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Found 106 papers, 25 papers with code
Zero-bias Deep Neural Network for Quickest RF Signal Surveillance
In this paper, we provide a deep learning framework for RF signal surveillance.
One-Bit Matrix Completion with Differential Privacy
Matrix completion is a prevailing collaborative filtering method for recommendation systems that requires the data offered by users to provide personalized service.
Applying Differential Privacy to Tensor Completion
Tensor completion aims at filling the missing or unobserved entries based on partially observed tensors.
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System
In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation.
A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks
Biphasic facial age translation aims at predicting the appearance of the input face at any age.
To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection
Despite of the success of previous works, explorations on an effective training strategy for the saliency network and accurate matches between image-level annotations and salient objects are still inadequate.
Mining Contextual Information Beyond Image for Semantic Segmentation
To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations.
Learning to Detect: A Data-driven Approach for Network Intrusion Detection
With massive data being generated daily and the ever-increasing interconnectivity of the world's Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security.
Network Intrusion Detection
Unsupervised Representation Learning
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously.
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition
The task of skeleton-based action recognition remains a core challenge in human-centred scene understanding due to the multiple granularities and large variation in human motion.
Flip Learning: Erase to Segment
Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome manual annotation.
Multi-agent Reinforcement Learning
Weakly supervised segmentation
Distributed Learning for Time-varying Networks: A Scalable Design
The wireless network is undergoing a trend from "onnection of things" to "connection of intelligence".
A Novel Interactive Two-stage Joint Retail Electricity Market for Multiple Microgrids
To accommodate the advent of microgrids (MG) managing distributed energy resources (DER) in distribution systems, an interactive two-stage joint retail electricity market mechanism is proposed to provide an effective platform for these prosumers to proactively join in retail transactions.
Deep Iterative 2D/3D Registration
A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy.
Bayesian Atlas Building with Hierarchical Priors for Subject-specific Regularization
This paper presents a novel hierarchical Bayesian model for unbiased atlas building with subject-specific regularizations of image registration.
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration
Our experimental results show that the proposed method is able to perform high-quality restoration for unconstrained underwater images without any supervision.
Seeing in Extra Darkness Using a Deep-Red Flash
We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source.
Detect and remove watermark in deep neural networks via generative adversarial networks
Experimental evaluations on the MNIST and CIFAR10 datasets demonstrate that, the proposed method can effectively remove about 98% of the watermark in DNN models, as the watermark retention rate reduces from 100% to less than 2% after applying the proposed attack.
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations
Experimental results show that, the backdoor detection rate of the proposed defense method is 99. 63%, 99. 76% and 99. 91% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively.
AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryption
Moreover, the proposed method only needs to encrypt an extremely low number of parameters, and the proportion of the encrypted parameters of all the model's parameters is as low as 0. 000205%.
One Shot Face Swapping on Megapixels
Extensive experiments demonstrate the superiority of MegaFS and the first megapixel level face swapping database is released for research on DeepFake detection and face image editing in the public domain.
Ranked #3 on
Face Swapping
on FaceForensics++
Class-Incremental Learning for Wireless Device Identification in IoT
The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services.
Unsupervised Multi-Source Domain Adaptation for Person Re-Identification
Although achieving great success, most of them only use limited data from a single-source domain for model pre-training, making the rich labeled data insufficiently exploited.
Estimating Egocentric 3D Human Pose in Global Space
Furthermore, these methods suffer from limited accuracy and temporal instability due to ambiguities caused by the monocular setup and the severe occlusion in a strongly distorted egocentric perspective.
Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images
To address these challenges, in this paper, we propose a method to protect the intellectual properties of DNN models by using an additional class and steganographic images.
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World
In this paper, we propose a robust physical backdoor attack method, PTB (physical transformations for backdoors), to implement the backdoor attacks against deep learning models in the real physical world.
Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT
Abnormality detection is essential to the performance of safety-critical and latency-constrained systems.
Drug Discovery Approaches using Quantum Machine Learning
Traditional drug discovery pipeline takes several years and cost billions of dollars.
Robust Reinforcement Learning under model misspecification
Reinforcement learning has achieved remarkable performance in a wide range of tasks these days.
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks
We not only verify the performance gain achieved, but also provide implementation-friend designs, i. e., a scalable neural network design for the agent and a virtual environment training framework.
ActiveGuard: An Active DNN IP Protection Technique via Adversarial Examples
For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of the DNN model will not be affected.
Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process.
Learning the Update Operator for 2D/3D Image Registration
We aim to address this gap by incorporating traditional methods in deep neural networks using known operator learning.
Incentive-based Decentralized Routing for Connected and Autonomous Vehicles using Information Propagation
The first stage incorporates a decentralized local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles' knowledge of local traffic information.
Autonomous Vehicles
Physics and Society
Computer Science and Game Theory
Systems and Control
Systems and Control
Two-sided Dirichlet heat estimates of symmetric stable processes on horn-shaped regions
In this paper, we consider symmetric $\alpha$-stable processes on (unbounded) horn-shaped regions which are non-uniformly $C^{1, 1}$ near infinity.
Probability
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey
In this paper, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators.
Systematic electrochemical etching of various metal tips for tunneling spectroscopy and scanning probe microscopy
To ensure the reproducibility of experimental results, the fabrication of tips should be standardized, and a controllable and convenient system should be set up.
Materials Science
Adaptive Remote Sensing Image Attribute Learning for Active Object Detection
In recent years, deep learning methods bring incredible progress to the field of object detection.
MFNet: Multi-Filter Directive Network for Weakly Supervised Salient Object Detection
The multiple accurate cues from multiple DFs are then simultaneously propagated to the saliency network with a multi-guidance loss.
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive-based learning frameworks.
Detection of magnetic gap in the topological surface states of MnBi2Te4
An essential ingredient to realize these quantum states is the magnetic gap in the topological surface states induced by the out-of-plane ferromagnetism on the surface of MnBi2Te4.
Materials Science
XCloud-MoDern: An Artificial Intelligence Cloud for Accelerated NMR Spectroscopy
For accelerated multi-dimensional NMR spectroscopy, non-uniform sampling is a powerful approach but requires sophisticated algorithms to reconstruct undersampled data.
Distant Domain Transfer Learning for Medical Imaging
In this paper, we propose a distant domain transfer learning (DDTL) method for medical image classification.
Compressive Privatization: Sparse Distribution Estimation under Locally Differentially Privacy
However, they are optimal only in the worst-case sense; their sample complexity is proportional to the size of the entire universe, which could be huge in practice (e. g., all IP addresses).
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems
To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager.
Group Contextual Encoding for 3D Point Clouds
In this work, we extended the contextual encoding layer that was originally designed for 2D tasks to 3D Point Cloud scenarios.
Deep Learning for Regularization Prediction in Diffeomorphic Image Registration
This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration.
SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images
After being injected with the perturbation, the social image can easily fool the object detector, while its visual quality will not be degraded.
3D Invisible Cloak
on person stealth attacks, and propose 3D transformations to generate 3D invisible cloak.
NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors
Experimental results demonstrate that, the generated adversarial examples are robust under various indoor and outdoor physical conditions, including different distances, angles, illuminations, and photographing.
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive learning frameworks.
Integrated Gallium Nitride Nonlinear Photonics
Thanks to its high nonlinearity and high refractive index contrast, GaN-on-insulator (GaNOI) is also a promising platform for nonlinear optical applications.
Optics Applied Physics
Robust MIMO Radar Waveform-Filter Design for Extended Target Detection in the Presence of Multipath
We resort to a sequential optimization procedure alternating between two SDR problems to optimize the covariance matrix of transmit waveform and receive filter, so we call the algorithm Duality Maximization Double Semi-Definite Relaxation(DMDSDR).
Biomedical named entity recognition using BERT in the machine reading comprehension framework
Using the sequence labeling framework to implement biomedical named entity recognition (BioNER) is currently a conventional method.
Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends
A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or unmanned aircraft system.
Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices
In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals.
Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement
In the first stage, heatmap regression network is applied to obtain a rough localization result, and a set of proposal keypoints, called guided points, are sampled.
TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition
Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues.
Quasi-Deterministic Channel Model for mmWaves: Mathematical Formalization and Validation
5G and beyond networks will use, for the first time ever, the millimeter wave (mmWave) spectrum for mobile communications.
Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.
Energy Model for UAV Communications: Experimental Validation and Model Generalization
In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible.
DeepFLASH: An Efficient Network for Learning-based Medical Image Registration
This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration.
Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis
Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences.
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Besides, the attack is further enhanced by adaptively tuning the translations of object and background.
Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation.
Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge
Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks.
Fast Color Constancy with Patch-wise Bright Pixels
In this paper, a learning-free color constancy algorithm called the Patch-wise Bright Pixels (PBP) is proposed.
ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities
The augmented images help to tell apart the edge gradients, edge pixels and non-edge ones in log-histogram, which contribute significantly to the feature extraction and color-ambiguity elimination, thereby advancing the accuracy of illuminant estimation.
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate.
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation
In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story.
Towards Good Practices for Video Object Segmentation
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning.
One-shot visual object segmentation
Semantic Segmentation
+1
FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces
In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis.
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning
Aspect category detection is an essential task for sentiment analysis and opinion mining.
Turán Problems for Vertex-disjoint Cliques in Multi-partite Hypergraphs
First, we consider a multi-partite version of the Erd\H{o}s matching conjecture and determine $ex_s(K_{n_1, \ldots, n_r}^{(s)}, kK_s^{(s)})$ for $n_1\geq s^3k^2+sr$.
Combinatorics
Realistic Channel Models Pre-training
In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.
Region-Manipulated Fusion Networks for Pancreatitis Recognition
Specifically, to effectively highlight the imperceptible lesion regions, a novel region-manipulated scheme in RMFN is proposed to force the lesion regions while weaken the non-lesion regions by ceaselessly aggregating the multi-scale local information onto feature maps.
Fractional-order Backpropagation Neural Networks: Modified Fractional-order Steepest Descent Method for Family of Backpropagation Neural Networks
This paper offers a novel mathematical approach, the modified Fractional-order Steepest Descent Method (FSDM) for training BackPropagation Neural Networks (BPNNs); this differs from the majority of the previous approaches and as such.
NIL: Learning Nonlinear Interpolants
Nonlinear interpolants have been shown useful for the verification of programs and hybrid systems in contexts of theorem proving, model checking, abstract interpretation, etc.
Deep Reinforcement Learning for Scheduling in Cellular Networks
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia.
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Motivation: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction.
Ranked #1 on
Named Entity Recognition
on BC4CHEMD
Re-Identification Supervised Texture Generation
The estimation of 3D human body pose and shape from a single image has been extensively studied in recent years.
Saec: Similarity-Aware Embedding Compression in Recommendation Systems
Production recommendation systems rely on embedding methods to represent various features.
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models
Accurate prediction of fading channel in future is essential to realize adaptive transmission and other methods that can save power and provide gains.
Stereoscopic Dark Flash for Low-light Photography
In this work, we present a camera configuration for acquiring "stereoscopic dark flash" images: a simultaneous stereo pair in which one camera is a conventional RGB sensor, but the other camera is sensitive to near-infrared and near-ultraviolet instead of R and B.
Rational function regression method for numerical analytic continuation
A simple method for numerical analytic continuation is developed.
Rational function regression method for numerical analytic continuation
A simple method for numerical analytic continuation is developed.
Learning Optimal Deep Projection of $^{18}$F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes.
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns.
Programmable Triangulation Light Curtains
A vehicle on a road or a robot in the field does not need a full-featured 3D depth sensor to detect potential collisions or monitor its blind spot.
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles
With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis.
Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
For single-vertebra registration, we achieve an accuracy of 0. 74$\pm$0. 26 mm and highly improved robustness.
Think Visually: Question Answering through Virtual Imagery
In this paper, we study the problem of geometric reasoning in the context of question-answering.
Neural Network Language Modeling with Letter-based Features and Importance Sampling
In this paper we describe an extension of the Kaldi software toolkit to support neural-based language modeling, intended for use in automatic speech recognition (ASR) and related tasks.
Ranked #27 on
Speech Recognition
on LibriSpeech test-other
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases
For word level task our best run achieved MAE 0. 545 (ranked 2nd), PCC 0. 892 (ranked 2nd) in valence prediction and MAE 0. 857 (ranked 1st), PCC 0. 678 (ranked 2nd) in arousal prediction.
Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths
Results In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task.
Reflectance Capture Using Univariate Sampling of BRDFs
A collocated setup provides only a 1-D "univariate" sampling of the 4-D BRDF.
Premise Selection for Theorem Proving by Deep Graph Embedding
We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture.
Ranked #1 on
Automated Theorem Proving
on HolStep (Unconditional)
Deep Metric Learning with Angular Loss
The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images.
Crowd Counting Considering Network Flow Constraints in Videos
This paper, for the first time, introduces a quadratic programming model with the network flow constraints to improve the accuracy of crowd counting.
Photometric Stereo With Small Angular Variations
This paper studies the effect of small angular variations in illumination directions to photometric stereo.
LiSens --- A Scalable Architecture for Video Compressive Sensing
The measurement rate of cameras that take spatially multiplexed measurements by using spatial light modulators (SLM) is often limited by the switching speed of the SLMs.
Cross-Modal Similarity Learning : A Low Rank Bilinear Formulation
A new approach to the problem has been raised which intends to match features of different modalities directly.
Recovery of Sparse Signals Using Multiple Orthogonal Least Squares
We study the problem of recovering sparse signals from compressed linear measurements.