Search Results for author:
Found 139 papers, 53 papers with code
Local Correlation Consistency for Knowledge Distillation
Sufficient knowledge extraction from the teacher network plays a critical role in the knowledge distillation task to improve the performance of the student network.
Expressivity and Trainability of Quadratic Networks
Despite promising results so far achieved by networks of quadratic neurons, there are important issues not well addressed.
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records
We propose a general supervised contrastive loss $\mathcal{L}_{\text{Contrastive Cross Entropy} } + \lambda \mathcal{L}_{\text{Supervised Contrastive Regularizer}}$ for learning both binary classification (e. g. in-hospital mortality prediction) and multi-label classification (e. g. phenotyping) in a unified framework.
Weakly Supervised Contrastive Learning
Specifically, our proposed framework is based on two projection heads, one of which will perform the regular instance discrimination task.
Ranked #41 on
Self-Supervised Image Classification
on ImageNet
Using Subobservers to Synthesize Opacity-Enforcing Supervisors
In discrete-event system control, the worst-case time complexity for computing a system's observer is exponential in the number of that system's states.
Correcting the User Feedback-Loop Bias for Recommendation Systems
We empirically validated the existence of such user feedback-loop bias in real world recommendation systems and compared the performance of our method with the baseline models that are either without de-biasing or with propensity scores estimated by other methods.
Table-based Fact Verification with Salience-aware Learning
From one perspective, our system conducts masked salient token prediction to enhance the model for alignment and reasoning between the table and the statement.
Heterogeneous Graph Neural Network with Multi-view Representation Learning
Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph.
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
In this paper, we propose an FL framework to jointly consider performance consistency and algorithmic fairness across different local clients (data sources).
Learning to Collaborate
We propose a learning to collaborate framework, where each client can choose to collaborate with certain members in the network to achieve a "collaboration equilibrium", where smaller collaboration coalitions are formed within the network so that each client can obtain the model with the best utility.
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection
Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data.
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently.
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations.
Ranked #41 on
Self-Supervised Image Classification
on ImageNet
Contrastive Learning
Self-Supervised Image Classification
+1
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
Vision Transformer Architecture Search
In this paper, we make a further step by examining the intrinsic structure of transformers for vision tasks and propose an architecture search method, dubbed ViTAS, to search for the optimal architecture with similar hardware budgets.
Learning To Restore Hazy Video: A New Real-World Dataset and a New Method
On the other hand, the video dehazing algorithms, which can acquire more satisfying dehazing results by exploiting the temporal redundancy from neighborhood hazy frames, receive less attention due to the absence of the video dehazing datasets.
S-LIME: Stabilized-LIME for Model Explanation
An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare.
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
The operation weight for each path is represented as a convex combination of items in a dictionary with a simplex code.
BCNet: Searching for Network Width with Bilaterally Coupled Network
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation
Though 3D convolutions are amenable to recognizing which actor is performing the queried actions, it also inevitably introduces misaligned spatial information from adjacent frames, which confuses features of the target frame and yields inaccurate segmentation.
Ranked #3 on
Referring Expression Segmentation
on A2D Sentences
Retrieving Complex Tables with Multi-Granular Graph Representation Learning
The task of natural language table retrieval (NLTR) seeks to retrieve semantically relevant tables based on natural language queries.
Research on Optimization Method of Multi-scale Fish Target Fast Detection Network
The fish target detection algorithm lacks a good quality data set, and the algorithm achieves real-time detection with lower power consumption on embedded devices, and it is difficult to balance the calculation speed and identification ability.
Prioritized Architecture Sampling with Monto-Carlo Tree Search
One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once.
MogFace: Rethinking Scale Augmentation on the Face Detector
In order to help the detector utilize the scale information efficiently, we analyze the relationship between the detector performance and the scale distribution of the training data.
Mapping the phase diagram of the quantum anomalous Hall and topological Hall effects in a dual-gated magnetic topological insulator heterostructure
We use magnetotransport in dual-gated magnetic topological insulator heterostructures to map out a phase diagram of the topological Hall and quantum anomalous Hall effects as a function of the chemical potential (primarily determined by the back gate voltage) and the asymmetric potential (primarily determined by the top gate voltage).
Mesoscale and Nanoscale Physics
Reformulating HOI Detection as Adaptive Set Prediction
To attain this, we map a trainable interaction query set to an interaction prediction set with a transformer.
Ranked #5 on
Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Locally Free Weight Sharing for Network Width Search
In this paper, to better evaluate each width, we propose a locally free weight sharing strategy (CafeNet) accordingly.
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Our method enables differentiable sparsification, and keeps the derived architecture equivalent to that of Engine-cell, which further improves the consistency between search and evaluation.
Temporal boundary solitons and extreme super-thermal light statistics
We discover the formation of a temporal boundary soliton (TBS) in the close proximity of a temporal boundary, moving in a nonlinear optical medium, upon high-intensity pulse collision with the boundary.
Optics Pattern Formation and Solitons
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
Adversarial Example Detection Using Latent Neighborhood Graph
We present the first graph-based adversarial detection method that constructs a Latent Neighborhood Graph (LNG) around an input example to determine if the input example is adversarial.
Learning With Privileged Tasks
Concretely, we introduce a privileged parameter so that the optimization direction does not necessarily follow the gradient from the privileged tasks, but concentrates more on the target tasks.
Explicit Learning Topology for Differentiable Neural Architecture Search
Differentiable neural architecture search (NAS) has gained much success in discovering more flexible and diverse cell types.
EnTranNAS: Towards Closing the Gap between the Architectures in Search and Evaluation
The Engine-cell is differentiable for architecture search, while the Transit-cell only transits the current sub-graph by architecture derivation.
SUSY Breaking Constraints on Modular flavor $S_3$ Invariant $SU(5)$ GUT Model
Modular flavor symmetry can be used to explain the quark and lepton flavor structures.
High Energy Physics - Phenomenology
Biomedical Knowledge Graph Refinement with Embedding and Logic Rules
This paper proposes a method BioGRER to improve the BioKG's quality, which comprehensively combines the knowledge graph embedding and logic rules that support and negate triplets in the BioKG.
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
In this paper, we examine the diversity of teacher models in the gradient space and regard the ensemble knowledge distillation as a multi-objective optimization problem so that we can determine a better optimization direction for the training of student network.
3D Registration for Self-Occluded Objects in Context
While much progress has been made on the task of 3D point cloud registration, there still exists no learning-based method able to estimate the 6D pose of an object observed by a 2. 5D sensor in a scene.
Explicitly Learning Topology for Differentiable Neural Architecture Search
Differentiable neural architecture search (DARTS) has gained much success in discovering more flexible and diverse cell types.
A Method to Generate High Precision Mesh Model and RGB-D Datasetfor 6D Pose Estimation Task
Recently, 3D version has been improved greatly due to the development of deep neural networks.
CODER: Knowledge infused cross-lingual medical term embedding for term normalization
This paper proposes CODER: contrastive learning on knowledge graphs for cross-lingual medical term representation.
Data Agnostic Filter Gating for Efficient Deep Networks
To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e. g., FLOPs).
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
By doing so, our network for search at each update satisfies the sparsity constraint and is efficient to train.
Robust Finite Mixture Regression for Heterogeneous Targets
FMR is an effective scheme for handling sample heterogeneity, where a single regression model is not enough for capturing the complexities of the conditional distribution of the observed samples given the features.
Domain Agnostic Learning for Unbiased Authentication
We further extend our method to a meta-learning framework to pursue more thorough domain-difference elimination.
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review.
Suicide Risk Modeling with Uncertain Diagnostic Records
Motivated by the pressing need for suicide prevention through improving behavioral healthcare, we use medical claims data to study the risk of subsequent suicide attempts for patients who were hospitalized due to suicide attempts and later discharged.
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy.
A(DP)$^2$SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy
A popular distributed learning strategy is federated learning, where there is a central server storing the global model and a set of local computing nodes updating the model parameters with their corresponding data.
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning
In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions.
Visualizing Deep Graph Generative Models for Drug Discovery
Drug discovery aims at designing novel molecules with specific desired properties for clinical trials.
CorefQA: Coreference Resolution as Query-based Span Prediction
In this paper, we present CorefQA, an accurate and extensible approach for the coreference resolution task.
Ranked #1 on
Coreference Resolution
on CoNLL 2012
(using extra training data)
MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process.
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
Bipartite ranking, which aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data, is widely adopted in various applications where sample prioritization is needed.
Why Attentions May Not Be Interpretable?
In this paper, we demonstrate that one root cause of this phenomenon is the combinatorial shortcuts, which means that, in addition to the highlighted parts, the attention weights themselves may carry extra information that could be utilized by downstream models after attention layers.
Adversarial Infidelity Learning for Model Interpretation
A popular way of performing model interpretation is Instance-wise Feature Selection (IFS), which provides an importance score of each feature representing the data samples to explain how the model generates the specific output.
Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration
While 3D-3D registration is traditionally tacked by optimization-based methods, recent work has shown that learning-based techniques could achieve faster and more robust results.
General-Purpose User Embeddings based on Mobile App Usage
In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage.
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme.
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems
In this paper, we introduce an eigendecomposition-free approach to training a deep network whose loss depends on the eigenvector corresponding to a zero eigenvalue of a matrix predicted by the network.
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet
The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones.
Ranked #40 on
Neural Architecture Search
on ImageNet
Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling
The advantage of this method is to make the model converge to various local optima by scheduling the learning rate in once training.
CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection
CentripetalNet predicts the position and the centripetal shift of the corner points and matches corners whose shifted results are aligned.
Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States
Primary outcome measures: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).
Gated Fusion Network for Degraded Image Super Resolution
To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately.
A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation
Human gaze is essential for various appealing applications.
PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection
Human and object points are the center of the detection boxes, and the interaction point is the midpoint of the human and object points.
Ranked #16 on
Human-Object Interaction Detection
on HICO-DET
Explicit-Blurred Memory Network for Analyzing Patient Electronic Health Records
While networks with explicit memory have been proposed recently, the discontinuities imposed by the discrete operations make such networks harder to train and require more supervision.
Federated Learning for Healthcare Informatics
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.
Biomedical Evidence Generation Engine
With the rapid development of precision medicine, a large amount of health data (such as electronic health records, gene sequencing, medical images, etc.)
Coreference Resolution as Query-based Span Prediction
In this paper, we present an accurate and extensible approach for the coreference resolution task.
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment
GRAPHENE consists of three main different modules 1) graph-augmented document representation learning; 2) query expansion and representation learning and 3) learning to rank biomedical articles.
A Real-Time Cross-modality Correlation Filtering Method for Referring Expression Comprehension
RCCF reformulates the referring expression comprehension as a correlation filtering process.
Bigger versus Similar: Selecting a Background Corpus for First Story Detection Based on Distributional Similarity
Using these metrics we rank different background corpora relative to a target corpus.
Learning Reinforced Attentional Representation for End-to-End Visual Tracking
Although numerous recent tracking approaches have made tremendous advances in the last decade, achieving high-performance visual tracking remains a challenge.
Research on Autonomous Maneuvering Decision of UCAV based on Approximate Dynamic Programming
To solve the problem of dimensional explosion in the air combat, the proposed method is implemented through feature selection, trajectory sampling, function approximation and Bellman backup operation in the air combat simulation environment.
Neural Cognitive Diagnosis for Intelligent Education Systems
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.
Neural Dynamics on Complex Networks
To address these challenges, we propose to combine Ordinary Differential Equation Systems (ODEs) and Graph Neural Networks (GNNs) to learn continuous-time dynamics on complex networks in a data-driven manner.
SFSegNet: Parse Freehand Sketches using Deep Fully Convolutional Networks
Parsing sketches via semantic segmentation is attractive but challenging, because (i) free-hand drawings are abstract with large variances in depicting objects due to different drawing styles and skills; (ii) distorting lines drawn on the touchpad make sketches more difficult to be recognized; (iii) the high-performance image segmentation via deep learning technologies needs enormous annotated sketch datasets during the training stage.
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
To establish a simultaneous post-model selection inference, we propose a method of contraction and expansion (MOCE) along the line of debiasing estimation that enables us to balance the bias-and-variance trade-off so that the super-sparsity assumption may be relaxed.
Joint Activity Recognition and Indoor Localization With WiFi Fingerprints
In this paper, we propose a novel deep learning framework for joint activity recognition and indoor localization task using WiFi Channel State Information~(CSI) fingerprints.
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation
This paper proposes a novel cascaded U-Net for brain tumor segmentation.
Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning
In order to keep track of the operational state of power grid, the world's largest sensor systems, smart grid, was built by deploying hundreds of millions of smart meters.
Joint Multi-frame Detection and Segmentation for Multi-cell Tracking
Another UNet is utilized to acquire primary segmentation.
FPGA-based Binocular Image Feature Extraction and Matching System
Image feature extraction and matching is a fundamental but computation intensive task in machine vision.
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records
In this paper, we propose MetaPred, a meta-learning for clinical risk prediction from longitudinal patient EHRs.
Learning Cascaded Siamese Networks for High Performance Visual Tracking
In order to achieve high performance visual tracking in various negative scenarios, a novel cascaded Siamese network is proposed and developed based on two different deep learning networks: a matching subnetwork and a classification subnetwork.
Graph Neural Reasoning for 2-Quantified Boolean Formula Solvers
In this paper, we investigate the feasibility of learning GNN (Graph Neural Network) based solvers and GNN-based heuristics for specified QBF (Quantified Boolean Formula) problems.
Siamese Attentional Keypoint Network for High Performance Visual Tracking
In this paper, we investigate the impacts of three main aspects of visual tracking, i. e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization.
Temporal Unet: Sample Level Human Action Recognition using WiFi
In this task, every WiFi distortion sample in the whole series should be categorized into one action, which is a critical technique in precise action localization, continuous action segmentation, and real-time action recognition.
Deep Comprehensive Correlation Mining for Image Clustering
Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data.
Ranked #5 on
Image Clustering
on Tiny-ImageNet
Joint Activity Recognition and Indoor Localization
The key rationale behind this type of work is that people behaviors can influence the WiFi signal propagation and introduce specific patterns into WiFi signals, called WiFi fingerprints, which further can be explored to identify human activities and locations.
Human-Computer Interaction
Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Sub-phenotype III is with average age 65. 07$ \pm 11. 32 $ years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr $1. 69\pm 0. 32$ mg/dL, eGFR $93. 97\pm 56. 53$ mL/min/1. 73$m^2$).
Feature Learning Viewpoint of AdaBoost and a New Algorithm
We explain the rationality of this and illustrate the theorem that when the dimension of these features increases, the performance of SVM would not be worse, which can explain the resistant to overfitting of AdaBoost.
Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study
While the NLP system showed high precision and recall measurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites.
Can WiFi Estimate Person Pose?
In this paper We try to answer this question by exploring the ability of WiFi on estimating single person pose.
Person-in-WiFi: Fine-grained Person Perception using WiFi
Fine-grained person perception such as body segmentation and pose estimation has been achieved with many 2D and 3D sensors such as RGB/depth cameras, radars (e. g., RF-Pose) and LiDARs.
SE2Net: Siamese Edge-Enhancement Network for Salient Object Detection
In this paper, we propose a simple yet effective Siamese Edge-Enhancement Network (SE2Net) to preserve the edge structure for salient object detection.
Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding
One major carrier for conducting patient similarity research is Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, and sparse.
Glyce: Glyph-vectors for Chinese Character Representations
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Ranked #1 on
Chinese Word Segmentation
on PKU
Chinese Dependency Parsing
Chinese Named Entity Recognition
+19
A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Care
Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.
Multi-column Point-CNN for Sketch Segmentation
Traditional sketch segmentation methods mainly rely on handcrafted features and complicate models, and their performance is far from satisfactory due to the abstract representation of sketches.
A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization
State-of-the-art studies have demonstrated the superiority of joint modelling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the two processes.
Continuous User Authentication by Contactless Wireless Sensing
This paper presents BodyPIN, which is a continuous user authentication system by contactless wireless sensing using commodity Wi-Fi.
Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming
Training of deep learning models depends on gradient descent and end-to-end differentiation.
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.
Boosted Sparse and Low-Rank Tensor Regression
We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.
A Self-Organizing Tensor Architecture for Multi-View Clustering
In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.
AutoGraph: Imperative-style Coding with Graph-based Performance
In machine learning, imperative style libraries like Autograd and PyTorch are easy to write, but suffer from high interpretive overhead and are not easily deployable in production or mobile settings.
Efficient Multi-level Correlating for Visual Tracking
Correlation filter (CF) based tracking algorithms have demonstrated favorable performance recently.
CSI-Net: Unified Human Body Characterization and Pose Recognition
We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals.
WiPIN: Operation-free Passive Person Identification Using Wi-Fi Signals
Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness.
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
Model-Protected Multi-Task Learning
Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together.
Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network
For complicated diseases such as Parkinson's and Alzheimer's, both EHR and neuroimaging information are very important for disease understanding because they contain complementary aspects of the disease.
The Devil of Face Recognition is in the Noise
2) With the original datasets and cleaned subsets, we profile and analyze label noise properties of MegaFace and MS-Celeb-1M.
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution.
A Multi-View Ensemble Classification Model for Clinically Actionable Genetic Mutations
This paper presents details of our winning solutions to the task IV of NIPS 2017 Competition Track entitled Classifying Clinically Actionable Genetic Mutations.
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans.
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features.
High Performance Visual Tracking with Circular and Structural Operators
Based on the proposed circular and structural operators, a set of primal confidence score maps can be obtained by circular correlating feature maps with their corresponding structural correlation filters.
A Complementary Tracking Model with Multiple Features
Discriminative Correlation Filters based tracking algorithms exploiting conventional handcrafted features have achieved impressive results both in terms of accuracy and robustness.
Large Margin Structured Convolution Operator for Thermal Infrared Object Tracking
To the best of our knowledge, we are the first to incorporate the advantages of DCF and SOSVM for TIR object tracking.
Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator
Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay.
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by finding the eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix representing a linear system.
Differentially Private Generative Adversarial Network
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models.
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero
Despite the recent successes of deep neural networks in various fields such as image and speech recognition, natural language processing, and reinforcement learning, we still face big challenges in bringing the power of numeric optimization to symbolic reasoning.
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
The surging availability of electronic medical records (EHR) leads to increased research interests in medical predictive modeling.
Structural Deep Embedding for Hyper-Networks
These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge.
Social and Information Networks
Heuristic Search for Structural Constraints in Data Association
In this paper, we propose a new heuristic method to search for structural constraints (HSSC) of multiple targets when solving the problem of online multi-object tracking.
Accelerate RNN-based Training with Importance Sampling
Unfortunately, the calculation of the sampling probability distribution $P$ causes a major limitation of IS: it requires the input data to be well-structured, i. e., the feature vector is properly defined.
Patient Subtyping via Time-Aware LSTM Networks
We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.
Ranked #2 on
Multivariate Time Series Forecasting
on MIMIC-III
Residual Attention Network for Image Classification
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion.
Ranked #256 on
Image Classification
on ImageNet
Google Map Aided Visual Navigation for UAVs in GPS-denied Environment
We propose a framework for Google Map aided UAV navigation in GPS-denied environment.
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes.
GaDei: On Scale-up Training As A Service For Deep Learning
By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks.
From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics
As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e. g. an threshold for outbreak).
Social and Information Networks Physics and Society
