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Found 523 papers, 170 papers with code
Parallelizing Support Vector Machines on Distributed Computers
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time.
MPBART - Multinomial Probit Bayesian Additive Regression Trees
This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)).
Location Recommendation in Location-based Social Networks using User Check-in Data
The proposed algorithms outperform traditional recommendation algorithms and other approaches that try to exploit LBSN information.
Collaborative Deep Learning for Recommender Systems
(CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Sentiment Expression via Emoticons on Social Media
The results confirms the arguments that: 1) a few emoticons are strong and reliable signals of sentiment polarity and one should take advantage of them in any senti- ment analysis; 2) a large group of the emoticons conveys com- plicated sentiment hence they should be treated with extreme caution.
A System for Extracting Sentiment from Large-Scale Arabic Social Data
This paper describes an enterprise system we developed for extracting sentiment from large volumes of social data in Arabic dialects.
A Survey on Bayesian Deep Learning
The past decade has seen major advances in many perception tasks such as visual object recognition and speech recognition using deep learning models.
Online Reconstruction of Indoor Scenes From RGB-D Streams
A system capable of performing robust online volumetric reconstruction of indoor scenes based on input from a handheld RGB-D camera is presented.
Bayesian quantile additive regression trees
Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors.
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence.
Evolving the Structure of Evolution Strategies
Various variants of the well known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) have been proposed recently, which improve the empirical performance of the original algorithm by structural modifications.
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models.
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting.
A Unified Framework for Community Detection and Network Representation Learning
Network representation learning (NRL) aims to learn low-dimensional vectors for vertices in a network.
Social and Information Networks Physics and Society
Combining fast\_align with Hierarchical Sub-sentential Alignment for Better Word Alignments
fast align is a simple and fast word alignment tool which is widely used in state-of-the-art machine translation systems.
Cluster-based Kriging Approximation Algorithms for Complexity Reduction
In addition, four Kriging approximation algorithms are proposed as candidate algorithms within the new framework.
Reinforcement Learning Based Argument Component Detection
Argument component detection (ACD) is an important sub-task in argumentation mining.
ZM-Net: Real-time Zero-shot Image Manipulation Network
We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones).
Transfer of View-manifold Learning to Similarity Perception of Novel Objects
We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience.
Crowdsourcing Argumentation Structures in Chinese Hotel Reviews
Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts.
Joint RNN Model for Argument Component Boundary Detection
Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline.
ResumeVis: A Visual Analytics System to Discover Semantic Information in Semi-structured Resume Data
Then, a set of visualizations are devised to represent the semantic information in multiple perspectives.
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space.
Dual Based DSP Bidding Strategy and its Application
In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method.
Face R-CNN
Faster R-CNN is one of the most representative and successful methods for object detection, and has been becoming increasingly popular in various objection detection applications.
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
To address these problems, we propose both a new model and a benchmark for precipitation nowcasting.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Real-Time Neural Style Transfer for Videos
More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames.
Using Argument-based Features to Predict and Analyse Review Helpfulness
We study the helpful product reviews identification problem in this paper.
A Transition-based System for Universal Dependency Parsing
This paper describes the system for our participation in the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.
Exploit imaging through opaque wall via deep learning
Imaging through scattering media is encountered in many disciplines or sciences, ranging from biology, mesescopic physics and astronomy.
Detecting Faces Using Region-based Fully Convolutional Networks
Face detection has achieved great success using the region-based methods.
Ranked #2 on
Face Detection
on FDDB
Detecting Faces Using Inside Cascaded Contextual CNN
Deep Convolutional Neural Networks (CNNs) achieve substantial improvements in face detection in the wild.
Privacy with Estimation Guarantees
Here, an analyst is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility), while other private functions should not be reconstructed with distortion below a certain threshold (privacy).
Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning
In this paper, we derive a temporal arbitrage policy for storage via reinforcement learning.
Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes
We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes.
Fast BTG-Forest-Based Hierarchical Sub-sentential Alignment
In this paper, we propose a novel BTG-forest-based alignment method.
Parameter-free $\ell_p$-Box Decoding of LDPC Codes
The Alternating Direction Method of Multipliers (ADMM) decoding of Low Density Parity Check (LDPC) codes has received many attentions due to its excellent performance at the error floor region.
Information Theory Information Theory
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning
In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive attribute (e. g., race or gender).
CosFace: Large Margin Cosine Loss for Deep Face Recognition
The central task of face recognition, including face verification and identification, involves face feature discrimination.
Ranked #3 on
Face Verification
on YouTube Faces DB
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint.
Multi-scale Location-aware Kernel Representation for Object Detection
Our MLKP can be efficiently computed on a modified multi-scale feature map using a low-dimensional polynomial kernel approximation. Moreover, different from existing orderless global representations based on high-order statistics, our proposed MLKP is location retentive and sensitive so that it can be flexibly adopted to object detection.
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.
l0-norm Based Centers Selection for Training Fault Tolerant RBF Networks and Selecting Centers
The aim of this paper is to train an RBF neural network and select centers under concurrent faults.
Fast L1-Minimization Algorithm for Sparse Approximation Based on an Improved LPNN-LCA framework
Simulation results show that the proposed sparse approximation method has the real-time solutions with satisfactory MSEs.
A Neural Question Answering Model Based on Semi-Structured Tables
Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer.
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+λ)$ EA Variants on OneMax and LeadingOnes
Marking an important step towards filling this gap, we adjust the COCO software to pseudo-Boolean optimization problems, and obtain from this a benchmarking environment that allows a fine-grained empirical analysis of discrete black-box heuristics.
Deep Inferential Spatial-Temporal Network for Forecasting Air Pollution Concentrations
Air pollution poses a serious threat to human health as well as economic development around the world.
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning
The prosperity of smart mobile devices has made mobile crowdsensing (MCS) a promising paradigm for completing complex sensing and computation tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Automatic Configuration of Deep Neural Networks with EGO
In this paper an Efficient Global Optimization (EGO) algorithm is adapted to automatically optimize and configure convolutional neural network architectures.
IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
Given as input algorithms and problems written in C or Python, it provides as output a statistical evaluation of the algorithms' performance by means of the distribution on the fixed-target running time and the fixed-budget function values.
Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition
Extensive experiments conducted on the three public domain face aging datasets (MORPH Album 2, CACD-VS and FG-NET) have shown the effectiveness of the proposed approach and the value of the constructed CAF dataset on AIFR.
Ranked #3 on
Age-Invariant Face Recognition
on MORPH Album2
Nonconvex and Nonsmooth Sparse Optimization via Adaptively Iterative Reweighted Methods
We propose a general formulation of nonconvex and nonsmooth sparse optimization problems with convex set constraint, which can take into account most existing types of nonconvex sparsity-inducing terms, bringing strong applicability to a wide range of applications.
A Hardware Friendly Unsupervised Memristive Neural Network with Weight Sharing Mechanism
Memristive neural networks (MNNs), which use memristors as neurons or synapses, have become a hot research topic recently.
Global-to-Local Generative Model for 3D Shapes
We also introduce new means to measure and evaluate the performance of an adversarial generative model.
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
When the performance of a machine learning model varies over groups defined by sensitive attributes (e. g., gender or ethnicity), the performance disparity can be expressed in terms of the probability distributions of the input and output variables over each group.
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling
We consider the problem of inferring the values of an arbitrary set of variables (e. g., risk of diseases) given other observed variables (e. g., symptoms and diagnosed diseases) and high-dimensional signals (e. g., MRI images or EEG).
The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction
In this paper, a margin-based Pareto deep ensemble pruning (MBPEP) model is proposed.
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification
Two-stream architecture have shown strong performance in video classification task.
Decorrelated Adversarial Learning for Age-Invariant Face Recognition
To reduce such a discrepancy, in this paper we propose a novel algorithm to remove age-related components from features mixed with both identity and age information.
Ranked #3 on
Age-Invariant Face Recognition
on CACDVS
Fully Memristive Neural Network Merging Image Preprocessing and Pattern Recognition
With the development of research on novel memristor model and device, fully memristive neural networks have become a hot research topic recently.
Emerging Technologies
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
Probabilistic modelling is a principled framework to perform model aggregation, which has been a primary mechanism to combat mode collapse in the context of Generative Adversarial Networks (GAN).
Ranked #22 on
Image Generation
on STL-10
AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions
In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning.
Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle.
Ranked #8 on
Cross-Modal Retrieval
on Recipe1M
Semantic Adversarial Network for Zero-Shot Sketch-Based Image Retrieval
Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario.
Data augmentation in microscopic images for material data mining
Recent progress in material data mining has been driven by high-capacity models trained on large datasets.
WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network
In this work, we propose WPU-net that redesigns the architecture and weighted loss of U-Net, which forces the network to integrate information from adjacent slices and pays more attention to the topology in boundary detection task.
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention.
Spectral Perturbation Meets Incomplete Multi-view Data
In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering.
Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
For guiding our IS to obtain better object performance, we explore issues of instance imbalance and class importance in datasets, which frequently occur and bring adverse effect on detection performance.
Forward and Backward Knowledge Transfer for Sentiment Classification
That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks.
Confluent-Drawing Parallel Coordinates: Web-Based Interactive Visual Analytics of Large Multi-Dimensional Data
In this paper, we propose a confluent drawing approach of parallel coordinates to support the web-based interactive visual analytics of large multi-dimensional data.
Virtual Energy Storage Sharing and Capacity Allocation
In our simulation results, the proposed storage virtualization model can reduce the physical energy storage investment of the aggregator by 54. 3% and reduce the users' total costs by 34. 7%, compared to the case where users acquire their own physical storage.
Semi-supervised representation learning via dual autoencoders for domain adaptation
Domain adaptation aims to exploit the knowledge in source domain to promote the learning tasks in target domain, which plays a critical role in real-world applications.
Learning with Noisy Labels for Sentence-level Sentiment Classification
We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training.
Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies
The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing.
Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities
We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA.
Conditional Text Generation for Harmonious Human-Machine Interaction
In recent years, with the development of deep learning, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication.
Detection of fraudulent users in P2P financial market
In this paper we illustrate how we tackle the fraud detection problem at HC Financial.
Quantitative analysis of Matthew effect and sparsity problem of recommender systems
Two of these problems are Matthew effect and sparsity problem.
DG-GAN: the GAN with the duality gap
Generative Adversarial Networks (GANs) are powerful, but difficult to understand and train because GANs is a min-max problem.
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.
Optimal Delivery with Budget Constraint in E-Commerce Advertising
Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals.
Asymmetric Cross-Guided Attention Network for Actor and Action Video Segmentation From Natural Language Query
To address these issues, we propose an asymmetric cross-guided attention network for actor and action video segmentation from natural language query.
Ranked #12 on
Referring Expression Segmentation
on J-HMDB
Towards Efficient Local Causal Structure Learning
To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS.
FIS-GAN: GAN with Flow-based Importance Sampling
Generative Adversarial Networks (GAN) training process, in most cases, apply Uniform or Gaussian sampling methods in the latent space, which probably spends most of the computation on examples that can be properly handled and easy to generate.
Joint Hölder continuity of local time for a class of interacting branching measure valued diffusions
Using a Tanaka representation of the local time for a class of superprocesses with dependent spatial motion, as well as sharp estimates from the theory of uniformly parabolic partial differential equations, the joint H\"older continuity in time and space of said local times is obtained in two and three dimensional Euclidean space.
Probability Primary 60J68, 60J80, Secondary 60H15, 60K35, 60K37
Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information
The validation experiments using three large datasets of very high-resolution (VHR) satellite imagery show that the proposed method can improve road extraction accuracy and provide an output that is more in line with human expectations.
Causality-based Feature Selection: Methods and Evaluations
It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system.
Inexact Primal-Dual Gradient Projection Methods for Nonlinear Optimization on Convex Set
In this paper, we propose a novel primal-dual inexact gradient projection method for nonlinear optimization problems with convex-set constraint.
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting
Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision.
Chinese Spelling Error Detection Using a Fusion Lattice LSTM
Spelling error detection serves as a crucial preprocessing in many natural language processing applications.
Relating lp regularization and reweighted l1 regularization
We propose a general framework of iteratively reweighted l1 methods for solving lp regularization problems.
Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts
To leverage both syntactic indicators and sentential contexts, we propose an indicator-aware approach for relation extraction.
Ranked #7 on
Relation Extraction
on SemEval-2010 Task-8
Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES
In this work we compare sequential and integrated algorithm selection and configuration approaches for the case of selecting and tuning the best out of 4608 variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) tested on the Black Box Optimization Benchmark (BBOB) suite.
Benchmarking Discrete Optimization Heuristics with IOHprofiler
Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems.
Causal Discovery from Incomplete Data: A Deep Learning Approach
As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs.
A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation
In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem.
Bayesian Neural Architecture Search using A Training-Free Performance Metric
Recurrent neural networks (RNNs) are a powerful approach for time series prediction.
To Split or Not to Split: The Impact of Disparate Treatment in Classification
To evaluate the effect of disparate treatment, we compare the performance of split classifiers (i. e., classifiers trained and deployed separately on each group) with group-blind classifiers (i. e., classifiers which do not use a sensitive attribute).
Sparse Optimization for Green Edge AI Inference
By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem.
Towards information-rich, logical text generation with knowledge-enhanced neural models
Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life.
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism
Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc.
Ranked #7 on
Cross-Modal Retrieval
on Recipe1M
Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval
Specifically, with the supervision of original semantic knowledge, PDFD decomposes visual features into domain features and semantic ones, and then the semantic features are projected into common space as retrieval features for ZS-SBIR.
Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries
Actor and action video segmentation with language queries aims to segment out the expression referred objects in the video.
Ranked #10 on
Referring Expression Segmentation
on J-HMDB
Secret Sharing based Secure Regressions with Applications
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.
Towards Persona-Based Empathetic Conversational Models
To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
Deep Hierarchical Classification for Category Prediction in E-commerce System
In e-commerce system, category prediction is to automatically predict categories of given texts.
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry.
Benchmarking a $(μ+λ)$ Genetic Algorithm with Configurable Crossover Probability
Moreover, we observe that the ``fast'' mutation scheme with its are power-law distributed mutation strengths outperforms standard bit mutation on complex optimization tasks when it is combined with crossover, but performs worse in the absence of crossover.
Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case
One of the most challenging problems in evolutionary computation is to select from its family of diverse solvers one that performs well on a given problem.
A Modular Hybridization of Particle Swarm Optimization and Differential Evolution
In swarm intelligence, Particle Swarm Optimization (PSO) and Differential Evolution (DE) have been successfully applied in many optimization tasks, and a large number of variants, where novel algorithm operators or components are implemented, has been introduced to boost the empirical performance.
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification
This paper studies the task of comparative preference classification (CPC).
Continuously Index Domain Adaptation
Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has been successfully applied in various fields, e. g., automated machine learning and design optimization.
Continuously Indexed Domain Adaptation
Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
Ranked #1 on
Domain Adaptation
on Rotating MNIST
Task-agnostic Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory signals and prior knowledge, consequently becoming a recognizable concept or object through coupling and transformation of these percepts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.
Decomposing Generation Networks with Structure Prediction for Recipe Generation
Recipe generation from food images and ingredients is a challenging task, which requires the interpretation of the information from another modality.
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective
Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug.
Domain-specific Communication Optimization for Distributed DNN Training
Communication overhead poses an important obstacle to distributed DNN training and draws increasing attention in recent years.
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.
Structure-Aware Generation Network for Recipe Generation from Images
We investigate an open research task of generating cooking instructions based on only food images and ingredients, which is similar to the image captioning task.
Detection Defense Against Adversarial Attacks with Saliency Map
Our experimental results of some representative adversarial attacks on common datasets including ImageNet and popular models show that our method can detect all the attacks with high detection success rate effectively.
Contract-based Time-of-use Pricing for Energy Storage Investment
We also show that the proposed contracts can reduce the system social cost by over 30%, compared with no storage investment benchmark.
Systems and Control Systems and Control
Deep Multi-Interest Network for Click-through Rate Prediction
Then the Multi-Interest Extractor Layer is applied to extract multiple user interests.
Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples
With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification.
Towards Natural Bilingual and Code-Switched Speech Synthesis Based on Mix of Monolingual Recordings and Cross-Lingual Voice Conversion
With these data, three neural TTS models -- Tacotron2, Transformer and FastSpeech are applied for building bilingual and code-switched TTS.
COSEA: Convolutional Code Search with Layer-wise Attention
However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.
Cooperative Energy Management of HVAC via Transactive Energy
However, how to scale up HVAC energy management for a group of users while persevering users' privacy remains a big challenge.
Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies
Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure.
Neural Network Design: Learning from Neural Architecture Search
Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests.
MatRec: Matrix Factorization for Highly Skewed Dataset
Recommender systems is one of the most successful AI technologies applied in the internet cooperations.
Learning causal representations for robust domain adaptation
In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain data is available, called robust domain adaptation.
Face Forgery Detection by 3D Decomposition
Detecting digital face manipulation has attracted extensive attention due to fake media's potential harms to the public.
Argumentation Mining on Essays at Multi Scales
Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level.
On Statistical Analysis of MOEAs with Multiple Performance Indicators
Assessing the empirical performance of Multi-Objective Evolutionary Algorithms (MOEAs) is vital when we extensively test a set of MOEAs and aim to determine a proper ranking thereof.
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification
After each sentiment classification task is learned, its knowledge is retained to help future task learning.
Learning Spatial Attention for Face Super-Resolution
Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e. g., $16\times16$).
Federated Learning for Personalized Humor Recognition
In this paper, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL).
Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge.
Keyword-Guided Neural Conversational Model
We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast.
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.
Catalytically Potent and Selective Clusterzymes for Modulation of Neuroinflammation Through Single-Atom Substitutions
Emerging artificial enzymes with reprogrammed and augmented catalytic activity and substrate selectivity have long been pursued with sustained efforts.
Biological Physics Medical Physics
Generative Interventions for Causal Learning
We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts.
Ranked #44 on
Image Classification
on ObjectNet
(using extra training data)
HCGrid: A Convolution-based Gridding Framework for RadioAstronomy in Hybrid Computing Environments
Gridding operation, which is to map non-uniform data samples onto a uniformly distributedgrid, is one of the key steps in radio astronomical data reduction process.
Instrumentation and Methods for Astrophysics
Self-Supervised Vessel Segmentation via Adversarial Learning
Vessel segmentation is critically essential for diagnosinga series of diseases, e. g., coronary artery disease and retinal disease.
Recurrent Exploration Networks for Recommender Systems
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems.
Towards An Efficient Approach for the Nonconvex $\ell_p$ Ball Projection: Algorithm and Analysis
This paper primarily focuses on computing the Euclidean projection of a vector onto the $\ell_{p}$ ball in which $p\in(0, 1)$.
Learning Guided Electron Microscopy with Active Acquisition
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection.
Privacy-Preserving Transactive Energy Management for IoT-aided Smart Homes via Blockchain
Further, we design an efficient blockchain system tailored for IoT devices and develop the smart contract to support the holistic transactive energy management system.
Network Clustering for Multi-task Learning
The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task.
Towards Natural and Controllable Cross-Lingual Voice Conversion Based on Neural TTS Model and Phonetic Posteriorgram
Cross-lingual voice conversion (VC) is an important and challenging problem due to significant mismatches of the phonetic set and the speech prosody of different languages.
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
In this paper, we analyze the generalization of models trained by noisy iterative algorithms.
Domain Adaptation for Time Series Forecasting via Attention Sharing
Recently, deep neural networks have gained increasing popularity in the field of time series forecasting.
Temporal Memory Attention for Video Semantic Segmentation
Video semantic segmentation requires to utilize the complex temporal relations between frames of the video sequence.
Ranked #1 on
Video Semantic Segmentation
on Cityscapes val
Delving into Deep Imbalanced Regression
We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range.
Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion
Joint event and causality extraction is a challenging yet essential task in information retrieval and data mining.
Residual-Aided End-to-End Learning of Communication System without Known Channel
However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system.
Calibrating User Response Predictions in Online Advertising
Due to the sparsity and latency of the user response behaviors such as clicks and conversions, traditional calibration methods may not work well in real-world online advertising systems.
Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules
However, when introducing a new component into an existing algorithm, assessing its potential benefits is a challenging task.
Towards Efficient Local Causal Structure Learning
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data.
Potential Advantages of Peak Picking Multi-Voltage Threshold Digitizer in Energy Determination in Radiation Measurement
After processing 30, 000 randomly-chosen pulses sampled by the oscilloscope with a 22Na point source, our method achieves an energy resolution of 17. 50% within a 450-650 KeV energy window, which is 2. 44% better than the result of traditional MVT with same thresholds; and we get a count number at 15225 in the same energy window while the result of MVT is at 14678.
Any Part of Bayesian Network Structure Learning
We study an interesting and challenging problem, learning any part of a Bayesian network (BN) structure.
Adversarial Attacks are Reversible with Natural Supervision
We find that images contain intrinsic structure that enables the reversal of many adversarial attacks.
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.
Scene Text Retrieval via Joint Text Detection and Similarity Learning
Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter.
An Iteratively Reweighted Method for Sparse Optimization on Nonconvex $\ell_{p}$ Ball
Specifically, if the current iterate is in the interior of the feasible set, then the weighted $\ell_{1}$ ball is formed by linearizing the $\ell_{p}$ norm at the current iterate.
Machine Learning Approach to Uncovering Residential Energy Consumption Patterns Based on Socioeconomic and Smart Meter Data
We model consumption patterns by representative loads and reveal the relationship between load patterns and socioeconomic characteristics.
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants
From our analysis, we saw that the features can classify the CMA-ES variants, or the function groups decently, and show a potential for predicting the performance of those variants.
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
Blockchain-Based Decentralized Energy Management Platform for Residential Distributed Energy Resources in A Virtual Power Plant
In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP.
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage
This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries.
Covariance Matrix Adaptation Evolution Strategy Assisted by Principal Component Analysis
Over the past decades, more and more methods gain a giant development due to the development of technology.
Is there Anisotropy in Structural Bias?
We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech
In contrast to conventional pipeline Spoken Language Understanding (SLU) which consists of automatic speech recognition (ASR) and natural language understanding (NLU), end-to-end SLU infers the semantic meaning directly from speech and overcomes the error propagation caused by ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Learning to Ask Appropriate Questions in Conversational Recommendation
Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations.
Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution
Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly.
KECRS: Towards Knowledge-Enriched Conversational Recommendation System
The chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions.
Zero-Shot Recommender Systems
This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RS.
Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification
Compared with the state-of-the-art methods, our proposed PS-HRNet improves 3. 4\%, 6. 2\%, 2. 5\%, 1. 1\% and 4. 2\% at Rank-1 on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets, respectively.
Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning
First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information.
Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction
We demonstrate that our approach can boost the fairness of the algorithm and enhances performance evaluated by traditional metrics.
Characterizing Residential Load Patterns by Household Demographic and Socioeconomic Factors
We develop a deep neural network (DNN) to analyze the relationship between users' load patterns and their demographic and socioeconomic features.
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction.
Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
In this work, we propose a new initialization scheme for user and item embeddings called Laplacian Eigenmaps with Popularity-based Regularization for Isolated Data (LEPORID).
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation.
