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Found 220 papers, 73 papers with code
GUAP: Graph Universal Attack Through Adversarial Patching
Graph neural networks (GNNs) are a class of effective deep learning models for node classification tasks; yet their predictive capability may be severely compromised under adversarially designed unnoticeable perturbations to the graph structure and/or node data.
Position Embedding Needs an Independent Layer Normalization
By analyzing the input and output of each encoder layer in VTs using reparameterization and visualization, we find that the default PE joining method (simply adding the PE and patch embedding together) operates the same affine transformation to token embedding and PE, which limits the expressiveness of PE and hence constrains the performance of VTs.
Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices.
Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild
The proposed method consists of a learnable blur kernel to estimate the defocus map, which is an unsupervised method, and a single-image defocus deblurring generative adversarial network (DefocusGAN) for the first time.
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted.
Optical Character Recognition
Weakly supervised Semantic Segmentation
+1
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Most video-and-language representation learning approaches employ contrastive learning, e. g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs.
Ranked #1 on
Video Retrieval
on LSMDC
(text-to-video Mean Rank metric)
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
Then, it conducts a non-linear interaction among multi-hop features to enhance the discrimination of nodes.
Dual Complementary Dynamic Convolution for Image Recognition
As a powerful engine, vanilla convolution has promoted huge breakthroughs in various computer tasks.
Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation
Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or Kullback-Leibler divergence between the original data and its low-rank approximation which often suffers from grossly corruptions or outliers and the neglect of manifold structures of data.
A Unified Pyramid Recurrent Network for Video Frame Interpolation
Flow-guide synthesis provides a common framework for frame interpolation, where optical flow is typically estimated by a pyramid network, and then leveraged to guide a synthesis network to generate intermediate frames between input frames.
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Then, we introduce a novel structure-mixing knowledge distillation strategy to enhance the learning ability of MLPs for structure information.
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations.
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e. g., unsupervised semantic segmentation (USS).
Flexible Alignment Super-Resolution Network for Multi-Contrast MRI
To tackle this problem, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution.
Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering
Under this setting, these 2D spatial reasoning approaches cannot distinguish the fine-grain spatial relations between visual objects and scene texts on the same image plane, thereby impairing the interpretability and performance of TextVQA models.
Deep Hyperspectral and Multispectral Image Fusion with Inter-image Variability
The fusion problem is stated as an optimization problem in the maximum a posteriori framework.
Data-Driven Control of Distributed Event-Triggered Network Systems
The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a. k. a.
Pathway to Future Symbiotic Creativity
This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation.
Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level context memory
This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery.
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms
In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms.
Neural Optimization Machine: A Neural Network Approach for Optimization
The NN objective function can have arbitrary architectures and activation functions.
Event-triggered Consensus Control of Heterogeneous Multi-agent Systems: Model- and Data-based Analysis
This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme.
Locality Guidance for Improving Vision Transformers on Tiny Datasets
Therefore, our locality guidance approach is very simple and efficient, and can serve as a basic performance enhancement method for VTs on tiny datasets.
NDF: Neural Deformable Fields for Dynamic Human Modelling
However, the learned canonical representation is static and the current design of the deformation fields is not able to represent large movements or detailed geometry changes.
Data-driven Self-triggered Control via Trajectory Prediction
Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity.
A Survey of Decision Making in Adversarial Games
Game theory has by now found numerous applications in various fields, including economics, industry, jurisprudence, and artificial intelligence, where each player only cares about its own interest in a noncooperative or cooperative manner, but without obvious malice to other players.
A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion
In order to demonstrate the proposed model's capability in dealing with severe data loss scenarios, we contribute a high-accuracy and challenging motion capture dataset of multi-person interactions with severe occlusion.
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models
We propose a new algorithm, called GAMI-Tree, that is similar to EBM, but has a number of features that lead to better performance.
Adaptive Random Fourier Features Kernel LMS
We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS).
Shapley Computations Using Surrogate Model-Based Trees
Shapley-related techniques have gained attention as both global and local interpretation tools because of their desirable properties.
$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features
In this paper, we show that the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features.
Bridging Mean-Field Games and Normalizing Flows with Trajectory Regularization
In this work, we unravel the connections between MFGs and NFs by contextualizing the training of an NF as solving the MFG.
PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction
We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset.
Enhanced Bi-directional Motion Estimation for Video Frame Interpolation
We present a novel simple yet effective algorithm for motion-based video frame interpolation.
Ranked #1 on
Video Frame Interpolation
on MSU Video Frame Interpolation
(PSNR metric)
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing.
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing
The performance of GNNs degrades as they become deeper due to the over-smoothing.
Ranked #5 on
Node Classification
on Squirrel
Interpretable Feature Engineering for Time Series Predictors using Attention Networks
Regression problems with time-series predictors are common in banking and many other areas of application.
Joint learning of object graph and relation graph for visual question answering
Modeling visual question answering(VQA) through scene graphs can significantly improve the reasoning accuracy and interpretability.
Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study
Overall, XGB and FFNNs were competitive, with FFNNs showing better performance in smooth models and tree-based boosting algorithms performing better in non-smooth models.
Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition
The applicant is then entitled to an explanation for the negative decision.
ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval
Visual appearance is considered to be the most important cue to understand images for cross-modal retrieval, while sometimes the scene text appearing in images can provide valuable information to understand the visual semantics.
Ranked #9 on
Cross-Modal Retrieval
on Flickr30k
Training-free Transformer Architecture Search
Recently, Vision Transformer (ViT) has achieved remarkable success in several computer vision tasks.
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily
Moreover, we propose a Conv-Agnostic GNNs framework (CAGNNs) to enhance the performance of GNNs on heterophily datasets by learning the neighbor effect for each node.
Data-Efficient Graph Grammar Learning for Molecular Generation
This is a non-trivial task for neural network-based generative models since the relevant chemical knowledge can only be extracted and generalized from the limited training data.
Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN
For the text modality, we use global vectors for word representation (GloVe) to perform word embeddings and the embeddings are fed into the Bi-LSTM network.
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning
To deploy SSDR-AL in a more practical scenario, we design a noise-aware iterative labeling strategy to confront the "noisy annotation" problem introduced by the previous "dominant labeling" strategy in superpoints.
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks.
Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI Systems
To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived.
Time-Frequency Mask Aware Bi-directional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation
In particular, this method breaks through the limitations of the existing methods, not only achieves good results in multivariate separation, but also effectively separates signals when mixed with 40dB Gaussian noise signals.
Evaluating Robustness of Cooperative MARL: A Model-based Approach
In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL).
Memory-based Message Passing: Decoupling the Message for Propogation from Discrimination
Based on the homophily assumption, the current message passing always aggregates features of connected nodes, such as the graph Laplacian smoothing process.
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention.
Geometry-Aware Guided Loss for Deep Crack Recognition
Despite the substantial progress of deep models for crack recognition, due to the inconsistent cracks in varying sizes, shapes, and noisy background textures, there still lacks the discriminative power of the deeply learned features when supervised by the cross-entropy loss.
Robust Recommendation with Implicit Feedback via Eliminating the Effects of Unexpected Behaviors
In this paper, we propose a Multi-Preferences Model (MPM) to eliminate the effects of unexpected behaviors.
Mean-Square Stability and Stabilizability Analyses of LTI Systems Under Spatially Correlated Multiplicative Perturbations
In this paper, we investigate the mean-square stability and stabilizability problems for linear time-invariant systems under stochastic spatially correlated multiplicative uncertainties.
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning
This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL), where agents over a communication network aim to find the optimal policy to maximize the average of all agents' local returns.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning Representation for Clustering via Prototype Scattering and Positive Sampling
The strengths of ProPos are avoidable class collision issue, uniform representations, well-separated clusters, and within-cluster compactness.
Ranked #1 on
Image Clustering
on Imagenet-dog-15
Traversing the Local Polytopes of ReLU Neural Networks
Our polytope traversing algorithm can be adapted to a wide range of applications related to robustness and interpretability.
Temporal-MPI: Enabling Multi-Plane Images for Dynamic Scene Modelling via Temporal Basis Learning
One of the seminal image-based rendering method, the multi-plane image (MPI), produces high novel-view synthesis quality for static scenes.
Traversing the Local Polytopes of ReLU Neural Networks: A Unified Approach for Network Verification
Although neural networks (NNs) with ReLU activation functions have found success in a wide range of applications, their adoption in risk-sensitive settings has been limited by the concerns on robustness and interpretability.
Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization
We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution in expectation with an $\mathcal{O}(\frac{1}{T}+\frac{1}{\sqrt{T}})$ convergence rate, where $T$ is the total number of iterations.
Data-driven Hedging of Stock Index Options via Deep Learning
We develop deep learning models to learn the hedge ratio for S&P500 index options directly from options data.
Data-driven Control of Dynamic Event-triggered Systems with Delays
Data-based representations for time-invariant linear systems with known or unknown system input matrices are first developed, along with a novel class of dynamic triggering schemes for sampled-data systems with time delays.
Data-Driven Resilient Predictive Control under Denial-of-Service
Finally, a numerical example is given to validate the effectiveness of the proposed control method.
Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode
Vega achieves SoA-leading efficiency of 615 GOPS/W on 8-bit INT computation (boosted to 1. 3TOPS/W for 8-bit DNN inference with hardware acceleration).
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
Improving the training and inference performance of graph neural networks (GNNs) is faced with a challenge uncommon in general neural networks: creating mini-batches requires a lot of computation and data movement due to the exponential growth of multi-hop graph neighborhoods along network layers.
Deep Encryption: Protecting Pre-Trained Neural Networks with Confusion Neurons
Having consumed huge amounts of training data and computational resource, large-scale pre-trained models are often considered key assets of AI service providers.
Evaluating Robustness of Cooperative MARL
In recent years, a proliferation of methods were developed for multi-agent reinforcement learning (MARL).
$G^3$: Representation Learning and Generation for Geometric Graphs
A geometric graph is a graph equipped with geometric information (i. e., node coordinates).
Federated Inference through Aligning Local Representations and Learning a Consensus Graph
On the top is a federation of the local data representations, performing global inference that incorporates all distributed parts collectively.
Transient Performance Analysis of the $\ell_1$-RLS
The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems.
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling.
On Faster Convergence of Scaled Sign Gradient Descent
For the first two cases, it can be shown that the scaled signGD converges at a linear rate.
An Information Theory-inspired Strategy for Automatic Network Pruning
This practically limits the application of model compression when the model needs to be deployed on a wide range of devices.
Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues
Finally, a Fusion Attention Block (FAB) is proposed to adaptively fuse the features from the two branches and generate an optimized colorization result.
Efficient proximal gradient algorithms for joint graphical lasso
We consider learning an undirected graphical model from sparse data.
Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration
A progressive texture blending module is designed to blend the encoded two-stream features in a multi-scale and progressive manner.
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection
For the task of end-to-end scene text recognition, our method outperforms Mask TextSpotter v3 by 1. 1% on Total-Text.
Graph coarsening: From scientific computing to machine learning
The general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning.
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification
In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features.
Scale-Consistent Fusion: from Heterogeneous Local Sampling to Global Immersive Rendering
Image-based geometric modeling and novel view synthesis based on sparse, large-baseline samplings are challenging but important tasks for emerging multimedia applications such as virtual reality and immersive telepresence.
Composition and Application of Current Advanced Driving Assistance System: A Review
Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price.
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques.
Bias, Fairness, and Accountability with AI and ML Algorithms
In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms.
Unsupervised domain adaptation via double classifiers based on high confidence pseudo label
In addition to aligning the global distribution, the real domain adaptation should also align the meso distribution and the micro distribution.
Geometrical Characterization of Sensor Placement for Cone-Invariant and Multi-Agent Systems against Undetectable Zero-Dynamics Attacks
Undetectable attacks are an important class of malicious attacks threatening the security of cyber-physical systems, which can modify a system's state but leave the system output measurements unaffected, and hence cannot be detected from the output.
RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection
Upon the frame, an Interaction Intensifier Module and a Correlation Parsing Module are carefully designed, where: a) interactive semantics from humans can be exploited and passed to objects to intensify interactions, b) interactive correlations among humans, objects and interactions are integrated to promote predictions.
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification
In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention.
Underwater Target Recognition based on Multi-Decision LOFAR Spectrum Enhancement: A Deep Learning Approach
To overcome this issue and to further improve the recognition performance, we adopt a deep learning approach for underwater target recognition and propose a LOFAR spectrum enhancement (LSE)-based underwater target recognition scheme, which consists of preprocessing, offline training, and online testing.
Carrying out CNN Channel Pruning in a White Box
Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner w. r. t.
Image-based Virtual Fitting Room
We firstly used Mask R-CNN to find the regions of different fashion items, and secondly used Neural Style Transfer to change the style of the selected fashion items.
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning
In this paper, we argue that learning by comparing helps identify these hard snippets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short.
Noise Injection-based Regularization for Point Cloud Processing
In this paper, we, for the first time, perform systematic investigation on noise injection-based regularization for point cloud-domain DNNs.
ReCU: Reviving the Dead Weights in Binary Neural Networks
We prove that reviving the "dead weights" by ReCU can result in a smaller quantization error.
Resilient Control under Quantization and Denial-of-Service: Co-designing a Deadbeat Controller and Transmission Protocol
When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals.
Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model
The LIFE algorithm is able to fit a wide single-hidden-layer neural network (NN) accurately with three steps: defining the subsets of a dataset by the linear projections of neural nodes, creating the features from multiple narrow single-hidden-layer NNs trained on the different subsets of the data, combining the features with a linear model.
Projection-based QLP Algorithm for Efficiently Computing Low-Rank Approximation of Matrices
The efficiency and effectiveness of our proposed PbP-QLP algorithm are investigated through various classes of synthetic and real-world data matrices.
Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning
Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images.
Performance of Domain-Wall Encoding for Quantum Annealing
In this paper we experimentally test the performance of the recently proposed domain-wall encoding of discrete variables from [Chancellor Quantum Sci.
Quantum Physics
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-Resolution
Among the major remaining challenges for single image super resolution (SISR) is the capacity to recover coherent images with global shapes and local details conforming to human vision system.
Generating a Doppelganger Graph: Resembling but Distinct
Deep generative models, since their inception, have become increasingly more capable of generating novel and perceptually realistic signals (e. g., images and sound waves).
Directed Acyclic Graph Neural Networks
Graph-structured data ubiquitously appears in science and engineering.
Ranked #7 on
Graph Property Prediction
on ogbg-code2
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model.
Transient Theoretical Analysis of Diffusion RLS Algorithm for Cyclostationary Colored Inputs
Convergence of the diffusion RLS (DRLS) algorithm to steady-state has been extensively studied in the literature, whereas no analysis of its transient convergence behavior has been reported yet.
CDNet: Centripetal Direction Network for Nuclear Instance Segmentation
Specifically, we define the centripetal direction feature as a class of adjacent directions pointing to the nuclear center to represent the spatial relationship between pixels within the nucleus.
A Plug-and-Play Priors Framework for Hyperspectral Unmixing
Spectral unmixing is a widely used technique in hyperspectral image processing and analysis.
Empirical Evaluation of Typical Sparse Fast Fourier Transform Algorithms
Computing the Sparse Fast Fourier Transform(sFFT) of a K-sparse signal of size N has emerged as a critical topic for a long time.
Annotation-efficient deep learning for automatic medical image segmentation
Automatic medical image segmentation plays a critical role in scientific research and medical care.
Online Convex Optimization Over Erdos-Renyi Random Networks
The regret bounds scaling with respect to $T$ match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problems, e. g., $\mathcal{O}(\sqrt{T}) $ and $\mathcal{O}(\ln(T)) $ regrets are established for convex and strongly convex losses with full gradient feedback and two-points information, respectively.
Handling Noisy Labels via One-Step Abductive Multi-Target Learning: An Application to Helicobacter Pylori Segmentation
This situation raises two more difficult problems: 1) the methodology of approaches making corrections corresponding to potentially noisy-labeled instances has limitations due to the complex noise existing in labels; and 2) the appropriate evaluation strategy for validation/testing is unclear because of the great difficulty in collecting the noisy-free ground-truth labels.
Edge Adaptive Hybrid Regularization Model For Image Deblurring
In the proposed model, it detects the edges and then spatially adjusts the parameters of Tikhonov and TV regularization terms for each pixel according to the edge information.
On Performance of Sparse Fast Fourier Transform Algorithms Using the Aliasing Filter
In the second part, we make two categories of experiments for computing the signals of different SNR, different N, different K by a standard testing platform and record the run time, percentage of the signal sampled and L0, L1, L2 error both in the exactly sparse case and general sparse case.
On Performance of Multiscale Sparse Fast Fourier Transform Algorithm
The sFFT algorithms decrease the runtime and sampling complexity by taking advantage of the signal inherent characteristics that a large number of signals are sparse in the frequency domain(e. g., sensors, video data, audio, medical image, etc.).
Deep reinforcement learning for RAN optimization and control
Due to the high variability of the traffic in the radio access network (RAN), fixed network configurations are not flexible enough to achieve optimal performance.
Multiple-element joint detection for Aspect-Based Sentiment Analysis
In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence.
Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors
In our work, we propose an NMF based unmixing framework which jointly uses a handcrafting regularizer and a learnt regularizer from data.
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution
To preserve the parallax structure among the reconstructed SAIs, we subsequently append a consistency regularization network trained over a structure-aware loss function to refine the parallax relationships over the coarse estimation.
A deep learning-based approach for the automated surface inspection of copper clad laminate images
A machine vision-based surface quality inspection system is usually composed of two processes: image acquisition and automatic defect detection.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and {the} learned prior to reconstruct the final HR HSI.
Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive performance over traditional statistical methods.
Supervised Machine Learning Techniques: An Overview with Applications to Banking
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking.
Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
The linear inverse problem is fundamental to the development of various scientific areas.
Deep Spatial-angular Regularization for Compressive Light Field Reconstruction over Coded Apertures
Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithms.
Image and Video Processing
Online Graph-Based Change Point Detection in Multiband Image Sequences
To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes.
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning
Specifically, PZRes-Net learns a high resolution and \textit{zero-centric} residual image, which contains high-frequency spatial details of the scene across all spectral bands, from both inputs in a progressive fashion along the spectral dimension.
Hyperspectral Image Super-Resolution
Hyperspectral Unmixing
+1
AnimeGAN: A Novel Lightweight GAN for Photo Animation
The existing methods usually have some problems, among which significant problems mainly include: 1) the generated images have no obvious animated style textures; 2) the generated images lose the content of the original images; 3) the parameters of the network require the large memory capacity.
Policy Gradient from Demonstration and Curiosity
In this work, an integrated policy gradient algorithm was proposed to boost exploration and facilitate intrinsic reward learning from only limited number of demonstrations.
AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown.
Ranked #7 on
Unsupervised Domain Adaptation
on Duke to Market
Domain Adaptive Person Re-Identification
Person Re-Identification
+1
Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization
Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited sampling resources have to be shared with the angular dimension.
Adaptive Explainable Neural Networks (AxNNs)
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results.
Learning a Weakly-Supervised Video Actor-Action Segmentation Model with a Wise Selection
Instead of blindly trusting quality-inconsistent PAs, WS^2 employs a learning-based selection to select effective PAs and a novel region integrity criterion as a stopping condition for weakly-supervised training.
Automated discovery of a robust interatomic potential for aluminum
The accuracy and robustness of an ML potential is primarily limited by the quality and diversity of the training dataset.
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information.
Parallel and distributed asynchronous adaptive stochastic gradient methods
The proposed method is tested on both convex and non-convex machine learning problems, and the numerical results demonstrate its clear advantages over the sync counterpart and the async-parallel nonadaptive SGM.
Optimization and Control Distributed, Parallel, and Cluster Computing Numerical Analysis Numerical Analysis 90C15, 65Y05, 68W15, 65K05
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models
Worse, the bad actors found for one graph model severely compromise other models as well.
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
The feature-learning procedure of CNN largely depends on the architecture of CNN.
Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader
When the faster evader is allowed to move freely without any constraint, the main issues are how to form an encirclement to trap the evader into the capture domain, how to balance between forming an encirclement and approaching the faster evader, and what conditions make the capture possible.
Systems and Control Systems and Control
Embedding Compression with Isotropic Iterative Quantization
Continuous representation of words is a standard component in deep learning-based NLP models.
Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning
Multitask learning is an approach to inductive transfer learning (using what is learned for one problem to assist in another problem) and helps improve generalization performance relative to learning each task separately by using the domain information contained in the training signals of related tasks as an inductive bias.
Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks
Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated.
Signal Processing
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Both the coarsening matrix and the transport cost matrix are parameterized, so that an optimal coarsening strategy can be learned and tailored for a given set of graphs.
Chart Auto-Encoders for Manifold Structured Data
CAE admits desirable manifold properties that auto-encoders with a flat latent space fail to obey, predominantly proximity of data.
A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors.
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator
In addition, CAG exhibits high transferability across different DNN classifier models in black-box attack scenario by introducing random dropout in the process of generating perturbations.
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
To achieve this, we represent the probabilistic kernel as an additional variational variable in a variational inference (VI) framework for SGPR models where its posterior belief is learned together with that of the other variational variables (i. e., inducing variables and kernel hyperparameters).
Graph Enhanced Cross-Domain Text-to-SQL Generation
Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations.
DFSMN-SAN with Persistent Memory Model for Automatic Speech Recognition
Self-attention networks (SAN) have been introduced into automatic speech recognition (ASR) and achieved state-of-the-art performance owing to its superior ability in capturing long term dependency.
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes
These approaches are supervised; a predictive task with ground-truth labels is used to drive the learning.
Adaptively Aligned Image Captioning via Adaptive Attention Time
In this paper, we propose a novel attention model, namely Adaptive Attention Time (AAT), to align the source and the target adaptively for image captioning.
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion
Specifically, the coarse sub-aperture image (SAI) synthesis module first explores the scene geometry from an unstructured sparsely-sampled LF and leverages it to independently synthesize novel SAIs, in which a confidence-based blending strategy is proposed to fuse the information from different input SAIs, giving an intermediate densely-sampled LF.
Attention on Attention for Image Captioning
In this paper, we propose an Attention on Attention (AoA) module, which extends the conventional attention mechanisms to determine the relevance between attention results and queries.
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency.
Light Field Super-resolution via Attention-Guided Fusion of Hybrid Lenses
To the best of our knowledge, this is the first end-to-end deep learning method for reconstructing a high-resolution LF image with a hybrid input.
Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks
In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels.
Learning discriminative features in sequence training without requiring framewise labelled data
We propose a novel method which simultaneously models both the sequence discriminative training and the feature discriminative learning within a single network architecture, so that it can learn discriminative deep features in sequence training that obviates the need for presegmented training data.
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.
Ranked #2 on
Graph Classification
on IPC-lifted
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model
This paper presents an unsupervised nonlinear spectral unmixing method based on a deep autoencoder network that applies to a generalized linear-mixture/nonlinear fluctuation model, consisting of a linear mixture component and an additive nonlinear mixture component that depends on both endmembers and abundances.
Time Series Simulation by Conditional Generative Adversarial Net
Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation.
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a challenging combinatorial problem, owing to the intractable search space superexponential in the number of graph nodes.
A Sequential Set Generation Method for Predicting Set-Valued Outputs
Though SSG is sequential in nature, it does not penalize the ordering of the appearance of the set elements and can be applied to a variety of set output problems, such as a set of classification labels or sequences.
Stratified Labeling for Surface Consistent Parallax Correction and Occlusion Completion
A stratified synthesis strategy is adopted which parses the scene content based on stratified disparity layers and across a varying range of spatial granularities.
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
Ranked #4 on
Dynamic Link Prediction
on DBLP Temporal
A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation
To the best of our knowledge, this dataset is the first publicly available dataset created in a systematic manner with ground-truth for spectral unmixing.
Scalable Graph Learning for Anti-Money Laundering: A First Look
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150, 000 people since 2006, upwards of 700, 000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people.
Automatic Seismic Salt Interpretation with Deep Convolutional Neural Networks
One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision.
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI.
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features.
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
We focus on the matrix representation of graphs and formulate penalty terms that regularize the output distribution of the decoder to encourage the satisfaction of validity constraints.
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
