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Found 120 papers, 41 papers with code
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
Diversity-Ensemble and Region Fitting","We introduce a three stage pipeline: resized-diverse-inputs (RDIM), diversity-ensemble (DEM) and region fitting, that work together to generate transferable adversarial examples.
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.
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Finally, we emphatically conduct experiments on large graph datasets to analyze the relevance between the training time, model accuracy, and hardware-level metrics, which helps achieve a good trade-off between time and accuracy in GNN training.
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.
MMChat: Multi-Modal Chat Dataset on Social Media
We develop a benchmark model to address this issue in dialogue generation tasks by adapting the attention routing mechanism on image features.
Provable Convergence of Nesterov Accelerated Method for Over-Parameterized Neural Networks
Specifically, we prove that the error of NAG converges to zero at a linear convergence rate $1-\Theta(1/\sqrt{\kappa})$, where $\kappa > 1$ is determined by the initialization and the architecture of neural network.
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).
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.
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.
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.
A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processes
This paper presents the first {\em model-free}, {\em simulator-free} reinforcement learning algorithm for Constrained Markov Decision Processes (CMDPs) with sublinear regret and zero constraint violation.
Exploring Discourse Structures for Argument Impact Classification
Discourse relations among arguments reveal logical structures of a debate conversation.
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 #1 on
Node Classification
on Chameleon
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.
Accuracy-Privacy Trade-off in Deep Ensembles
Ensemble learning has also been used to defend against membership inference attacks that undermine privacy.
Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantification of those differences outside of the gait laboratory has been elusive.
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.
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.
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.
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.
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.
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.
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.
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.
Balanced Softmax Cross-Entropy for Incremental Learning
Deep neural networks are prone to catastrophic forgetting when incrementally trained on new classes or new tasks as adaptation to the new data leads to a drastic decrease of the performance on the old classes and tasks.
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.
Deep Manifold Learning for Dynamic MR Imaging
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
Annotation-efficient deep learning for automatic medical image segmentation
Automatic medical image segmentation plays a critical role in scientific research and medical care.
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
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.
AGNet: Weighing Black Holes with Machine Learning
Supermassive black holes (SMBHs) are ubiquitously found at the centers of most galaxies.
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.
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.
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.
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.
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.
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.
Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars
Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring.
POND: Pessimistic-Optimistic oNline Dispatching
This paper considers constrained online dispatching with unknown arrival, reward and constraint distributions.
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.
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging.
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.
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
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.
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.
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.
Making Adversarial Examples More Transferable and Indistinguishable
Many previous methods generate adversarial examples based on the fast gradient sign attack series.
Deep Low-rank Prior in Dynamic MR Imaging
The deep learning methods have achieved attractive performance in dynamic MR cine imaging.
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.
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 $\mathcal{G}$.
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.
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.
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.
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 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.
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.
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.
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.
Neural Subgraph Isomorphism Counting
In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.
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.
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.
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.
VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks
Our approach can effectively improve the structural sparsity of residual models.
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.
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.
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.
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.
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.
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.
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.
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.
ASER: A Large-scale Eventuality Knowledge Graph
Understanding human's language requires complex world knowledge.
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.
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.
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).
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.
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.
Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification
Hierarchical text classification has many real-world applications.
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.
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.
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.
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.
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
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
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.
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.
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.
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.
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.
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
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.
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.
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).
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.
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.
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 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.
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.
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.
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 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.
VIPLFaceNet: An Open Source Deep Face Recognition SDK
Robust face representation is imperative to highly accurate face recognition.
Ranked #22 on
Face Verification
on Labeled Faces in the Wild
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).
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.
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.
Double Thompson Sampling for Dueling Bandits
This simple algorithm applies to general Copeland dueling bandits, including Condorcet dueling bandits as its special case.
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.
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.
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.