Search Results for author:
Found 142 papers, 39 papers with code
MPII: Multi-Level Mutual Promotion for Inference and Interpretation
In this paper, we propose a multi-level Mutual Promotion mechanism for self-evolved Inference and sentence-level Interpretation (MPII).
Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification
The combination of both should provide new insights for deep learning based image diagnosis.
Uncovering and Categorizing Social Biases in Text-to-SQL
In this work, we aim to uncover and categorize social biases in Text-to-SQL models.
Uncovering and Quantifying Social Biases in Code Generation
In this work, we explore the social bias problem in pre-trained code generation models.
Detecting Out-of-Context Multimodal Misinformation with interpretable neural-symbolic model
Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions.
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc.
A novel efficient Multi-view traffic-related object detection framework
With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception.
Parallel Sentence-Level Explanation Generation for Real-World Low-Resource Scenarios
However, current works pursuing sentence-level explanations rely heavily on annotated training data, which limits the development of interpretability to only a few tasks.
Estimating Treatment Effects in Continuous Time with Hidden Confounders
Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making.
Personalized Interpretable Classification
In addition to the problem formulation on this new issue, we present a greedy algorithm called PIC (Personalized Interpretable Classifier) to identify a personalized rule for each individual test sample.
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers
Advances in representation learning have led to great success in understanding and generating data in various domains.
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods.
D$^3$ETR: Decoder Distillation for Detection Transformer
Specifically, Adaptive Matching applies bipartite matching to adaptively match the outputs of the teacher and the student in each decoder layer, while Fixed Matching fixes the correspondence between the outputs of the teacher and the student with the same object queries, with the teacher's fixed object queries fed to the decoder of the student as an auxiliary group.
Significance-Based Categorical Data Clustering
As a by-product, we can further calculate an empirical $p$-value to assess the statistical significance of a set of clusters and develop an improved gap statistic for estimating the cluster number.
Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality
The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation.
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
To address these issues, in this paper, we build up a causal framework that model the causal effect of misinformation from the perspective of temporal point process.
Transition to Adulthood for Young People with Intellectual or Developmental Disabilities: Emotion Detection and Topic Modeling
This study is to explore how to use natural language processing (NLP) methods, especially unsupervised machine learning, to assist psychologists to analyze emotions and sentiments and to use topic modeling to identify common issues and challenges that young people with IDD and their families have.
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
There is currently a large gap in performance between the statistically rigorous methods like linear regression or additive splines and the powerful deep methods using neural networks.
Automated Utterance Labeling of Conversations Using Natural Language Processing
Conversational data is essential in psychology because it can help researchers understand individuals cognitive processes, emotions, and behaviors.
SsaA: A Self-supervised auto-Annotation System for Online Visual Inspection and Manufacturing Automation
To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios.
3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis
This framework is applied to the prediction of the malignancy of lung's nodules.
EGCN: An Ensemble-based Learning Framework for Exploring Effective Skeleton-based Rehabilitation Exercise Assessment
We also examine the properness of existing evaluation criteria and focus on evaluating the prediction ability of our proposed method.
Ranked #1 on
Action Assessment
on KIMORE
I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding
To this end, we develop an Importance Sampling based distance metric -- I-SEA -- which enjoys the properties of a metric while consistently achieving superior performance for machine learning tasks such as classification and representation learning.
Policy Learning under Endogeneity Using Instrumental Variables
To this end, we incorporate the marginal treatment effects (MTE) when identifying treatment effect parameters and consider encouragement rules that affect social welfare through treatment take-up when designing policies.
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos
Upon aggregating the results of multiple modalities, our method is found to outperform state-of-the-art approaches on six evaluation protocols of the five datasets; thus, the proposed MMNet can effectively capture mutually complementary features in different RGB-D video modalities and provide more discriminative features for HAR.
Ranked #1 on
Action Recognition In Videos
on PKU-MMD
(using extra training data)
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results.
BIG-bench Machine Learning
Physics-informed machine learning
Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement
Malicious accounts spreading misinformation has led to widespread false and misleading narratives in recent times, especially during the COVID-19 pandemic, and social media platforms struggle to eliminate these contents rapidly.
SAITS: Self-Attention-based Imputation for Time Series
Missing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis.
On the Importance of Building High-quality Training Datasets for Neural Code Search
The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived.
Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer
User preference music transfer (UPMT) is a new problem in music style transfer that can be applied to many scenarios but remains understudied.
Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision
In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision.
Forecasting Loss of Signal in Optical Networks with Machine Learning
Furthermore, we show that it is possible to forecast LOS from all facility types and all networks with a single model, whereas fine-tuning for a particular facility or network only brings modest improvements.
BIG-bench Machine Learning
Classification on Time Series with Missing Data
Two-dimensional flow field measurement of sediment-laden flow based on ultrasound image velocimetry
This paper proposes a novel particle image velocimetry (PIV) technique to generate an instantaneous two-dimensional velocity field for sediment-laden fluid based on the optical flow algorithm of ultrasound imaging.
Optimization of Grant-Free NOMA with Multiple Configured-Grants for mURLLC
To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs.
Large-Scale Hyperspectral Image Clustering Using Contrastive Learning
Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool.
Unsupervised PET Reconstruction from a Bayesian Perspective
Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve imaging quality.
VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media
Specifically, when modeling the observed data from social media with neural temporal point process, we jointly learn a Gibbs-like distribution of group assignment based on how consistent an assignment is to (1) the account embedding space and (2) the prior knowledge.
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity
Specifically, the ability of using mask prior to help detect objects is learned from base categories and transferred to novel categories.
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change.
AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation
On the one hand, we model the rich correlations between the users' diverse behaviors (e. g., answer, follow, vote) to obtain the individual-level behavior interaction.
Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization
Long text understanding is important yet challenging in natural language processing.
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition
To tackle the above problems, we propose a new non-autoregressive transformer with a unified bidirectional decoder (NAT-UBD), which can simultaneously utilize left-to-right and right-to-left contexts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition
Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting.
MeDiaQA: A Question Answering Dataset on Medical Dialogues
In this paper, we introduce MeDiaQA, a novel question answering(QA) dataset, which constructed on real online Medical Dialogues.
StrucTexT: Structured Text Understanding with Multi-Modal Transformers
Due to the complexity of content and layout in VRDs, structured text understanding has been a challenging task.
Characterizing Online Engagement with Disinformation and Conspiracies in the 2020 U.S. Presidential Election
Identifying and characterizing disinformation in political discourse on social media is critical to ensure the integrity of elections and democratic processes around the world.
Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification
This paper proposes a multi-task learning network with phoneme-aware and channel-wise attentive learning strategies for text-dependent Speaker Verification (SV).
Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression
In this paper, we propose a two-task graph convolutional network (2T-GCN) to represent skeleton data for HAE tasks involving abnormality detection and quality evaluation.
Ranked #2 on
Action Assessment
on EHE
COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives
In this work, we investigate misinformation communities and narratives that can contribute to COVID-19 vaccine hesitancy.
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues.
Multimodal Fusion via Teacher-Student Network for Indoor Action Recognition
In our TSMF, we utilize a teacher network to transfer the structural knowledge of the skeleton modality to a student network for the RGB modality.
Ranked #2 on
Action Recognition In Videos
on PKU-MMD
(using extra training data)
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow.
An Examination of Fairness of AI Models for Deepfake Detection
Recent studies have demonstrated that deep learning models can discriminate based on protected classes like race and gender.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods.
The impact of data volume on performance of deep learning based building rooftop extraction using very high spatial resolution aerial images
Building rooftop data are of importance in several urban applications and in natural disaster management.
Interpretable Artificial Intelligence through the Lens of Feature Interaction
Interpretation of deep learning models is a very challenging problem because of their large number of parameters, complex connections between nodes, and unintelligible feature representations.
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT.
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications.
Modeling Treatment Effect Modification in Multidrug-Resistant Tuberculosis in an Individual Patient Data Meta-Analysis
We propose a marginal structural model (MSM) for effect modification by different patient characteristics and co-medications in a meta-analysis of observational IPD.
Methodology
An Examination of Preference-based Reinforcement Learning for Treatment Recommendation
Treatment recommendation is a complex multi-faceted problem with many conflicting objectives, e. g., optimizing the survival rate (or expected lifetime), mitigating negative impacts, reducing financial expenses and time costs, avoiding over-treatment, etc.
Time Series Counterfactual Inference with Hidden Confounders
We present augmented counterfactual ordinary differential equations (ACODEs), a new approach to counterfactual inference on time series data with a focus on healthcare applications.
Weakly Supervised Scene Graph Grounding
Recent researches have achieved substantial advances in learning structured representations from images.
Differentiable Approximations for Multi-resource Spatial Coverage Problems
Resource allocation for coverage of physical spaces is a challenging problem in robotic surveillance, mobile sensor networks and security domains.
Adversarial Multiscale Feature Learning for Overlapping Chromosome Segmentation
Since manual analysis is highly time and effort consuming, computer-assisted automatic chromosome karyotype analysis based on images is routinely used to improve the efficiency and accuracy of the analysis.
Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data
As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging.
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography.
RACH in Self-Powered NB-IoT Networks: Energy Availability and Performance Evaluation
In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission.
Multi-agent Trajectory Prediction with Fuzzy Query Attention
Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning.
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography
Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs).
CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels
Current mainstream of CT reconstruction methods based on deep learning usually needs to fix the scanning geometry and dose level, which will significantly aggravate the training cost and need more training data for clinical application.
Asymptotic Properties of the Maximum Likelihood Estimator in Regime-Switching Models with Time-Varying Transition Probabilities
We prove the asymptotic properties of the maximum likelihood estimator (MLE) in time-varying transition probability (TVTP) regime-switching models.
Analysis of Random Access in NB-IoT Networks with Three Coverage Enhancement Groups: A Stochastic Geometry Approach
NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks.
PolSIRD: Modeling Epidemic Spread under Intervention Policies
Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies.
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection
We evaluate the effectiveness of our propose method by comparing it with the baseline models and also with state-of-the-art works.
Network Inference from a Mixture of Diffusion Models for Fake News Mitigation
Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics.
Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks
Most existing crowd counting systems rely on the availability of the object location annotation which can be expensive to obtain.
Human Activity Recognition based on Dynamic Spatio-Temporal Relations
In the proposed method, an activity is divided into several successive actions represented by spatio temporal patterns, and the evolution of these actions are captured by a sequential model.
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations.
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable.
How does this interaction affect me? Interpretable attribution for feature interactions
Feature attribution is a way to analyze the impact of features on predictions.
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
Although the knowledge of governing partial differential equations (PDE) of data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems.
Req2Lib: A Semantic Neural Model for Software Library Recommendation
In this work, we would like to make recommendations based on requirement descriptions to avoid these problems.
Skeleton Focused Human Activity Recognition in RGB Video
The data-driven approach that learns an optimal representation of vision features like skeleton frames or RGB videos is currently a dominant paradigm for activity recognition.
Effective Human Activity Recognition Based on Small Datasets
To do so, we propose a HAR method that consists of three steps: (i) data transformation involving the generation of new features based on transforming of raw data, (ii) feature extraction involving the learning of a classifier based on the AdaBoost algorithm and the use of training data consisting of the transformed features, and (iii) parameter determination and pattern recognition involving the determination of parameters based on the features generated in (ii) and the use of the parameters as training data for deep learning algorithms to be used to recognize human activities.
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.
Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network
Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation.
COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations
The analysis is presented and updated on a publically accessible dashboard (https://usc-melady. github. io/COVID-19-Tweet-Analysis) to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020.
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models
Label noise is increasingly prevalent in datasets acquired from noisy channels.
Ranked #20 on
Image Classification
on Clothing1M
(using extra training data)
Towards Using Count-level Weak Supervision for Crowd Counting
Most existing crowd counting methods require object location-level annotation, i. e., placing a dot at the center of an object.
Analyzing Grant-Free Access for URLLC Service
Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances.
Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks
Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constantly adjusting the beam directions.
Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics
Sparsely available data points cause a numerical error on finite differences which hinder to modeling the dynamics of physical systems.
From Crowdsourcing to Crowdmining: Using Implicit Human Intelligence for Better Understanding of Crowdsourced Data
A generic model for CrowdMining is further proposed based on a set of existing studies.
Hybrid Low-order and Higher-order Graph Convolutional Networks
With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved.
Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.
A simple and effective postprocessing method for image classification
Whether it is computer vision, natural language processing or speech recognition, the essence of these applications is to obtain powerful feature representations that make downstream applications completion more efficient.
Extracting Interpretable Concept-Based Decision Trees from CNNs
In an attempt to gather a deeper understanding of how convolutional neural networks (CNNs) reason about human-understandable concepts, we present a method to infer labeled concept data from hidden layer activations and interpret the concepts through a shallow decision tree.
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.
GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection.
D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.
Differentiable Physics-informed Graph Networks
While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks.
Combating Fake News: A Survey on Identification and Mitigation Techniques
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion.
Instance-Based Classification through Hypothesis Testing
After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value.
Can I trust you more? Model-Agnostic Hierarchical Explanations
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models.
Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability
Neural networks are known to model statistical interactions, but they entangle the interactions at intermediate hidden layers for shared representation learning.
Dial2Desc: End-to-end Dialogue Description Generation
We first propose a new task named Dialogue Description (Dial2Desc).
Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches
In this paper, we first design an efficient division method to decompose a manifold into ordered continuous maximal linear patches (CMLPs) that denote meaningful action snippets of the action sequence.
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Multi-Rate Multivariate Time Series (MR-MTS) are the multivariate time series observations which come with various sampling rates and encode multiple temporal dependencies.
DynGEM: Deep Embedding Method for Dynamic Graphs
The major advantages of DynGEM include: (1) the embedding is stable over time, (2) it can handle growing dynamic graphs, and (3) it has better running time than using static embedding methods on each snapshot of a dynamic graph.
Social and Information Networks
A Modified Sigma-Pi-Sigma Neural Network with Adaptive Choice of Multinomials
We propose in this paper a modified Sigma-Pi-Sigma neural network (MSPSNN) with an adaptive approach to find a better multinomial for a given problem.
Robustness of classification ability of spiking neural networks
The numerical results show that there is not significant reduction in the classification ability of the network if the input signals are subject to sinusoidal and Gaussian perturbations.
Automatically Inferring Data Quality for Spatiotemporal Forecasting
Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on.
Relational Multi-Instance Learning for Concept Annotation from Medical Time Series
Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data.
Tensor Regression Meets Gaussian Processes
Low-rank tensor regression, a new model class that learns high-order correlation from data, has recently received considerable attention.
Deep Generative Dual Memory Network for Continual Learning
This phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data.
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications.
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
We use this generative model together with a convolutional neural network (CNN) based prediction model to improve the onset prediction performance.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
Ranked #3 on
Traffic Prediction
on PeMS-M
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
+2
Detecting Statistical Interactions from Neural Network Weights
Interpreting neural networks is a crucial and challenging task in machine learning.
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.
CSI: A Hybrid Deep Model for Fake News Detection
Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.
Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding
To account for high dimensionality, we use the embedding medical features in the CNN model which hold the natural medical concepts.
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling
In this paper, we show ways of sampling intermediate steps of alternating minimization algorithms for computing low rank tensor CP decompositions, leading to the sparse alternating least squares (SPALS) method.
Universal dependencies for Uyghur
The Universal Dependencies (UD) Project seeks to build a cross-lingual studies of treebanks, linguistic structures and parsing.
Learning Influence Functions from Incomplete Observations
We establish both proper and improper PAC learnability of influence functions under randomly missing observations.
On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps
A well-known construction of such functions comes from Bochner's characterization, which connects a positive-definite function with a probability distribution.
On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization
Different from traditional unfolding based tensor norm, this low-rank tensor constraint has optimality properties similar to that of matrix rank derived from SVD, so the complementary information among views can be explored more efficiently and thoroughly.
Learning from Multiway Data: Simple and Efficient Tensor Regression
In this paper, we introduce subsampled tensor projected gradient to solve the problem.
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
Ranked #4 on
Multivariate Time Series Forecasting
on MuJoCo
Multivariate Time Series Forecasting
Multivariate Time Series Imputation
+2
The DARPA Twitter Bot Challenge
A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes.
A Survey on Social Media Anomaly Detection
In this paper, we present a survey on existing approaches to address this problem.
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research.
Weighted Schatten $p$-Norm Minimization for Image Denoising and Background Subtraction
However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications.
Spectral Sparsification of Random-Walk Matrix Polynomials
Our work is particularly motivated by the algorithmic problems for speeding up the classic Newton's method in applications such as computing the inverse square-root of the precision matrix of a Gaussian random field, as well as computing the $q$th-root transition (for $q\geq1$) in a time-reversible Markov model.
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications.
Model Selection for Topic Models via Spectral Decomposition
Topic models have achieved significant successes in analyzing large-scale text corpus.
Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models
random samples for $n$-dimensional Gaussian random fields with SDDM precision matrices.
GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract
Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications.
Multiple Instance Learning on Structured Data
Ignoring this structure information limits the performance of existing MIL algorithms.
