Cong, Jia Liu, Puneet Manchanda

Over the post-livestream period, the demand for the recorded version is still sensitive to price, but much less than in the pre-livestream period.

STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

no code implementations NeurIPS 2021 Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, Pramod K. Varshney

Despite extensive research, for a generic non-convex FL problem, it is not clear, how to choose the WNs' and the server's update directions, the minibatch sizes, and the local update frequency, so that the WNs use the minimum number of samples and communication rounds to achieve the desired solution.

Federated Learning

CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning

no code implementations14 Jun 2021 Haibo Yang, Jia Liu, Elizabeth S. Bentley

This matches the convergence rate of distributed/federated learning without compression, thus achieving high communication efficiency while not sacrificing learning accuracy in FL.

Federated Learning

Incentivized Bandit Learning with Self-Reinforcing User Preferences

no code implementations19 May 2021 Tianchen Zhou, Jia Liu, Chaosheng Dong, Jingyuan Deng

In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences.

Recommendation Systems

GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning

no code implementations4 May 2021 Xin Zhang, Jia Liu, Zhengyuan Zhu, Elizabeth S. Bentley

Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity.

Data Poisoning Attacks and Defenses to Crowdsourcing Systems

no code implementations18 Feb 2021 Minghong Fang, Minghao Sun, Qi Li, Neil Zhenqiang Gong, Jin Tian, Jia Liu

Our empirical results show that the proposed defenses can substantially reduce the estimation errors of the data poisoning attacks.

Data Poisoning

Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

no code implementations ICLR 2021 Haibo Yang, Minghong Fang, Jia Liu

Our results also reveal that the local steps in FL could help the convergence and show that the maximum number of local steps can be improved to $T/m$ in full worker participation.

Federated Learning Open-Ended Question Answering

Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks

no code implementations16 Jan 2021 Jia Liu, Yaochu Jin

Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans.

Distributionally robust second-order stochastic dominance constrained optimization with Wasserstein ball

no code implementations4 Jan 2021 Yu Mei, Jia Liu, Zhiping Chen

We consider a distributionally robust second-order stochastic dominance constrained optimization problem.

Optimization and Control 90C15, 91B70, 90C31, 90-08

FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

1 code implementation27 Dec 2020 Xiaoyu Cao, Minghong Fang, Jia Liu, Neil Zhenqiang Gong

Finally, the service provider computes the average of the normalized local model updates weighted by their trust scores as a global model update, which is used to update the global model.

Federated Learning

$κ$TNG: Effect of Baryonic Processes on Weak Lensing with IllustrisTNG Simulations

1 code implementation19 Oct 2020 Ken Osato, Jia Liu, Zoltán Haiman

The $\kappa$TNG suite includes 10, 000 realisations of $5 \times 5 \, \mathrm{deg}^2$ maps for 40 source redshifts up to $z_s = 2. 6$, well covering the range of interest for existing and upcoming weak lensing surveys.

Cosmology and Nongalactic Astrophysics

The Scale of Superpartner Masses and Electroweakino Searches at the High-Luminosity LHC

no code implementations26 Aug 2020 Jia Liu, Navin McGinnis, Carlos E. M. Wagner, Xiao-Ping Wang

Searches for weakly interacting particles is one of the main goals of the high luminosity LHC run.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Multi-Armed Bandits with Local Differential Privacy

no code implementations6 Jul 2020 Wenbo Ren, Xingyu Zhou, Jia Liu, Ness B. Shroff

To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee.

Multi-Armed Bandits

The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons

1 code implementation ICML 2020 Wenbo Ren, Jia Liu, Ness B. Shroff

From a given set of items, the learner can make pairwise comparisons on every pair of items, and each comparison returns an independent noisy result about the preferred item.

Active Learning

Re-examining the Solar Axion Explanation for the XENON1T Excess

no code implementations25 Jun 2020 Christina Gao, Jia Liu, Lian-Tao Wang, Xiao-Ping Wang, Wei Xue, Yi-Ming Zhong

Meanwhile, they can also scatter with the atoms through the inverse Primakoff process via the axion-photon coupling, which emits a photon and mimics the electronic recoil signals.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Transformation Importance with Applications to Cosmology

2 code implementations4 Mar 2020 Chandan Singh, Wooseok Ha, Francois Lanusse, Vanessa Boehm, Jia Liu, Bin Yu

Machine learning lies at the heart of new possibilities for scientific discovery, knowledge generation, and artificial intelligence.

Toward Low-Cost and Stable Blockchain Networks

no code implementations19 Feb 2020 Minghong Fang, Jia Liu

To address the high mining cost problem of blockchain networks, in this paper, we propose a blockchain mining resources allocation algorithm to reduce the mining cost in PoW-based (proof-of-work-based) blockchain networks.

Influence Function based Data Poisoning Attacks to Top-N Recommender Systems

no code implementations19 Feb 2020 Minghong Fang, Neil Zhenqiang Gong, Jia Liu

Given the number of fake users the attacker can inject, we formulate the crafting of rating scores for the fake users as an optimization problem.

Data Poisoning Recommendation Systems

Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization

no code implementations15 Jan 2020 Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu

Further, we show that the iteration complexity of the proposed method is $O(n\varepsilon^{-2})$ to achieve $\epsilon$-stationary point, where $n$ is the number of blocks of coordinates.


Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach

no code implementations12 Jan 2020 Xin Zhang, Minghong Fang, Jia Liu, Zhengyuan Zhu

In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing.

Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems

no code implementations16 Dec 2019 Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu

In the applications of signal processing and data analytics, there is a wide class of non-convex problems whose objective function is freed from the common global Lipschitz continuous gradient assumption (e. g., the nonnegative matrix factorization (NMF) problem).

Detecting Cyberattacks in Industrial Control Systems Using Online Learning Algorithms

no code implementations8 Dec 2019 Guangxia Lia, Yulong Shena, Peilin Zhaob, Xiao Lu, Jia Liu, Yangyang Liu, Steven C. H. Hoi

Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information.

Continuous Control Intrusion Detection

FDDWNet: A Lightweight Convolutional Neural Network for Real-time Sementic Segmentation

1 code implementation2 Nov 2019 Jia Liu, Quan Zhou, Yong Qiang, Bin Kang, Xiaofu Wu, Baoyu Zheng

The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.

Segmentation Semantic Segmentation

Hierarchical Feature-Aware Tracking

no code implementations13 Oct 2019 Wenhua Zhang, Licheng Jiao, Jia Liu

Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated.

Visual Tracking

Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach

no code implementations10 Sep 2019 Haibo Yang, Xin Zhang, Minghong Fang, Jia Liu

In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who could send arbitrary information to the parameter server to disrupt the training process.

Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach

no code implementations28 May 2019 Xin Zhang, Jia Liu, Zhengyuan Zhu

In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network.


Nucleus Neural Network: A Data-driven Self-organized Architecture

no code implementations8 Apr 2019 Jia Liu, Maoguo Gong, Haibo He

In this paper, we propose a nucleus neural network (NNN) and corresponding connecting architecture learning method.


Combinatorial Sleeping Bandits with Fairness Constraints

no code implementations15 Jan 2019 Fengjiao Li, Jia Liu, Bo Ji

To tackle this new problem, we extend an online learning algorithm, UCB, to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints.


Exploring $k$ out of Top $ρ$ Fraction of Arms in Stochastic Bandits

no code implementations28 Oct 2018 Wenbo Ren, Jia Liu, Ness Shroff

Results in this paper provide up to $\rho n/k$ reductions compared with the "$k$-exploration" algorithms that focus on finding the (PAC) best $k$ arms out of $n$ arms.

Poisoning Attacks to Graph-Based Recommender Systems

no code implementations11 Sep 2018 Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, Jia Liu

To address the challenge, we formulate the poisoning attacks as an optimization problem, solving which determines the rating scores for the fake users.

Recommendation Systems

Multiobjective Optimization Training of PLDA for Speaker Verification

2 code implementations25 Aug 2018 Liang He, Xianhong Chen, Can Xu, Jia Liu

Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.

Multiobjective Optimization Text-Independent Speaker Verification

PAC Ranking from Pairwise and Listwise Queries: Lower Bounds and Upper Bounds

no code implementations8 Jun 2018 Wenbo Ren, Jia Liu, Ness B. Shroff

For the PAC top-$k$ ranking problem, we derive a lower bound on the sample complexity (aka number of queries), and propose an algorithm that is sample-complexity-optimal up to an $O(\log(k+l)/\log{k})$ factor.

Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning

no code implementations24 May 2018 Xin Zhang, Jia Liu, Zhengyuan Zhu

Understanding the convergence performance of asynchronous stochastic gradient descent method (Async-SGD) has received increasing attention in recent years due to their foundational role in machine learning.

Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time

no code implementations23 May 2018 Hejian Sang, Jia Liu

In this paper, we propose a new adaptive stochastic gradient Langevin dynamics (ASGLD) algorithmic framework and its two specialized versions, namely adaptive stochastic gradient (ASG) and adaptive gradient Langevin dynamics(AGLD), for non-convex optimization problems.

Generative Steganography by Sampling

no code implementations26 Apr 2018 Jia Liu, Yu Lei, Yan Ke, Jun Li, Min-qing Zhang, Xiaoyuan Yan

In this paper, a new data-driven information hiding scheme called generative steganography by sampling (GSS) is proposed.

Image Inpainting

Coverless Information Hiding Based on Generative adversarial networks

no code implementations18 Dec 2017 Ming-ming Liu, Min-qing Zhang, Jia Liu, Ying-nan Zhang, Yan Ke

The main idea of the method is that the class label of generative adversarial networks is replaced with the secret information as a driver to generate hidden image directly, and then extract the secret information from the hidden image through the discriminator.

Cryptography and Security Multimedia

Generative Steganography with Kerckhoffs' Principle

no code implementations14 Nov 2017 Yan Ke, Min-qing Zhang, Jia Liu, Tingting Su, Xiaoyuan Yang

The secret messages can be outputted by the generator if and only if the extraction key and the cover image are both inputted.


Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification

no code implementations14 Jul 2017 Yi Liu, Liang He, Yao Tian, Zhuzi Chen, Jia Liu, Michael T. Johnson

Additionally, we also find that even though bottleneck features perform well for text-independent speaker verification, they do not outperform MFCCs on the most challenging Imposter-Correct trials on RedDots.

Speaker Identification Speaker Recognition +3

Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

1 code implementation8 May 2017 Juntao Gao, Yulong Shen, Jia Liu, Minoru Ito, Norio Shiratori

Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion.

Networking and Internet Architecture

Lepton Jets from Radiating Dark Matter

1 code implementation27 May 2015 Malte Buschmann, Joachim Kopp, Jia Liu, Pedro A. N. Machado

In this paper, we discuss lepton jets as a promising signature of an extended dark sector.

High Energy Physics - Phenomenology

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