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Found 282 papers, 117 papers with code
The Power of Online Learning in Stochastic Network Optimization
We prove strong performance guarantees of the proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time.
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
To the best of our knowledge, this is the first work that shows how to achieve logarithmic regret in constrained contextual bandits.
AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation
Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme.
Ranked #4 on
Age Estimation
on ChaLearn 2015
exploring the context of locations for personalized location recommendations
In this paper, by leveraging the Skipgram model, we learn the latent representation for a location to capture the influence of its context.
Double Thompson Sampling for Dueling Bandits
This simple algorithm applies to general Copeland dueling bandits, including Condorcet dueling bandits as its special case.
Saliency Integration: An Arbitrator Model
Previous offline integration methods usually face two challenges: 1. if most of the candidate saliency models misjudge the saliency on an image, the integration result will lean heavily on those inferior candidate models; 2. an unawareness of the ground truth saliency labels brings difficulty in estimating the expertise of each candidate model.
Spontaneous Facial Micro-Expression Recognition using Discriminative Spatiotemporal Local Binary Pattern with an Improved Integral Projection
For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition.
Image denoising via group sparsity residual constraint
Group sparsity has shown great potential in various low-level vision tasks (e. g, image denoising, deblurring and inpainting).
VIPLFaceNet: An Open Source Deep Face Recognition SDK
Robust face representation is imperative to highly accurate face recognition.
Analyzing the Affect of a Group of People Using Multi-modal Framework
We fuse face, upperbody and scene information for robustness of GER against the challenging environments.
Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition
In this paper, we propose a novel model, named Stroke Sequence-dependent Deep Convolutional Neural Network (SSDCNN), using the stroke sequence information and eight-directional features for Online Handwritten Chinese Character Recognition (OLHCCR).
Analyzing the group sparsity based on the rank minimization methods
Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficient of each group is measured by estimating the singular values of each group.
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation
However, it still lacks a sound mathematical explanation on why WNNM is more feasible than NNM.
Group Sparsity Residual Constraint for Image Denoising
Unlike the conventional group-based sparse representation denoising methods, two kinds of prior, namely, the NSS priors of noisy and pre-filtered images, are used in GSRC.
CNN based music emotion classification
Music emotion recognition (MER) is usually regarded as a multi-label tagging task, and each segment of music can inspire specific emotion tags.
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization
In this paper, a group-based sparse representation method with non-convex regularization (GSR-NCR) for image CS reconstruction is proposed.
Parameter Estimation in Computational Biology by Approximate Bayesian Computation coupled with Sensitivity Analysis
We address the problem of parameter estimation in models of systems biology from noisy observations.
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits
When the load/price is low, so is the cost/regret of pulling a suboptimal arm (e. g., trying a suboptimal network configuration).
Recursive Spatial Transformer (ReST) for Alignment-Free Face Recognition
The designed ReST has an intrinsic recursive structure and is capable of progressively aligning faces to a canonical one, even those with large variations.
MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval
More specifically, MTFH exploits an efficient objective function to flexibly learn the modality-specific hash codes with different length settings, while synchronously learning two semantic correlation matrices to semantically correlate the different hash representations for heterogeneous data comparable.
In-Orbit Instrument Performance Study and Calibration for POLAR Polarization Measurements
POLAR is a compact space-borne detector designed to perform reliable measurements of the polarization for transient sources like Gamma-Ray Bursts in the energy range 50-500keV.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Instrumentation and Detectors
CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation
In principle, CerfGAN contains a novel component, i. e., a multi-class discriminator (MCD), which gives the model an extremely powerful ability to match multiple translation mappings.
DPatch: An Adversarial Patch Attack on Object Detectors
Successful realization of DPatch also illustrates the intrinsic vulnerability of the modern detector architectures to such patch-based adversarial attacks.
LCQMC:A Large-scale Chinese Question Matching Corpus
In this paper, we first use a search engine to collect large-scale question pairs related to high-frequency words from various domains, then filter irrelevant pairs by the Wasserstein distance, and finally recruit three annotators to manually check the left pairs.
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification
As the largest manually annotated public Chinese SSEI corpus in the bank domain, the BQ corpus is not only useful for Chinese question semantic matching research, but also a significant resource for cross-lingual and cross-domain SSEI research.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
Parallelizable Algorithms for Optimization Problems with Orthogonality Constraints
Numerical experiments in serial illustrate that the novel updating rule for the Lagrangian multipliers significantly accelerates the convergence of PLAM and makes it comparable with the existent feasible solvers for optimization problems with orthogonality constraints, and the performance of PCAL does not highly rely on the choice of the penalty parameter.
Optimization and Control 15A18, 65F15, 65K05, 90C06
Catching Loosely Synchronized Behavior in Face of Camouflage
Fraud has severely detrimental impacts on the business of social networks and other online applications.
Social and Information Networks Cryptography and Security
Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration
We evaluate the effectiveness of our algorithm based on a simulator built by real traces.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators
On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by na\"ive implementations without optimizations, and the throughput is further improved up to 1. 26x by leveraging heterogeneous optimizations in DNNVM.
AdaLinUCB: Opportunistic Learning for Contextual Bandits
In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations.
Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification
Hierarchical text classification has many real-world applications.
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
Due to insufficient training data and the high computational cost to train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-network-based applications.
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification
These word pairs can be extracted by using dependency parsers and simple rules.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Relation Discovery with Out-of-Relation Knowledge Base as Supervision
In this paper, we study the problem of how to use out-of-relation knowledge bases to supervise the discovery of unseen relations, where out-of-relation means that relations to discover from the text corpus and those in knowledge bases are not overlapped.
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators
However, the GAN-based SR methods only use image discriminator to distinguish SR images and high-resolution (HR) images.
ASER: A Large-scale Eventuality Knowledge Graph
Understanding human's language requires complex world knowledge.
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network.
Multitask Learning for Network Traffic Classification
We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy.
Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
Usually, acquiring less data is a direct but important strategy to address these issues.
Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling
Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an early stage.
hood2vec: Identifying Similar Urban Areas Using Mobility Networks
Hence, in this paper, we introduce an approach, namely hood2vec, to identify the similarity between urban areas through learning a node embedding of the mobility network captured through Foursquare check-ins.
A Novel Kalman Filter Based Shilling Attack Detection Algorithm
Collaborative filtering has been widely used in recommendation systems to recommend items that users might like.
Hyper-Path-Based Representation Learning for Hyper-Networks
Then a carefully designed algorithm, Hyper-gram, utilizes these random walks to capture both pairwise relationships and tuplewise relationships in the whole hyper-networks.
Cross Domain Image Matching in Presence of Outliers
Cross domain image matching between image collections from different source and target domains is challenging in times of deep learning due to i) limited variation of image conditions in a training set, ii) lack of paired-image labels during training, iii) the existing of outliers that makes image matching domains not fully overlap.
VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks
Our approach can effectively improve the structural sparsity of residual models.
IPO: Interior-point Policy Optimization under Constraints
In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints.
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Security of Deep Learning Methodologies: Challenges and Opportunities
Despite the plethora of studies about security vulnerabilities and defenses of deep learning models, security aspects of deep learning methodologies, such as transfer learning, have been rarely studied.
Neural Subgraph Isomorphism Counting
In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.
XGPT: Cross-modal Generative Pre-Training for Image Captioning
While many BERT-based cross-modal pre-trained models produce excellent results on downstream understanding tasks like image-text retrieval and VQA, they cannot be applied to generation tasks directly.
Learning Diverse Fashion Collocation by Neural Graph Filtering
To facilitate a comprehensive study on diverse fashion collocation, we reorganize Amazon Fashion dataset with carefully designed evaluation protocols.
Decomposing Word Embedding with the Capsule Network
We propose a Capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding, called CapsDecE2S.
On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at both the text span level and the sentence level.
MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
This toolbox supports a wide spectrum of fashion analysis tasks, including Fashion Attribute Prediction, Fashion Recognition and Retrieval, Fashion Landmark Detection, Fashion Parsing and Segmentation and Fashion Compatibility and Recommendation.
On the Difficulty of Membership Inference Attacks
Recent studies propose membership inference (MI) attacks on deep models, where the goal is to infer if a sample has been used in the training process.
BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach
The system uses a Convolutional Neural Network (CNN) to find the blinking pattern, which is defined as a series of Open and Closed states.
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
Telehealth and remote health monitoring have become increasingly important during the SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on healthcare practices.
Contextual Bandits with Side-Observations
We show that this asymptotically optimal regret is upper-bounded as $O\left(|\chi(\mathcal{G})|\log T\right)$, where $|\chi(\mathcal{G})|$ is the domination number of $\mathcal{G}$.
An Opportunistic Bandit Approach for User Interface Experimentation
In fact, we model user interface experimentation as an opportunistic bandit problem, in which the cost of exploration varies under a factor extracted from customer features.
Deep Low-rank Prior in Dynamic MR Imaging
The deep learning methods have achieved attractive performance in dynamic MR cine imaging.
Graph Convolutional Networks for Graphs Containing Missing Features
Notably, our approach does not increase the computational complexity of GCN and it is consistent with GCN when the features are complete.
2nd Place Scheme on Action Recognition Track of ECCV 2020 VIPriors Challenges: An Efficient Optical Flow Stream Guided Framework
To address the problem of training on small datasets for action recognition tasks, most prior works are either based on a large number of training samples or require pre-trained models transferred from other large datasets to tackle overfitting problems.
A Variational Approach to Unsupervised Sentiment Analysis
Our objective function is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment classifier.
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition
Gesture recognition has attracted considerable attention owing to its great potential in applications.
Candidate Periodically Variable Quasars from the Dark Energy Survey and the Sloan Digital Sky Survey
We present a systematic search for periodic light curves in 625 spectroscopically confirmed quasars with a median redshift of 1. 8 in a 4. 6 deg$^2$ overlapping region of the Dark Energy Survey Supernova (DES-SN) fields and the Sloan Digital Sky Survey Stripe 82 (SDSS-S82).
High Energy Astrophysical Phenomena Astrophysics of Galaxies
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference
The inference of a input sample can exit from early stage if the prediction of the stage is confident enough.
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging.
WeightAlign: Normalizing Activations by Weight Alignment
Such methods are less stable than BN as they critically depend on the statistics of a single input sample.
POND: Pessimistic-Optimistic oNline Dispatching
This paper considers constrained online dispatching with unknown arrival, reward and constraint distributions.
Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars
Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring.
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility
The demand for seamless Internet access under extreme user mobility, such as on high-speed trains and vehicles, has become a norm rather than an exception.
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning
With more regulations tackling users' privacy-sensitive data protection in recent years, access to such data has become increasingly restricted and controversial.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Soft-Median Choice: An Automatic Feature Smoothing Method for Sound Event Detection
In Sound Event Detection (SED) systems, the lengths of median filters for post-processing have never been optimized during training due to several problems.
Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning
Noises, artifacts, and loss of information caused by the magnetic resonance (MR) reconstruction may compromise the final performance of the downstream applications.
AGNet: Weighing Black Holes with Machine Learning
Supermassive black holes (SMBHs) are ubiquitously found at the centers of most galaxies.
MedWriter: Knowledge-Aware Medical Text Generation
To exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical.
Getting Ready for LISA: The Data, Support and Preparation Needed to Maximize US Participation in Space-Based Gravitational Wave Science
Among our findings: LISA science productivity is greatly enhanced by a full-featured US science center and an open access data model.
Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology
Annotation-efficient deep learning for automatic medical image segmentation
Automatic medical image segmentation plays a critical role in scientific research and medical care.
OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space
Specifically, we employ orthogonal low-rank embedding in the latent space to disentangle the features in the latent space using mutual class information.
An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation
Based on the massive vessel trajectories collected, the CAE can learn the low-dimensional representations of informative trajectory images in an unsupervised manner.
Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans
The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the three datasets with tens of times smaller model size and higher accuracy.
Generalized Image Reconstruction over T-Algebra
We also show in our experiments that the performance of TPCA increases when the order of compounded pixels increases.
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices
In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock.
Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and Cognitive Radio
ISTCN aims to provide high speed and pervasive network services by integrating broadband terrestrial mobile networks with satellite communication networks.
Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i. e. parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure.
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints
Thus, the overall computational complexity of our algorithm is similar to that of the linear UCB for unconstrained stochastic linear bandits.
DST: Data Selection and joint Training for Learning with Noisy Labels
In this paper, we propose a Data Selection and joint Training (DST) method to automatically select training samples with accurate annotations.
Higher-order topological superconductors based on weak topological insulators
High-order topological phases host robust boundary states at the boundary of the boundary, which can be interpreted from their boundary topology.
Superconductivity Mesoscale and Nanoscale Physics Materials Science
Deep Manifold Learning for Dynamic MR Imaging
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
Sampling methods for efficient training of graph convolutional networks: A survey
Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations.
Balanced softmax cross-entropy for incremental learning with and without memory
When incrementally trained on new classes, deep neural networks are subject to catastrophic forgetting which leads to an extreme deterioration of their performance on the old classes while learning the new ones.
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence
With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment.
No frame left behind: Full Video Action Recognition
A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action.
Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks
Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment.
Learning to Filter: Siamese Relation Network for Robust Tracking
RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result.
ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities
After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them.
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4. 0 on the edge computing level.
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy
A practical long-term tracker typically contains three key properties, i. e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism.
FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity
%on how to exploit strong convexity to further improve the convergence rate of AdaBelief.
Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective
We analyze the impact of various factors in deep ensembles and demonstrate the root cause of the trade-off.
Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data.
FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval
To tackle these issues, we formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data so as to minimize the quantization loss of mapping such data to hamming space, and propose an efficient Fast Discriminative Discrete Hashing (FDDH) approach for large-scale cross-modal retrieval.
Improving Graph Neural Networks with Simple Architecture Design
Combining these techniques, we present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and show empirically that the proposed model outperforms other state of the art GNN models and achieves up to 64% improvements in accuracy on node classification tasks.
Ranked #2 on
Node Classification
on Cornell
Exploring Discourse Structures for Argument Impact Classification
Discourse relations among arguments reveal logical structures of a debate conversation.
A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processes
This paper presents the first model-free, simulator-free reinforcement learning algorithm for Constrained Markov Decision Processes (CMDPs) with sublinear regret and zero constraint violation.
Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning
In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer.
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary.
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers
In this paper, we target a new healthcare application of human activity recognition, early mobility recognition for Intensive Care Unit(ICU) patients.
iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis
We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE).
Provable Convergence of Nesterov's Accelerated Gradient Method for Over-Parameterized Neural Networks
Despite the fact that the objective function is non-convex and non-smooth, we show that NAG converges to a global minimum at a non-asymptotic linear rate $(1-\Theta(1/\sqrt{\kappa}))^t$, where $\kappa > 1$ is the condition number of a gram matrix and $t$ is the number of the iterations.
MMChat: Multi-Modal Chat Dataset on Social Media
To better investigate this issue, we manually annotate 100K dialogues from MMChat and further filter the corpus accordingly, which yields MMChat-hf.
AGNet: Weighing Black Holes with Deep Learning
We find a 1$\sigma$ scatter of 0. 37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate.
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Extensive experiments show that our method is universal to mainstream sampling algorithms and helps significantly reduce the training time, especially in large-scale graphs.
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph
To address this challenge, we propose dictionary-based heterogeneous graph neural network (DHGNet) that effectively handles the heterogeneity of DHG by two-step aggregations, which are word-level and language-level aggregations.
EfficientPhys: Enabling Simple, Fast, and Accurate Camera-Based Vitals Measurement
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance.
Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective
Federated Learning (FL) is a distributed model training paradigm that preserves clients' data privacy.
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective
Federated learning (FL) hyper-parameters significantly affect the training overheads in terms of computation time, transmission time, computation load, and transmission load.
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning
The past decade witnesses a rapid development in the measurement and monitoring technologies for food science.
Learning Higher-Order Dynamics in Video-Based Cardiac Measurement
Recent developments in camera-based vital sign measurement have shown that cardiac measurements can be recovered with impressive accuracy from videos; however, most of the research has focused on extracting summary statistics such as heart rate.
EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance.
Face Detection
Photoplethysmography (PPG) heart rate estimation
Synthetic Data for Multi-Parameter Camera-Based Physiological Sensing
We present systematic experiments showing how physiologically-grounded synthetic data can be used in training camera-based multi-parameter cardiopulmonary sensing.
Natural Image Reconstruction from fMRI using Deep Learning: A Survey
With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain.
RGB Camera-based Physiological Sensing: Challenges and Future Directions
Numerous real-world applications have been driven by the recent algorithmic advancement of artificial intelligence (AI).
Simplifying approach to Node Classification in Graph Neural Networks
In this work, we decouple the node feature aggregation step and depth of graph neural network, and empirically analyze how different aggregated features play a role in prediction performance.
CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing
Network slicing is proposed as a promising solution for resource utilization in 5G and future networks to address this dire need.
Consistency Regularization for Deep Face Anti-Spoofing
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems.
MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification
We propose MASTAF, a Model-Agnostic Spatio-Temporal Attention Fusion network for few-shot video classification.
Leaping Through Time with Gradient-based Adaptation for Recommendation
Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based meta-learning to model time dependencies.
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
RDIM and region fitting do not require extra running time and these three steps can be well integrated into other attacks.
KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation
Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently.
Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching
Based on this observation, we propose dual message passing neural networks (DMPNNs) to enhance the substructure representation learning in an asynchronous way for subgraph isomorphism counting and matching as well as unsupervised node classification.
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation
Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.
Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function
In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of $d$ agents.
MobilePhys: Personalized Mobile Camera-Based Contactless Physiological Sensing
Camera-based contactless photoplethysmography refers to a set of popular techniques for contactless physiological measurement.
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Next, we provide comparisons from aspects of the efficiency and characteristics of these methods.
Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach
Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries.
Multi-Objective Reinforcement Learning
reinforcement-learning
Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention
We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds.
An Efficient Subpopulation-based Membership Inference Attack
The intuition is that the model response should not be significantly different between the target sample and its subpopulation if it was not a training sample.
User-Level Membership Inference Attack against Metric Embedding Learning
In this paper, we develop a user-level MI attack where the goal is to find if any sample from the target user has been used during training even when no exact training sample is available to the attacker.
Federated Remote Physiological Measurement with Imperfect Data
The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic.
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks
Specifically, we evaluated the model performance of representative homogeneous GNN models which consider single-type nodes and edges and heterogeneous GNN models which support different types of nodes and edges.
A Convergence Analysis of Nesterov's Accelerated Gradient Method in Training Deep Linear Neural Networks
To the best of our knowledge, this is the first theoretical guarantee for the convergence of NAG to the global minimum in training deep neural networks.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Selective clustering ensemble based on kappa and F-score
Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years.
An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity
Moreover, we show the relationship between the new model and the original problem.
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
To understand the exploration, exploitation, and engagement in these systems, we propose a new model, called MAB-A where "A" stands for abandonment and the abandonment probability depends on the current recommended item and the user's past experience (called state).
SCAMPS: Synthetics for Camera Measurement of Physiological Signals
The use of cameras and computational algorithms for noninvasive, low-cost and scalable measurement of physiological (e. g., cardiac and pulmonary) vital signs is very attractive.
A Multi-task Framework for Infrared Small Target Detection and Segmentation
First, with the use of UNet as the backbone to maintain resolution and semantic information, our model can achieve a higher detection accuracy than other state-of-the-art methods by attaching a simple anchor-free head.
Boosting Graph Structure Learning with Dummy Nodes
We extend graph kernels and graph neural networks with dummy nodes and conduct experiments on graph classification and subgraph isomorphism matching tasks.
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL).
Cross-Silo Federated Learning: Challenges and Opportunities
Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private.
Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution
Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information.
Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding
Learning point clouds is challenging due to the lack of connectivity information, i. e., edges.
Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning
For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge.
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.
A high-resolution dynamical view on momentum methods for over-parameterized neural networks
Due to the simplicity and efficiency of the first-order gradient method, it has been widely used in training neural networks.
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging
Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject.
A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
Decentralized optimization with orthogonality constraints is found widely in scientific computing and data science.
Rethinking Efficiency and Redundancy in Training Large-scale Graphs
Thereby, we propose to drop redundancy and improve efficiency of training large-scale graphs with GNNs, by rethinking the inherent characteristics in a graph.
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation
Second, we experimentally observe and verify the edge enhancement and suppression behavior.
Ranked #3 on
3D Part Segmentation
on ShapeNet-Part
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism.
rPPG-Toolbox: Deep Remote PPG Toolbox
Camera-based physiological measurement is a fast growing field of computer vision.
ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs
In this paper, we present ByteTransformer, a high-performance transformer boosted for variable-length inputs.
SimPer: Simple Self-Supervised Learning of Periodic Targets
From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes.
MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition
The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.
Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node Classification
With extensive experiments, we show that our proposed model outperforms the state-of-the-art GNN models with remarkable improvements up to 27. 8%.
GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs
First, unlike previous methods, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning.
Self-Supervised Learning via Maximum Entropy Coding
To cope with this issue, we propose Maximum Entropy Coding (MEC), a more principled objective that explicitly optimizes on the structure of the representation, so that the learned representation is less biased and thus generalizes better to unseen downstream tasks.
Opportunistic Episodic Reinforcement Learning
In this paper, we propose and study opportunistic reinforcement learning - a new variant of reinforcement learning problems where the regret of selecting a suboptimal action varies under an external environmental condition known as the variation factor.
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance
Explainability plays an increasingly important role in machine learning.
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia.
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.
Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform
However, the performance of the hyperbolic KG embedding models for non-transitive relations is still unpromising, while the complex hyperbolic embeddings do not deal with multi-relations.
Getting the Most out of Simile Recognition
Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects (i. e., tenors and vehicles).
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery
Understanding users' intentions in e-commerce platforms requires commonsense knowledge.
Quantifying the Impact of Label Noise on Federated Learning
Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets.
Federated Learning Hyper-Parameter Tuning from a System Perspective
In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training.
Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints
This paper studies the problem of stochastic continuum-armed bandit with constraints (SCBwC), where we optimize a black-box reward function $f(x)$ subject to a black-box constraint function $g(x)\leq 0$ over a continuous space $\mathcal X$.
Efficient stereo matching on embedded GPUs with zero-means cross correlation
By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1, 280x384 pixel images with a maximum disparity of 128.
On the Discredibility of Membership Inference Attacks
We argue that current membership inference attacks can identify memorized subpopulations, but they cannot reliably identify which exact sample in the subpopulation was used during the training.
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems
The proposed algorithm is distributed in two aspects: (i) the learned policy is a distributed policy that maps a local state of an agent to its local action and (ii) the learning/training is distributed, during which each agent updates its policy based on its own and neighbors' information.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
C2ST: Cross-Modal Contextualized Sequence Transduction for Continuous Sign Language Recognition
In this paper, we propose a Cross-modal Contextualized Sequence Transduction (C2ST) for CSLR, which effectively incorporates the knowledge of gloss sequence into the process of video representation learning and sequence transduction.
Progressive Neighbor Consistency Mining for Correspondence Pruning
In the end, we develop a Neighbor Consistency Mining Network (NCMNet) for accurately recovering camera poses and identifying inliers.
Feature Selection: Key to Enhance Node Classification with Graph Neural Networks
Graphs help to define the relationships between entities in the data.
Ranked #1 on
Node Classification
on twitch-gamers
Online Nonstochastic Control with Adversarial and Static Constraints
This subroutine also achieves the state-of-the-art regret and constraint violation bounds for constrained online convex optimization problems, which is of independent interest.
MMPD: Multi-Domain Mobile Video Physiology Dataset
Second, most datasets are relatively small and therefore are limited in diversity, both in appearance (e. g., skin tone), behaviors (e. g., motion) and environment (e. g., lighting conditions).
Cross-domain Random Pre-training with Prototypes for Reinforcement Learning
CRPTpro significantly outperforms the next best Proto-RL(C) on 11/12 cross-domain downstream tasks with only 54\% wall-clock pre-training time, exhibiting state-of-the-art pre-training performance with greatly improved pre-training efficiency.
Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing
In this paper, we investigate three key factors (i. e., inputs, pre-training, and finetuning) in ViT for multimodal FAS with RGB, Infrared (IR), and Depth.
X-ray Spectral Estimation using Dictionary Learning
As computational tools for X-ray computed tomography (CT) become more quantitatively accurate, knowledge of the source-detector spectral response is critical for quantitative system-independent reconstruction and material characterization capabilities.
Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning
We construct a large-scale unpaired HDR-LDR video dataset to facilitate the unsupervised training process for video tone mapping.
Facial Affect Recognition based on Transformer Encoder and Audiovisual Fusion for the ABAW5 Challenge
In this paper, we present our solutions for the 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW), which includes four sub-challenges of Valence-Arousal (VA) Estimation, Expression (Expr) Classification, Action Unit (AU) Detection and Emotional Reaction Intensity (ERI) Estimation.
Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement
Our findings illustrate the usefulness of motion transfer as a data augmentation technique for improving the generalization of models for camera-based physiological sensing.
BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements
As an example, perception occurs at different scales both spatially and temporally, suggesting that the extraction of salient visual information may be made more effective by paying attention to specific features at varying scales.
NVAutoNet: Fast and Accurate 360$^{\circ}$ 3D Visual Perception For Self Driving
Achieving robust and real-time 3D perception is fundamental for autonomous vehicles.
