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Found 105 papers, 32 papers with code
SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing
Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i. e., 7 types by SenseMag versus 4 types by the existing work in comparisons).
Cross-Vendor CT Image Data Harmonization Using CVH-CT
We propose a novel deep learning approach called CVH-CT for harmonizing CT images captured using scanners from different vendors.
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem
We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem.
sMGC: A Complex-Valued Graph Convolutional Network via Magnetic Laplacian for Directed Graphs
Recent advancements in Graph Neural Networks have led to state-of-the-art performance on representation learning of graphs for node classification.
M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining
Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 and Switch Transformer possessing hundreds of billions or even trillions of parameters.
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem
To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning
In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i. e., the pickup node must precede the pairing delivery node.
ACDC: Online Unsupervised Cross-Domain Adaptation
We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces -- a fully labeled source stream and an unlabeled target stream -- are learned together.
Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark
We expect the ERATO as well as our proposed SMTA to open up a new way for PERR task in video understanding and further improve the research of multi-modal fusion methodology.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Meta Gradient Adversarial Attack
In recent years, research on adversarial attacks has become a hot spot.
Exploring Structure Consistency for Deep Model Watermarking
However, little attention has been devoted to the protection of DNNs in image processing tasks.
Poison Ink: Robust and Invisible Backdoor Attack
As the image structure can keep its semantic meaning during the data transformation, such trigger pattern is inherently robust to data transformations.
Locality-aware Channel-wise Dropout for Occluded Face Recognition
Furthermore, by randomly dropping out several feature channels, our method can well simulate the occlusion of larger area.
Supply Chain Digital Twin Framework Design: An Approach of Supply Chain Operations Reference Model and System of Systems
This paper explains what is 'twined' in supply chain digital twin and how to 'twin' them to handle the spatio-temporal dynamic issue.
Impact of Rotary-Wing UAV Wobbling on Millimeter-wave Air-to-Ground Wireless Channel
Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the channel temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link.
SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
Further, negative items should be carefully sampled under the defined distribution, in order to avoid selecting an observed positive item in the training dataset.
CT Image Harmonization for Enhancing Radiomics Studies
While remarkable advances have been made in Computed Tomography (CT), capturing CT images with non-standardized protocols causes low reproducibility regarding radiomic features, forming a barrier on CT image analysis in a large scale.
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares.
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
M6-T: Exploring Sparse Expert Models and Beyond
Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling.
HINet: Half Instance Normalization Network for Image Restoration
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks.
Ranked #1 on
Single Image Deraining
on Rain100H
Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication
Specifically, we present the reversible watermarking problem of deep convolutional neural networks and utilize the pruning theory of model compression technology to construct a host sequence used for embedding watermarking information by histogram shift.
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
Radiotherapy is a treatment where radiation is used to eliminate cancer cells.
Deep Model Intellectual Property Protection via Deep Watermarking
By jointly training the target model and watermark embedding, the extra barrier can even be absorbed into the target model.
M6: A Chinese Multimodal Pretrainer
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.
Dynamic Virtual Graph Significance Networks for Predicting Influenza
In this study, we develop a novel method, Dynamic Virtual Graph Significance Networks (DVGSN), which can supervisedly and dynamically learn from similar "infection situations" in historical timepoints.
Meta-Path-Free Representation Learning on Heterogeneous Networks
To the best of our knowledge, this is the first attempt to break out of the confinement of meta-paths for representation learning on heterogeneous networks.
Spatio-Temporal Multi-step Prediction of Influenza Outbreaks
We compared the MAPEs of SVM, RF, LSTM models of predicting flu data of the 1-4 weeks ahead with and without other countries' flu data.
Spin and polarization effects on the nonlinear Breit-Wheeler pair production in laser-plasma interaction
The spin effect of electrons/positrons ($e^-$/$e^+$) and polarization effect of $\gamma$ photons are investigated in the interaction of two counter-propagating linearly polarized 10-PW-class laser pulses with a thin foil target.
Plasma Physics
An Improvement for Quantum Tunneling Radiation of Fermions in a Stationary Kerr-Newman Black Hole Spacetime
By introducing a specific etheric-like vector in the Dirac equation with Lorentz Invariance Violation (LIV) in the curved spacetime, an improved method for quantum tunneling radiation of fermions is proposed.
General Relativity and Quantum Cosmology High Energy Physics - Theory
BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions
Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.
Optical manipulation of electronic dimensionality in a quantum material
Exotic phenomenon can be achieved in quantum materials by confining electronic states into two dimensions.
Strongly Correlated Electrons Materials Science Superconductivity
TGCN: Time Domain Graph Convolutional Network for Multiple Objects Tracking
Multiple object tracking is to give each object an id in the video.
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning
To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation.
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems
We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems.
Attributes Aware Face Generation with Generative Adversarial Networks
Recent studies have shown remarkable success in face image generations.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Data Augmentation for Multiclass Utterance Classification -- A Systematic Study
Utterance classification is a key component in many conversational systems.
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
In this paper, we propose a novel neural network framework ESDF to tackle the above three challenges simultaneously.
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions
This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin.
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling
However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression.
Passport-aware Normalization for Deep Model Protection
Only when the model IP is suspected to be stolen by someone, the private passport-aware branch is added back for ownership verification.
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
A Critical Study on Data Leakage in Recommender System Offline Evaluation
We show that accessing to different amount of future data may improve or deteriorate a model's recommendation accuracy.
Low-frequency whistler waves excited by relativistic laser pulses
It is shown by multi-dimensional particle-in-cell simulations that intense secondary whistler waves with special vortex-like field topology can be excited by a relativistic laser pulse in the highly magnetized, near-critical density plasma.
Plasma Physics
Cascaded Semantic and Positional Self-Attention Network for Document Classification
In this work, we propose a new architecture to aggregate the two sources of information using cascaded semantic and positional self-attention network (CSPAN) in the context of document classification.
Low-Rank Reorganization via Proportional Hazards Non-negative Matrix Factorization Unveils Survival Associated Gene Clusters
Towards the goal of precision health and cancer treatments, the proposed algorithm can help understand and interpret high-dimensional heterogeneous genomics data with accurate identification of survival-associated gene clusters.
FrUITeR: A Framework for Evaluating UI Test Reuse
UI testing is tedious and time-consuming due to the manual effort required.
Software Engineering
Attentive Fusion Enhanced Audio-Visual Encoding for Transformer Based Robust Speech Recognition
The proposed method can sufficiently combine the two streams and weaken the over-reliance on the audio modality.
Wireless Performance Evaluation of Building Layouts: Closed-Form Computation of Figures of Merit
This paper presents a part of our ground-breaking work on evaluation of buildings in terms of wireless friendliness in the building-design stage.
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
Graph neural networks are promising architecture for learning and inference with graph-structured data.
Federated Mutual Learning
The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.
Adaptive Sampling to Reduce Disparate Performance
In such a scenario, a natural strategy to mitigate the performance difference of the classifier is to provide additional training data drawn from the demographic groups that are worse off.
A Re-visit of the Popularity Baseline in Recommender Systems
On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images
Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset.
Benchmarking High Bandwidth Memory on FPGAs
FPGAs are starting to be enhanced with High Bandwidth Memory (HBM) as a way to reduce the memory bandwidth bottleneck encountered in some applications and to give the FPGA more capacity to deal with application state.
Hardware Architecture
Estimation of the Laser Frequency Nosie Spectrum by Continuous Dynamical Decoupling
Decoherence induced by the laser frequency noise is one of the most important obstacles in the quantum information processing.
Quantum Physics
Adaptive Structural Fingerprints for Graph Attention Networks
Yet, how to fully exploit rich structural information in the attention mechanism remains a challenge.
Single-Side Domain Generalization for Face Anti-Spoofing
In this work, we propose an end-to-end single-side domain generalization framework (SSDG) to improve the generalization ability of face anti-spoofing.
Reconstructing normal section profiles of 3D revolving structures via pose-unconstrained multi-line structured-light vision
First, we establish a model to estimate the axis of 3D revolving geometrical structure and the normal section profile using corresponding points.
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation.
STAN-CT: Standardizing CT Image using Generative Adversarial Network
Computed tomography (CT) plays an important role in lung malignancy diagnostics and therapy assessment and facilitating precision medicine delivery.
Application of Structural Similarity Analysis of Visually Salient Areas and Hierarchical Clustering in the Screening of Similar Wireless Capsule Endoscopic Images
Small intestinal capsule endoscopy is the mainstream method for inspecting small intestinal lesions, but a single small intestinal capsule endoscopy will produce 60, 000 - 120, 000 images, the majority of which are similar and have no diagnostic value.
Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character
The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters.
Model Watermarking for Image Processing Networks
In this way, when the attacker trains one surrogate model by using the input-output pairs of the target model, the hidden watermark will be learned and extracted afterward.
Bounded Incentives in Manipulating the Probabilistic Serial Rule
In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation.
Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems
In this paper, we propose a deep reinforcement learning based approach to automatically discover new variable ordering heuristics that are better adapted for a given class of CSP instances.
COBRA: Context-aware Bernoulli Neural Networks for Reputation Assessment
Trust and reputation management (TRM) plays an increasingly important role in large-scale online environments such as multi-agent systems (MAS) and the Internet of Things (IoT).
Learning Improvement Heuristics for Solving Routing Problems
In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems.
Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning
These observations suggest that our proposed method can seek the trade-off where both channel resources and customers' satisfaction are optimal.
Bilinear Constraint based ADMM for Mixed Poisson-Gaussian Noise Removal
In this paper, we propose new operator-splitting algorithms for the total variation regularized infimal convolution (TV-IC) model [4] in order to remove mixed Poisson-Gaussian(MPG) noise.
Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation
Knowledge graphs (KGs) have proven to be effective for high-quality recommendation, where the connectivities between users and items provide rich and complementary information to user-item interactions.
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.
Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation
This regularized CAM can be embedded in most recent advanced weakly supervised semantic segmentation framework.
Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context
We select the most suitable modalities for valence and arousal tasks respectively and each modal feature is extracted using the modality-specific pre-trained deep model on large generic dataset.
FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images
In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images.
Method of diagnosing heart disease based on deep learning ECG signal
Some have tried to combine expertise and signal processing to classify ECG signal by heart disease type.
Imbalanced Learning-based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
Change detection is a quite challenging task due to the imbalance between unchanged and changed class.
Transferrable Operative Difficulty Assessment in Robot-assisted Teleoperation: A Domain Adaptation Approach
Providing an accurate and efficient assessment of operative difficulty is important for designing robot-assisted teleoperation interfaces that are easy and natural for human operators to use.
Average-case Analysis of the Assignment Problem with Independent Preferences
Recently, [Deng, Gao, Zhang 2017] show that when the agents' preferences are drawn from a uniform distribution, its \textit{average-case approximation ratio} is upper bounded by 3. 718.
Multi-Normal Estimation via Pair Consistency Voting
This paper presents a unified definition for point cloud normals of feature and non-feature points, which allows feature points to possess multiple normals.
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML
Lifelong learning, the problem of continual learning where tasks arrive in sequence, has been lately attracting more attention in the computer vision community.
Class-incremental Learning via Deep Model Consolidation
The idea is to first train a separate model only for the new classes, and then combine the two individual models trained on data of two distinct set of classes (old classes and new classes) via a novel double distillation training objective.
Generative Visual Dialogue System via Adaptive Reasoning and Weighted Likelihood Estimation
The key challenge of generative Visual Dialogue (VD) systems is to respond to human queries with informative answers in natural and contiguous conversation flow.
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression
State-of-the-art deep model compression methods exploit the low-rank approximation and sparsity pruning to remove redundant parameters from a learned hidden layer.
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting
The optimal DMS policy is applied to select the best model at each time step with a moving window.
Geometric Constrained Joint Lane Segmentation and Lane Boundary Detection
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.
Semi-supervised Learning on Graphs with Generative Adversarial Nets
We first provide insights on working principles of adversarial learning over graphs and then present GraphSGAN, a novel approach to semi-supervised learning on graphs.
An Operation Network for Abstractive Sentence Compression
Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase the words, which are more coherent to human sentence compression.
Spectral Network Embedding: A Fast and Scalable Method via Sparsity
In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis.
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices
Recurrent neural networks (RNNs) achieve cutting-edge performance on a variety of problems.
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Incentive mechanisms for crowdsourcing are designed to incentivize financially self-interested workers to generate and report high-quality labels.
An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending
This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting.
Hourly-Similarity Based Solar Forecasting Using Multi-Model Machine Learning Blending
The final optimal model is a combination of MMFF models with the best-performed blending algorithm at every hour.
Interacting Attention-gated Recurrent Networks for Recommendation
In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.
Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images
Firstly, we pre-train CNN on the ImageNet dataset and transfer the knowledge from the pre-trained model to the medical imaging progression representation, generating the features for different tasks.
Intensity Video Guided 4D Fusion for Improved Highly Dynamic 3D Reconstruction
The availability of high-speed 3D video sensors has greatly facilitated 3D shape acquisition of dynamic and deformable objects, but high frame rate 3D reconstruction is always degraded by spatial noise and temporal fluctuations.
Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Then we select the relevant group features by performing the group Lasso feature selection process in a sequence of parameters.
Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling
Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.
Occlusion-Free Face Alignment: Deep Regression Networks Coupled With De-Corrupt AutoEncoders
Face alignment or facial landmark detection plays an important role in many computer vision applications, e. g., face recognition, facial expression recognition, face animation, etc.
Leveraging Datasets With Varying Annotations for Face Alignment via Deep Regression Network
Facial landmark detection, as a vital topic in computer vision, has been studied for many decades and lots of datasets have been collected for evaluation.
Scaling POMDPs For Selecting Sellers in E-markets-Extended Version
In multiagent e-marketplaces, buying agents need to select good sellers by querying other buyers (called advisors).
Correlative Multi-Label Multi-Instance Image Annotation
The cross-level label coherence en-codes the consistency between the labels at the image leveland the labels at the region level.