Search Results for author:
Found 142 papers, 42 papers with code
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing
Since the generated samples simulate the low density area for each modal, the discriminator could directly detect anomalies from normal data.
Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection
The survey of translations suggests that the mistakenly-aligned triggers in the expanded data negatively influences the retraining process.
Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias
The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias.
Can Class-Priors Help Single-Positive Multi-Label Learning?
Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label.
Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction
This implies the great potential of the semantic correlations for the entity-independent inductive link prediction task.
Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions
Robust 3D perception under corruption has become an essential task for the realm of 3D vision.
A Ground Segmentation Method Based on Point Cloud Map for Unstructured Roads
Unstructured road scenes represented by open-pit mines have irregular boundary lines and uneven road surfaces, which lead to segmentation errors in current ground segmentation methods.
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Learning a recommender system model from an item's raw modality features (such as image, text, audio, etc.
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment
To achieve this, we introduce a pre-trained LLM to generate context descriptions, and we encourage the prompts to learn from the LLM's knowledge by alignment, as well as the alignment between prompts and local image features.
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
In particular, we notice that the runtime of the Resub and Mfs2 operators often dominates the overall runtime of LS optimization processes.
A Novel Ehanced Move Recognition Algorithm Based on Pre-trained Models with Positional Embeddings
The recognition of abstracts is crucial for effectively locating the content and clarifying the article.
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution
Observing the quality issue of HR images, in this paper we propose a novel idea to boost STISR by first enhancing the quality of HR images and then using the enhanced HR images as supervision to do STISR.
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization
The CDGCN-based clustering method consists of graph generation, sub-graph detection, and Graph-based Overlapped Speech Detection (Graph-OSD).
TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support
Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns.
Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
In this paper, we explore the potential of LLMs such as GPT-4 to outperform traditional AI tools in dementia diagnosis.
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in the item recommendation settings.
Provably Convergent Subgraph-wise Sampling for Fast GNN Training
During the message passing (MP) in GNNs, subgraph-wise sampling methods discard messages outside the mini-batches in backward passes to avoid the well-known neighbor explosion problem, i. e., the exponentially increasing dependencies of nodes with the number of MP iterations.
RAF: Holistic Compilation for Deep Learning Model Training
In this paper, we present RAF, a deep learning compiler for training.
Sparse Bayesian Learning-Based 3D Spectrum Environment Map Construction-Sampling Optimization, Scenario-Dependent Dictionary Construction and Sparse Recovery
Then, a maximum and minimum distance (MMD) clustering-based SBL algorithm is proposed to recover the spectrum data at the unsampled positions and construct the whole 3D SEM.
Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution
The appealing features of RSD-OA include that: (1) RSD-OA is invariant to visual distractions, as it is conditioned on the predefined subsequent action sequence without task-irrelevant information from transition dynamics, and (2) the reward sequence captures long-term task-relevant information in both rewards and transition dynamics.
Variable Selection for Kernel Two-Sample Tests
We consider the variable selection problem for two-sample tests, aiming to select the most informative variables to distinguish samples from two groups.
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions
To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space.
De Novo Molecular Generation via Connection-aware Motif Mining
The obtained motif vocabulary consists of not only molecular motifs (i. e., the frequent fragments), but also their connection information, indicating how the motifs are connected with each other.
LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence
The key idea of LMC is to retrieve the discarded messages in backward passes based on a message passing formulation of backward passes.
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Cut selection -- which aims to select a proper subset of the candidate cuts to improve the efficiency of solving MILPs -- heavily depends on (P1) which cuts should be preferred, and (P2) how many cuts should be selected.
Deep learning numerical methods for high-dimensional fully nonlinear PIDEs and coupled FBSDEs with jumps
We propose a deep learning algorithm for solving high-dimensional parabolic integro-differential equations (PIDEs) and high-dimensional forward-backward stochastic differential equations with jumps (FBSDEJs), where the jump-diffusion process are derived by a Brownian motion and an independent compensated Poisson random measure.
Tagging before Alignment: Integrating Multi-Modal Tags for Video-Text Retrieval
Vision-language alignment learning for video-text retrieval arouses a lot of attention in recent years.
Two Measure is Two Know: Calibration-free Full Duplex Monitoring for Software Radio Platforms
FDMonitor thus uses a bidirectional coupler, a two-channel receiver, and a new source separation algorithm to simultaneously estimate the transmitted signal and the signal incident on the antenna.
Efficient Exploration in Resource-Restricted Reinforcement Learning
In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode.
Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map
To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans.
SportsTrack: An Innovative Method for Tracking Athletes in Sports Scenes
The SportsMOT dataset aims to solve multiple object tracking of athletes in different sports scenes such as basketball or soccer.
LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification
It utilizes score variation as an indicator to detect adversarial examples, where the score variation is the absolute discrepancy between the ASV scores of an original audio recording and its transformed audio synthesized from its masked complex spectrogram.
WeKws: A production first small-footprint end-to-end Keyword Spotting Toolkit
Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an indispensable component of smart devices.
Tag-Set-Sequence Learning for Generating Question-Answer Pairs
Transformer-based QG models can generate question-answer pairs (QAPs) with high qualities, but may also generate silly questions for certain texts.
Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting
This paper describes a spatial-aware speaker diarization system for the multi-channel multi-party meeting.
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection
Existing 3D detectors significantly improve the accuracy by adopting a two-stage paradigm which merely relies on LiDAR point clouds for 3D proposal refinement.
Contrastive Semantic-Guided Image Smoothing Network
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details.
Sparse Polynomial Optimization: Theory and Practice
Fortunately, for many applications, we can look at the problem in the eyes and exploit the inherent data structure arising from the cost and constraints describing the problem, for instance sparsity or symmetries.
Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion
One-shot voice conversion (VC) with only a single target speaker's speech for reference has become a hot research topic.
Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables
The goal of single-step retrosynthesis is to identify the possible reactants that lead to the synthesis of the target product in one reaction.
Improving Pseudo Labels With Intra-Class Similarity for Unsupervised Domain Adaptation
In this paper, we propose a novel approach to improve the accuracy of the pseudo labels in the target domain.
Flow Rate Independent Multiscale Liquid Biopsy for Precision Oncology
Immunoaffinity-based liquid biopsies of circulating tumor cells (CTCs) hold great promise for cancer management, but typically suffer from low throughput, relative complexity and post-processing limitations.
FlowX: Towards Explainable Graph Neural Networks via Message Flows
We investigate the explainability of graph neural networks (GNNs) as a step towards elucidating their working mechanisms.
Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification
A major novelty of SALA is the task-adaptive metric, which can learn the metric adaptively for different tasks in an end-to-end fashion.
Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities
By enforcing the entities' embeddings close to their associated prototypes' embeddings, our approach can effectively encourage the global semantic similarities of entities -- that can be far away in the KG -- connected by the same relation.
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.
Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation
To tackle these problems, we propose a novel Adversarial Knowledge Distillation framework for graph models named GraphAKD, which adversarially trains a discriminator and a generator to adaptively detect and decrease the discrepancy.
Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions
Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios.
Downstream Transformer Generation of Question-Answer Pairs with Preprocessing and Postprocessing Pipelines
We present a system called TP3 to perform a downstream task of transformers on generating question-answer pairs (QAPs) from a given article.
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks
Based on the approximation theory of neural networks, we show that the neural network IPM test has the type-II risk in the order of $n^{-(s+\beta)/d}$, which is in the same order of the type-II risk as the H\"older IPM test.
C3-STISR: Scene Text Image Super-resolution with Triple Clues
In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution.
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings
Semantic matching models -- which assume that entities with similar semantics have similar embeddings -- have shown great power in knowledge graph embeddings (KGE).
Contextual Networks and Unsupervised Ranking of Sentences
We construct a contextual network to represent a document with syntactic and semantic relations between word-sentence pairs, based on which we devise an unsupervised algorithm called CNATAR (Contextual Network And Text Analysis Rank) to score sentences, and rank them through a bi-objective 0-1 knapsack maximization problem over topic analysis and sentence scores.
Boilerplate Detection via Semantic Classification of TextBlocks
We present a hierarchical neural network model called SemText to detect HTML boilerplate based on a novel semantic representation of HTML tags, class names, and text blocks.
An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture Search
However, many previous works employ neural networks with fixed architecture and size to represent different 3D objects, which lead to excessive network parameters for simple objects and limited reconstruction accuracy for complex objects.
A Survey of Neural Trojan Attacks and Defenses in Deep Learning
It provides a comprehensible gateway to the broader community to understand the recent developments in Neural Trojans.
The xmuspeech system for multi-channel multi-party meeting transcription challenge
By performing DMSNet based OSD module, the DER of cluster-based diarization system decrease significantly form 13. 44% to 7. 63%.
A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets
Hypothesis testing for small-sample scenarios is a practically important problem.
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
Surprisingly, we observe from experiments that the graph structure modeling in GCNs does not have a significant impact on the performance of KGC models, which is in contrast to the common belief.
Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems
From the view of object detectors, the traffic sign`s position and quality of the video are continuously changing, rendering the digital AEs ineffective in the physical world.
Learning to Reformulate for Linear Programming
In the past decades, a serial of traditional operation research algorithms have been proposed to obtain the optimum of a given LP in a fewer solving time.
An Improved Reinforcement Learning Algorithm for Learning to Branch
Similar to offline reinforcement learning, we initially train on the demonstration data to accelerate learning massively.
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
Many existing algorithms learn robust policies by modeling the disturbance and applying it to source environments during training, which usually requires prior knowledge about the disturbance and control of simulators.
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds
Fine-grained geometry, captured by aggregation of point features in local regions, is crucial for object recognition and scene understanding in point clouds.
Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction
JAL-MTP use a Social to Lane (S2L) module to jointly represent the static lane and the dynamic motion of the neighboring agents as instance-level lane, a Recurrent Lane Attention (RLA) mechanism for utilizing the instance-level lanes to predict the map-adaptive future trajectories and two selectors to identify the typical and reasonable trajectories.
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
To address this challenge, we propose a novel query embedding model, namely Cone Embeddings (ConE), which is the first geometry-based QE model that can handle all the FOL operations, including conjunction, disjunction, and negation.
Multi-Modal Multi-Instance Learning for Retinal Disease Recognition
We propose in this paper Multi-Modal Multi-Instance Learning (MM-MIL) for selectively fusing CFP and OCT modalities.
Sinkhorn Distributionally Robust Optimization
We study distributionally robust optimization (DRO) with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization.
XMUSPEECH System for VoxCeleb Speaker Recognition Challenge 2021
This paper describes the XMUSPEECH speaker recognition and diarisation systems for the VoxCeleb Speaker Recognition Challenge 2021.
A 60-GHz Radar Sensor for Micron-Scale Motion Detection
A compact, continuous-wave, mmWave radar sensor is developed for non-contact detection of micron-scale motions.
Technical Report of Team GraphMIRAcles in the WikiKG90M-LSC Track of OGB-LSC @ KDD Cup 2021
To address this challenge, we propose a framework that integrates three components -- a basic model ComplEx-CMRC, a rule miner AMIE 3, and an inference model to predict missing links.
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems
Our method is entirely data driven and thus adaptive, i. e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically.
Unsupervised Domain Expansion for Visual Categorization
In this paper we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model's performance on the source domain.
Ranked #1 on
Unsupervised Domain Expansion
on UDE-DomainNet
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers.
Ranked #22 on
Visual Object Tracking
on LaSOT
Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs
TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs.
Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results.
On Explainability of Graph Neural Networks via Subgraph Explorations
To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs.
Adversarially learning disentangled speech representations for robust multi-factor voice conversion
To increase the robustness of highly controllable style transfer on multiple factors in VC, we propose a disentangled speech representation learning framework based on adversarial learning.
UNIT: Unifying Tensorized Instruction Compilation
However, it is hard to leverage mixed precision without hardware support because of the overhead of data casting.
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization
In this paper, we propose an attention-guided black-box adversarial attack based on the large-scale multiobjective evolutionary optimization, termed as LMOA.
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack
The studies on black-box adversarial attacks have become increasingly prevalent due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs).
The simpler the better: vanilla sgd revisited
The stochastic gradient descent (SGD) method, first proposed in 1950's, has been the foundation for deep-neural-network (DNN) training with numerous enhancements including adding a momentum or adaptively selecting learning rates, or using both strategies and more.
Topological charge engineering in lasing bound states in continuum
We experimentally analyze all four observed lasing BICs by imaging their far-field polarization vortices and their associated topological charges.
Optics
Deep Deterministic Policy Gradient for Relay Selection and Power Allocation in Cooperative Communication Network
Perfect channel state information (CSI) is usually required when considering relay selection and power allocation in cooperative communication.
Information Theory Systems and Control Systems and Control Information Theory
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
However, in continuous action spaces, integrating entropy regularization with expressive policies is challenging and usually requires complex inference procedures.
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
Tensor factorization based models have shown great power in knowledge graph completion (KGC).
Ranked #2 on
Link Prediction
on YAGO3-10
A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification
Voxel-based 3D object classification has been thoroughly studied in recent years.
Reliable Off-policy Evaluation for Reinforcement Learning
In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy using logged trajectory data generated from a different behavior policy, without execution of the target policy.
Extracting Body Text from Academic PDF Documents for Text Mining
Accurate extraction of body text from PDF-formatted academic documents is essential in text-mining applications for deeper semantic understandings.
Generating Adequate Distractors for Multiple-Choice Questions
This paper presents a novel approach to automatic generation of adequate distractors for a given question-answer pair (QAP) generated from a given article to form an adequate multiple-choice question (MCQ).
Two-sample Test using Projected Wasserstein Distance: Breaking the Curse of Dimensionality
We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution.
Line Graph Neural Networks for Link Prediction
In this formalism, a link prediction problem is converted to a graph classification task.
When HLS Meets FPGA HBM: Benchmarking and Bandwidth Optimization
With the recent release of High Bandwidth Memory (HBM) based FPGA boards, developers can now exploit unprecedented external memory bandwidth.
Hardware Architecture
Meta Sequence Learning for Generating Adequate Question-Answer Pairs
Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) on the main points of the document.
An Intuitive Tutorial to Gaussian Processes Regression
This tutorial aims to provide an intuitive understanding of the Gaussian processes regression.
Variational Representations and Neural Network Estimation of Rényi Divergences
We further show that this R\'enyi variational formula holds over a range of function spaces; this leads to a formula for the optimizer under very weak assumptions and is also key in our development of a consistency theory for R\'enyi divergence estimators.
DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography
In this study, a convolutional neural network (CNN) based method is proposed to fulfill a DR classification framework using en face OCT and OCTA.
Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning
The effect of including OCTA data for retinal fluid segmentation was investigated in this study.
Review of Text Style Transfer Based on Deep Learning
In the traditional text style transfer model, the text style is generally relied on by experts knowledge and hand-designed rules, but with the application of deep learning in the field of natural language processing, the text style transfer method based on deep learning Started to be heavily researched.
An Unsupervised Semantic Sentence Ranking Scheme for Text Documents
It applies two variants of article-structure-biased PageRank to score phrases and words on the first graph and sentences on the second graph.
Chordal-TSSOS: a moment-SOS hierarchy that exploits term sparsity with chordal extension
The novelty and distinguishing feature of such relaxations is to obtain quasi block-diagonal matrices obtained in an iterative procedure that performs chordal extension of certain adjacency graphs.
Optimization and Control 14P10, 90C25, 12D15, 12Y05
Baryon acoustic oscillations reconstruction using convolutional neural networks
We propose a new scheme to reconstruct the baryon acoustic oscillations (BAO) signal, which contains key cosmological information, based on deep convolutional neural networks (CNN).
TSSOS: A Moment-SOS hierarchy that exploits term sparsity
This paper is concerned with polynomial optimization problems.
Optimization and Control
An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation
In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation.
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)---a novel model-based approach---that can effectively improve the asymptotic performance using the uncertainty in Q-values.
Model-based Reinforcement Learning
reinforcement-learning
+1
D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problems
To the best of our knowledge, D-SPIDER-SFO achieves the state-of-the-art performance for solving nonconvex optimization problems on decentralized networks in terms of the computational cost.
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.
Ranked #1 on
Knowledge Graph Completion
on WN18RR
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
Reversible Adversarial Attack based on Reversible Image Transformation
In order to prevent illegal or unauthorized access of image data such as human faces and ensure legitimate users can use authorization-protected data, reversible adversarial attack technique is rise.
Generating an Overview Report over Many Documents
To overcome this obstacle, we present NDORGS (Numerous Documents' Overview Report Generation Scheme) that integrates text filtering, keyword scoring, single-document summarization (SDS), topic modeling, MDS, and title generation to generate a coherent, well-structured ORPT.
Analyzing Linguistic Complexity and Scientific Impact
In this context, scientific writing increasingly plays an important role in scholars' scientific careers.
Cross-Platform Modeling of Users' Behavior on Social Media
Structured and unstructured data of same users shared by NetEase Music and Sina Weibo have been collected for cross-platform analysis of correlations between music preference and other users' characteristics.
A Syllable-Structured, Contextually-Based Conditionally Generation of Chinese Lyrics
This paper presents a novel, syllable-structured Chinese lyrics generation model given a piece of original melody.
Automatic Conditional Generation of Personalized Social Media Short Texts
Automatic text generation has received much attention owing to rapid development of deep neural networks.
Automatic Acrostic Couplet Generation with Three-Stage Neural Network Pipelines
In this paper, we comprehensively study on automatic generation of acrostic couplet with the first characters defined by users.
A Hierarchical Attention Based Seq2seq Model for Chinese Lyrics Generation
In this paper, we comprehensively study on context-aware generation of Chinese song lyrics.
Deep learning based mood tagging for Chinese song lyrics
Nowadays, listening music has been and will always be an indispensable part of our daily life.
Theme-aware generation model for chinese lyrics
With rapid development of neural networks, deep-learning has been extended to various natural language generation fields, such as machine translation, dialogue generation and even literature creation.
SvTPM: A Secure and Efficient vTPM in the Cloud
In this paper, we present SvTPM, a secure and efficient software-based vTPM implementation based on hardware-rooted Trusted Execution Environment (TEE), providing a whole life cycle protection of vTPMs in the cloud.
Cryptography and Security
An Efficient Pre-processing Method to Eliminate Adversarial Effects
Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated by imposing subtle perturbations to inputs that lead a model to predict incorrect outputs.
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Segmentation stands at the forefront of many high-level vision tasks.
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power.
Mexican Hat Wavelet Kernel ELM for Multiclass Classification
Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems.
Semantic WordRank: Generating Finer Single-Document Summarizations
We present Semantic WordRank (SWR), an unsupervised method for generating an extractive summary of a single document.
Examining Scientific Writing Styles from the Perspective of Linguistic Complexity
The observations suggest marginal differences between groups in syntactical and lexical complexity.
Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality
We show that the orderings of the ROUGE and WESM scores of our algorithms are highly comparable, suggesting that WESM may serve as a viable alternative for measuring the quality of a summary.
DTATG: An Automatic Title Generator based on Dependency Trees
We study automatic title generation for a given block of text and present a method called DTATG to generate titles.
Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Then we select the relevant group features by performing the group Lasso feature selection process in a sequence of parameters.
Probabilistic graphical model based approach for water mapping using GaoFen-2 (GF-2) high resolution imagery and Landsat 8 time series
Difficulties of water mapping on high resolution data includes: 1) misclassification between water and shadows or other low-reflectance ground objects, which is mostly caused by the spectral similarity within the given band range; 2) small water bodies with size smaller than the spatial resolution of MS image.
A probabilistic graphical model approach in 30 m land cover mapping with multiple data sources
Conditional probability distributions were computed based on data quality and reliability by using information selectively.
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD.
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction
By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the inactive features and samples that are guaranteed to be irrelevant to the outputs.
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection
By a novel hierarchical projection algorithm, MLFre is able to test the nodes independently from any of their ancestor nodes.
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices
One of the appealing features of DPC is that: it is safe in the sense that the detected inactive features are guaranteed to have zero coefficients in the solution vectors across all tasks.
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique for simultaneously discovering group and within-group sparse patterns by using a combination of the $\ell_1$ and $\ell_2$ norms.
Scaling SVM and Least Absolute Deviations via Exact Data Reduction
Some appealing features of our screening method are: (1) DVI is safe in the sense that the vectors discarded by DVI are guaranteed to be non-support vectors; (2) the data set needs to be scanned only once to run the screening, whose computational cost is negligible compared to that of solving the SVM problem; (3) DVI is independent of the solvers and can be integrated with any existing efficient solvers.
Safe Screening With Variational Inequalities and Its Application to LASSO
Safe screening is gaining increasing attention since 1) solving sparse learning formulations usually has a high computational cost especially when the number of features is large and 2) one needs to try several regularization parameters to select a suitable model.
A Safe Screening Rule for Sparse Logistic Regression
The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection.
Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods
One key building block of the proposed algorithm is the l1q-regularized Euclidean projection (EP_1q).
Lasso Screening Rules via Dual Polytope Projection
To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed the SAFE rules which are able to quickly identify the inactive predictors, i. e., predictors that have $0$ components in the solution vector.
