Search Results for author: Xue Liu

Found 76 papers, 23 papers with code

Collaborative Performance Prediction for Large Language Models

1 code implementation1 Jul 2024 Qiyuan Zhang, Fuyuan Lyu, Xue Liu, Chen Ma

The pioneering scaling law on downstream works demonstrated intrinsic similarities within model families and utilized such similarities for performance prediction.

RAEE: A Training-Free Retrieval-Augmented Early Exiting Framework for Efficient Inference

no code implementations24 May 2024 Lianming Huang, Shangyu Wu, Yufei Cui, Ying Xiong, Xue Liu, Tei-Wei Kuo, Nan Guan, Chun Jason Xue

Finally, based on the pre-built retrieval database, RAEE leverages the retrieved similar data's exiting information to guide the backbone model to exit at the layer, which is predicted by the approximated distribution.

Language Modelling Large Language Model +1

Design Editing for Offline Model-based Optimization

no code implementations22 May 2024 Ye Yuan, Youyuan Zhang, Can Chen, Haolun Wu, Zixuan Li, Jianmo Li, James J. Clark, Xue Liu

We hence propose the second phase, existing design editing, to directly incorporate the high-scoring features from the offline dataset into design generation.

The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks

1 code implementation14 May 2024 Ziquan Liu, Yufei Cui, Yan Yan, Yi Xu, Xiangyang Ji, Xue Liu, Antoni B. Chan

In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient health and road safety, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks and reliable uncertainty quantification in decision-making.

Adversarial Defense Adversarial Robustness +4

Modelling infectious disease transmission dynamics in conference environments: An individual-based approach

no code implementations17 Apr 2024 Xue Liu, Yue Deng, Jingying Huang, Yuhong Zhang, Jinzhi Lei

The global public health landscape is perpetually challenged by the looming threat of infectious diseases.

Toward a Better Understanding of Fourier Neural Operators: Analysis and Improvement from a Spectral Perspective

no code implementations10 Apr 2024 Shaoxiang Qin, Fuyuan Lyu, Wenhui Peng, Dingyang Geng, Ju Wang, Naiping Gao, Xue Liu, Liangzhu Leon Wang

In solving partial differential equations (PDEs), Fourier Neural Operators (FNOs) have exhibited notable effectiveness compared to Convolutional Neural Networks (CNNs).

Ensemble Learning

Learning to Extract Structured Entities Using Language Models

no code implementations6 Feb 2024 Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra

Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text.

ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion

no code implementations4 Jan 2024 Shangyu Wu, Ying Xiong, Yufei Cui, Xue Liu, Buzhou Tang, Tei-Wei Kuo, Chun Jason Xue

Retrieval-based augmentations (RA) incorporating knowledge from an external database into language models have greatly succeeded in various knowledge-intensive (KI) tasks.

Natural Language Understanding Neural Architecture Search +5

Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation

1 code implementation30 Dec 2023 Can Chen, Hao liu, Zeming Liu, Xue Liu, Dejing Dou

In this paper, we propose Dual-space Hierarchical Learning (DHL) to leverage multi-level goal sequences and their hierarchical relationships for conversational recommendation.

Recommendation Systems Representation Learning

Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation

no code implementations22 Dec 2023 Chengming Hu, Haolun Wu, Xuan Li, Chen Ma, Xi Chen, Jun Yan, Boyu Wang, Xue Liu

A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner.

Bilevel Optimization Click-Through Rate Prediction +2

Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization

no code implementations6 Dec 2023 Jimmy Li, Igor Kozlov, Di wu, Xue Liu, Gregory Dudek

This coincides with a rapid increase in the number of cell sites worldwide, driven largely by dramatic growth in cellular network traffic.

Anomaly Detection

Towards Automated Negative Sampling in Implicit Recommendation

no code implementations6 Nov 2023 Fuyuan Lyu, Yaochen Hu, Xing Tang, Yingxue Zhang, Ruiming Tang, Xue Liu

Hence, we propose a hypothesis that the negative sampler should align with the capacity of the recommendation models as well as the statistics of the datasets to achieve optimal performance.

AutoML

Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval

no code implementations31 Oct 2023 Haolun Wu, Ofer Meshi, Masrour Zoghi, Fernando Diaz, Xue Liu, Craig Boutilier, Maryam Karimzadehgan

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems.

Diversity GPR +3

Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network

1 code implementation NeurIPS 2023 Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Weihong Luo, Liang Chen, Xiuqiang He, Xue Liu

In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks.

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching

no code implementations5 Oct 2023 Junliang Luo, Yi Tian Xu, Di wu, Michael Jenkin, Xue Liu, Gregory Dudek

In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics.

Teacher-Student Architecture for Knowledge Distillation: A Survey

no code implementations8 Aug 2023 Chengming Hu, Xuan Li, Dan Liu, Haolun Wu, Xi Chen, Ju Wang, Xue Liu

Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement.

Knowledge Distillation regression

Fourier neural operator for real-time simulation of 3D dynamic urban microclimate

1 code implementation8 Aug 2023 Wenhui Peng, Shaoxiang Qin, Senwen Yang, Jianchun Wang, Xue Liu, Liangzhu Leon Wang

We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions.

An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics

no code implementations26 Jun 2023 Xue Liu, Dan Sun, Wei Wei, Zhiming Zheng

This approach incorporates the physics-based heat kernel and DropNode technique to transform each static graph into a sequence of temporal ones.

Graph Classification

Think Before You Act: Decision Transformers with Working Memory

1 code implementation24 May 2023 Jikun Kang, Romain Laroche, Xingdi Yuan, Adam Trischler, Xue Liu, Jie Fu

We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training.

Atari Games Decision Making +2

Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios

no code implementations22 Mar 2023 Yi Tian Xu, Jimmy Li, Di wu, Michael Jenkin, Seowoo Jang, Xue Liu, Gregory Dudek

When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training.

Reinforcement Learning (RL)

Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks

no code implementations14 Mar 2023 Jikun Kang, Di wu, Ju Wang, Ekram Hossain, Xue Liu, Gregory Dudek

In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs.

Towards Improved Illicit Node Detection with Positive-Unlabelled Learning

1 code implementation4 Mar 2023 Junliang Luo, Farimah Poursafaei, Xue Liu

Detecting illicit nodes on blockchain networks is a valuable task for strengthening future regulation.

Graph Representation Learning

Ternary Quantization: A Survey

no code implementations2 Mar 2023 Dan Liu, Xue Liu

Numerous research efforts have been made to compress neural network models with faster inference and higher accuracy.

Quantization

Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks

no code implementations3 Feb 2023 Igor Kozlov, Dmitriy Rivkin, Wei-Di Chang, Di wu, Xue Liu, Gregory Dudek

Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance.

Change Detection Self-Supervised Learning

Optimizing Feature Set for Click-Through Rate Prediction

1 code implementation26 Jan 2023 Fuyuan Lyu, Xing Tang, Dugang Liu, Liang Chen, Xiuqiang He, Xue Liu

Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates.

Click-Through Rate Prediction

Result Diversification in Search and Recommendation: A Survey

1 code implementation29 Dec 2022 Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Bowei He, Bhaskar Mitra, Xue Liu

Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.

Diversity Retrieval

Hyperspherical Quantization: Toward Smaller and More Accurate Models

no code implementations24 Dec 2022 Dan Liu, Xi Chen, Chen Ma, Xue Liu

Model quantization enables the deployment of deep neural networks under resource-constrained devices.

Quantization

Hyperspherical Loss-Aware Ternary Quantization

no code implementations24 Dec 2022 Dan Liu, Xue Liu

Most of the existing works use projection functions for ternary quantization in discrete space.

Image Classification object-detection +2

Pruning On-the-Fly: A Recoverable Pruning Method without Fine-tuning

no code implementations24 Dec 2022 Dan Liu, Xue Liu

Most existing pruning works are resource-intensive, requiring retraining or fine-tuning of the pruned models for accuracy.

Iterative Data Refinement for Self-Supervised MR Image Reconstruction

no code implementations24 Nov 2022 Xue Liu, Juan Zou, Xiawu Zheng, Cheng Li, Hairong Zheng, Shanshan Wang

Then, we design an effective self-supervised training data refinement method to reduce this data bias.

Image Reconstruction

AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments

no code implementations21 Nov 2022 Tim Tianyi Yang, Tom Tianze Yang, Andrew Liu, Jie Tang, Na An, Shaoshan Liu, Xue Liu

Also, through the AICOM-MP project, we have generalized a methodology of developing health AI technologies for AMCs to allow universal access even in resource-constrained environments.

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

no code implementations11 Nov 2022 Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.

Decision Making Recommendation Systems +2

Teacher-Student Architecture for Knowledge Learning: A Survey

no code implementations28 Oct 2022 Chengming Hu, Xuan Li, Dan Liu, Xi Chen, Ju Wang, Xue Liu

To tackle this issue, Teacher-Student architectures were first utilized in knowledge distillation, where simple student networks can achieve comparable performance to deep teacher networks.

Knowledge Distillation Multi-Task Learning

Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepancies

no code implementations2 Oct 2022 Xue Liu, Dan Sun, Xiaobo Cao, Hao Ye, Wei Wei

Graph embedding provides a feasible methodology to conduct pattern classification for graph-structured data by mapping each data into the vectorial space.

Graph Embedding

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction

1 code implementation9 Aug 2022 Fuyuan Lyu, Xing Tang, Hong Zhu, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Xue Liu

To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models.

Click-Through Rate Prediction Recommendation Systems

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

1 code implementation2 Aug 2022 Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates

In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.

Bilevel Optimization Graph Neural Network

Gradient-based Bi-level Optimization for Deep Learning: A Survey

no code implementations24 Jul 2022 Can Chen, Xi Chen, Chen Ma, Zixuan Liu, Xue Liu

In this survey, we first give a formal definition of the gradient-based bi-level optimization.

Hyperparameter Optimization

Confidence-Guided Unsupervised Domain Adaptation for Cerebellum Segmentation

no code implementations14 Jun 2022 Xuan Li, Paule-J Toussaint, Alan Evans, Xue Liu

To dispense with the manual annotation requirement, we propose to train a model to adaptively transfer the annotation from the cerebellum on the Allen Brain Human Brain Atlas to the BigBrain in an unsupervised manner, taking into account the different staining and spacing between sections.

Segmentation Semantic Segmentation +1

Unbiased Implicit Feedback via Bi-level Optimization

no code implementations31 May 2022 Can Chen, Chen Ma, Xi Chen, Sirui Song, Hao liu, Xue Liu

Recent works reveal a huge gap between the implicit feedback and user-item relevance due to the fact that implicit feedback is also closely related to the item exposure.

Recommendation Systems

Joint Multisided Exposure Fairness for Recommendation

1 code implementation29 Apr 2022 Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu

Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.

Exposure Fairness Information Retrieval +2

Structure-aware Protein Self-supervised Learning

1 code implementation6 Apr 2022 Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, Dejing Dou

Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning.

Graph Neural Network Protein Language Model +2

Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey

no code implementations20 Mar 2022 Yuecai Zhu, Fuyuan Lyu, Chengming Hu, Xi Chen, Xue Liu

However, the temporal information embedded in the dynamic graphs brings new challenges in analyzing and deploying them.

Decoder Graph Learning

Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods

no code implementations20 Nov 2021 Yang Hu, Zhui Zhu, Sirui Song, Xue Liu, Yang Yu

Experimental results in an exemplary environment show that our MARL approach is able to demonstrate the effectiveness and necessity of restrictions on individual liberty for collaborative supply of public goods.

Multi-agent Reinforcement Learning

Generalized Data Weighting via Class-level Gradient Manipulation

1 code implementation29 Oct 2021 Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue Liu, Hao liu, Dejing Dou

To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.

Learning Multi-Objective Curricula for Robotic Policy Learning

1 code implementation6 Oct 2021 Jikun Kang, Miao Liu, Abhinav Gupta, Chris Pal, Xue Liu, Jie Fu

Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL).

Reinforcement Learning (RL)

MOBA: Multi-teacher Model Based Reinforcement Learning

no code implementations29 Sep 2021 Jikun Kang, Xi Chen, Ju Wang, Chengming Hu, Xue Liu, Gregory Dudek

Results show that, compared with SOTA model-free methods, our method can improve the data efficiency and system performance by up to 75% and 10%, respectively.

Decision Making Knowledge Distillation +4

Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning

no code implementations29 Sep 2021 Di wu, Tianyu Li, David Meger, Michael Jenkin, Xue Liu, Gregory Dudek

Unfortunately, most online reinforcement learning algorithms require a large number of interactions with the environment to learn a reliable control policy.

Continuous Control Imitation Learning +3

CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication

no code implementations23 Aug 2021 Xuan Li, Liqiong Chang, Xue Liu

To this end, this paper proposes a framework to assess the impact of the near-duplicate images on CNN training performance, called CE-Dedup.

Image Classification

Pruning Ternary Quantization

no code implementations23 Jul 2021 Dan Liu, Xi Chen, Jie Fu, Chen Ma, Xue Liu

To simultaneously optimize bit-width, model size, and accuracy, we propose pruning ternary quantization (PTQ): a simple, effective, symmetric ternary quantization method.

Image Classification Model Compression +3

A Graph Data Augmentation Strategy with Entropy Preservation

no code implementations13 Jul 2021 Xue Liu, Dan Sun, Wei Wei

Considering the preservation of graph entropy, we propose an effective strategy to generate randomly perturbed training data but maintain both graph topology and graph entropy.

Data Augmentation Node Classification

Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation

no code implementations6 May 2021 Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu

To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee.

Fairness Recommendation Systems

Graph Classification Based on Skeleton and Component Features

no code implementations2 Feb 2021 Xue Liu, Wei Wei, Xiangnan Feng, Xiaobo Cao, Dan Sun

Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation.

General Classification Graph Classification +1

Variational Nested Dropout

1 code implementation CVPR 2021 Yufei Cui, Yu Mao, Ziquan Liu, Qiao Li, Antoni B. Chan, Xue Liu, Tei-Wei Kuo, Chun Jason Xue

Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training.

Representation Learning

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.

Metric Learning Recommendation Systems

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates

To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.

Knowledge Graphs Recommendation Systems

Reinforced Epidemic Control: Saving Both Lives and Economy

1 code implementation4 Aug 2020 Sirui Song, Zefang Zong, Yong Li, Xue Liu, Yang Yu

Saving lives or economy is a dilemma for epidemic control in most cities while smart-tracing technology raises people's privacy concerns.

Graph Neural Network reinforcement-learning +1

Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs

no code implementations31 Jul 2020 Xing Li, Wei Wei, Xiangnan Feng, Xue Liu, Zhiming Zheng

The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction , etc.

Clustering Graph Neural Network +3

Feature Statistics Guided Efficient Filter Pruning

no code implementations21 May 2020 Hang Li, Chen Ma, Wei Xu, Xue Liu

Building compact convolutional neural networks (CNNs) with reliable performance is a critical but challenging task, especially when deploying them in real-world applications.

Diversity

Memory Augmented Graph Neural Networks for Sequential Recommendation

1 code implementation26 Dec 2019 Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates

In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.

Graph Neural Network Sequential Recommendation

Connecting First and Second Order Recurrent Networks with Deterministic Finite Automata

1 code implementation12 Nov 2019 Qinglong Wang, Kaixuan Zhang, Xue Liu, C. Lee Giles

We propose an approach that connects recurrent networks with different orders of hidden interaction with regular grammars of different levels of complexity.

Shapley Homology: Topological Analysis of Sample Influence for Neural Networks

no code implementations15 Oct 2019 Kaixuan Zhang, Qinglong Wang, Xue Liu, C. Lee Giles

This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models.

BIG-bench Machine Learning Data Poisoning

Hierarchical Gating Networks for Sequential Recommendation

2 code implementations21 Jun 2019 Chen Ma, Peng Kang, Xue Liu

However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed.

 Ranked #1 on Recommendation Systems on Amazon-CDs (Recall@10 metric)

Sequential Recommendation

Gated Attentive-Autoencoder for Content-Aware Recommendation

1 code implementation7 Dec 2018 Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, Xue Liu

In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder.

Product Recommendation Recommendation Systems

Verification of Recurrent Neural Networks Through Rule Extraction

no code implementations14 Nov 2018 Qinglong Wang, Kaixuan Zhang, Xue Liu, C. Lee Giles

The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured.

Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

1 code implementation27 Sep 2018 Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu

To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel.

Decoder Recommendation Systems

A Comparative Study of Rule Extraction for Recurrent Neural Networks

no code implementations16 Jan 2018 Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles

Then we empirically evaluate different recurrent networks for their performance of DFA extraction on all Tomita grammars.

An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks

no code implementations29 Sep 2017 Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles

Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis.

Medical Diagnosis

Learning Adversary-Resistant Deep Neural Networks

no code implementations5 Dec 2016 Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles

Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design.

Autonomous Vehicles

Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks

no code implementations6 Oct 2016 Qinglong Wang, Wenbo Guo, Alexander G. Ororbia II, Xinyu Xing, Lin Lin, C. Lee Giles, Xue Liu, Peng Liu, Gang Xiong

Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles.

Autonomous Vehicles Dimensionality Reduction +2

Adversary Resistant Deep Neural Networks with an Application to Malware Detection

no code implementations5 Oct 2016 Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, Xue Liu

However, after a thorough analysis of the fundamental flaw in DNNs, we discover that the effectiveness of current defenses is limited and, more importantly, cannot provide theoretical guarantees as to their robustness against adversarial sampled-based attacks.

Information Retrieval Malware Detection +3

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