Search Results for author:
Found 201 papers, 88 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.
Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations
In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African American and Hispanic individuals.
Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks
In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors.
CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose
Photometric differences are widely used as supervision signals to train neural networks for estimating depth and camera pose from unlabeled monocular videos.
Denoising Self-attentive Sequential Recommendation
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies.
MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware Beamforming Network for Speech Separation
Recently, many deep learning based beamformers have been proposed for multi-channel speech separation.
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction
Graph neural networks have achieved significant success in representation learning.
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling
Intuitively, FR algorithms can benefit from both the estimation of uncertainty and the detection of out-of-distribution (OOD) samples.
SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation
We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation.
Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields
Neural Radiance Fields (NeRF) have achieved photorealistic novel views synthesis; however, the requirement of accurate camera poses limits its application.
Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues
Because of the uniqueness and importance of combating health misinformation in social media, we conduct this survey to further facilitate interdisciplinary research on this problem.
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Materials and Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000-2020.
Salience Allocation as Guidance for Abstractive Summarization
Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.
Ranked #6 on
Abstractive Text Summarization
on CNN / Daily Mail
Regularized Data Programming with Bayesian Priors
The cost of manual data labeling can be a significant obstacle in supervised learning.
Application of Deep Learning on Single-Cell RNA-sequencing Data Analysis: A Review
Single-cell RNA-sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously.
Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline
To better bridge the domain gap between source domain (synthetic data) and target domain (real-world data), we also propose a Selective Feature Alignment (SFA) module which only aligns the features of consistent foreground area between the two domains, thus realizing inter-domain intra-modality adaptation.
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface
The proposed SSML learns a meta model with the existing subjects first, then fine-tunes the model in a semi-supervised learning manner, i. e. using few labeled and many unlabeled samples of target subject for calibration.
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
These results suggest that we can design efficient and effective models as long as they solve the indistinguishability of samples, without being limited to STGNNs.
Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization
We develop two simple yet effective plug and play methods to improve the performance of existing local and non-local propagation-based VSR algorithms on widely-used public videos.
ScaleNet: Searching for the Model to Scale
Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales.
LightViT: Towards Light-Weight Convolution-Free Vision Transformers
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias.
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors
We observe that the core difficulty for heterogeneous KD (hetero-KD) is the significant semantic gap between the backbone features of heterogeneous detectors due to the different optimization manners.
Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets
The second issue refers to that models may output misleading predictions due to epistemic uncertainty and aleatoric uncertainty, thus existing methods that rely solely on the output probabilities may fail to distinguish confident samples.
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations.
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals.
Ranked #3 on
Traffic Prediction
on METR-LA
Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data
In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses as guided by the diagnosis hierarchy of the International Classification of Diseases.
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
However, the patterns of time series and the dependencies between them (i. e., the temporal and spatial patterns) need to be analyzed based on long-term historical MTS data.
Ranked #2 on
Traffic Prediction
on PEMS-BAY
Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022)
They questioned the generalizability of our methods and the statistical significance, stability, and overfitting of the results, and proposed a pipeline for disease subtyping.
Masked Distillation with Receptive Tokens
To obtain a group of masks, the receptive tokens are learned via the regular task loss but with teacher fixed, and we also leverage a Dice loss to enrich the diversity of learned masks.
Green Hierarchical Vision Transformer for Masked Image Modeling
We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones.
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing
Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations.
Knowledge Distillation from A Stronger Teacher
In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly.
Ranked #2 on
Knowledge Distillation
on CIFAR-100
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning
This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective.
Ranked #2 on
Data-to-Text Generation
on ToTTo
A Keypoint-based Global Association Network for Lane Detection
Earlier works follow a top-down roadmap to regress predefined anchors into various shapes of lane lines, which lacks enough flexibility to fit complex shapes of lanes due to the fixed anchor shapes.
Ranked #3 on
Lane Detection
on TuSimple
(F1 score metric)
Multimodal Machine Learning in Precision Health
Attempts to improve prediction and resemble the multimodal nature of clinical expert decision-making this has been met in the computational field of machine learning by a fusion of disparate data.
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to align aspects and corresponding sentiments for aspect-specific sentiment polarity inference.
TR-MOT: Multi-Object Tracking by Reference
RS takes previous detected results as references to aggregate the corresponding features from the combined features of the adjacent frames and makes a one-to-one track state prediction for each reference in parallel.
Learning Where to Learn in Cross-View Self-Supervised Learning
In this paper, we present a new approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial information of features, so that the projected embeddings could be exactly aligned and thus guide the feature learning better.
UV Volumes for Real-time Rendering of Editable Free-view Human Performance
Neural volume rendering enables photo-realistic renderings of a human performer in free-view, a critical task in immersive VR/AR applications.
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.
DyRep: Bootstrapping Training with Dynamic Re-parameterization
Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks.
Relational Self-Supervised Learning
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations.
Ranked #37 on
Self-Supervised Image Classification
on ImageNet
Contrastive Learning
Self-Supervised Image Classification
+1
SimMatch: Semi-supervised Learning with Similarity Matching
Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community.
Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval
In contrast to previous works, we focus on the reproducibility of the approaches and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text.
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising
Furthermore, an overlapped inference mechanism is introduced to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models.
Relational Surrogate Loss Learning
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e. g., average precision and F1 score.
Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets
In this work, we proposed a DDI detection method based on molecular structures using graph convolutional networks and deep sets.
A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis
To evaluate our proposed method's utility and privacy loss, we apply our model on a medical report disease label classification task using two noisy challenging clinical text datasets.
Manifoldron: Direct Space Partition via Manifold Discovery
A neural network with the widely-used ReLU activation has been shown to partition the sample space into many convex polytopes for prediction.
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection
Today's cyber-world is vastly multivariate.
Time Series Anomaly Detection
Unsupervised Anomaly Detection
Face2Exp: Combating Data Biases for Facial Expression Recognition
To combat the mismatch between FR and FER data, Meta-Face2Exp uses a circuit feedback mechanism, which improves the base network with the feedback from the adaptation network.
Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring
Instead of estimating alignment information, we propose a simple and effective deep Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) to effectively propagate and gather the information from unaligned neighboring frames for better video deblurring.
Ranked #8 on
Deblurring
on GoPro
You Can Wash Better: Daily Handwashing Assessment with Smartwatches
We propose UWash, an intelligent solution upon smartwatches, to assess handwashing for the purpose of raising users' awareness and cultivating habits in high-quality handwashing.
Incentive Compatible Pareto Alignment for Multi-Source Large Graphs
Challenges of the problem include 1) how to align large-scale entities between sources to share information and 2) how to mitigate negative transfer from joint learning multi-source data.
GreedyNASv2: Greedier Search with a Greedy Path Filter
In this paper, we leverage an explicit path filter to capture the characteristics of paths and directly filter those weak ones, so that the search can be thus implemented on the shrunk space more greedily and efficiently.
Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure
As a result, clinicians cannot easily or rapidly scrutinize the CDSS recommendation when facing a difficult diagnosis or treatment decision in practice.
Persona Authentication through Generative Dialogue
In this paper we define and investigate the problem of \emph{persona authentication}: learning a conversational policy to verify the consistency of persona models.
On 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.
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.
Few-shot graph link prediction with domain adaptation
In this work, we propose an adversarial training based modification to the current state-of-the-arts link prediction method to solve this problem.
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.
Ranked #5 on
Table-based Fact Verification
on TabFact
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.
Neural Network Gaussian Processes by Increasing Depth
Inspired by a width-depth symmetry consideration, we use a shortcut network to show that increasing the depth of a neural network can also give rise to a Gaussian process, which is a valuable addition to the existing theory and contributes to revealing the true picture of deep learning.
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).
Collaboration Equilibrium in Federated Learning
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy.
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 #54 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.
ViTAS: Vision Transformer Architecture Search
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks.
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 #5 on
Referring Expression Segmentation
on J-HMDB
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.
Reconstructing 3D Model from Single-View Sketch with Deep Neural Network
It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples.
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: Towards a Deeper Appreciation on Face Detection
As a result, practical solutions on label assignment, scale-level data augmentation, and reducing false alarms are necessary for advancing face detectors.
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 #15 on
Human-Object Interaction Detection
on HICO-DET
(using extra 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
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.
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.
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.
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.
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.
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
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.
Stretchable Cells Help DARTS Search Better
However, even for this consistent search, the searched cells often suffer from poor performance, especially for the supernet with fewer layers, as current DARTS methods are prone to wide and shallow cells, and this topology collapse induces sub-optimal searched cells.
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 #2 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.
Deep Learning Based Single Sample Per Person Face Recognition: A Survey
Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models.
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.
Ranked #7 on
Image Dehazing
on Haze4k
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.
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.
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 #68 on
Neural Architecture Search
on ImageNet
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 #21 on
Human-Object Interaction Detection
on V-COCO
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.
Graph Neural Networks for Reasoning 2-Quantified Boolean Formulas
In this paper, we target the QBF (Quantified Boolean Formula) satisfiability problem, the complexity of which is in-between propositional logic and predicate logic, and investigate the feasibility of learning GNN-based solvers and GNN-based heuristics for the cases with a universal-existential quantifier alternation (so-called 2QBF problems).
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.
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.
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.
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.
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.
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.
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 #7 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.
