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Found 460 papers, 178 papers with code
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a natural way to model transitive relational data (e. g. entailment graphs).
Adversarial Metric Learning
In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs.
Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders
Unsupervised Image-to-Image Translation achieves spectacularly advanced developments nowadays.
Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes
Image analysis using more than one modality (i. e. multi-modal) has been increasingly applied in the field of biomedical imaging.
Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net
Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice.
Improved Representation Learning for Predicting Commonsense Ontologies
Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints.
Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories
Precision medicine requires the precision disease risk prediction models.
Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis
In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation.
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.
Learning the Sparse and Low Rank PARAFAC Decomposition via the Elastic Net
In this article, we derive a Bayesian model to learning the sparse and low rank PARAFAC decomposition for the observed tensor with missing values via the elastic net, with property to find the true rank and sparse factor matrix which is robust to the noise.
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
Based on the 2-Component shared embedding, we design a new RNN algorithm and evaluate it using the language modeling task on several benchmark datasets.
Statistical Properties of the Single Linkage Hierarchical Clustering Estimator
Distance-based hierarchical clustering (HC) methods are widely used in unsupervised data analysis but few authors take account of uncertainty in the distance data.
Top-push Video-based Person Re-identification
Since only limited information can be exploited from still images, it is hard (if not impossible) to overcome the occlusion, pose and camera-view change, and lighting variation problems.
An Enhanced Deep Feature Representation for Person Re-identification
In this paper, we focus on the feature representation and claim that hand-crafted histogram features can be complementary to Convolutional Neural Network (CNN) features.
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate.
Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering
We derive a statistical model for estimation of a dendrogram from single linkage hierarchical clustering (SLHC) that takes account of uncertainty through noise or corruption in the measurements of separation of data.
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning
We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL?
Adversarial Open-World Person Re-Identification
The main idea is learning to attack feature extractor on the target people by using GAN to generate very target-like images (imposters), and in the meantime the model will make the feature extractor learn to tolerate the attack by discriminative learning so as to realize group-based verification.
Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
The framework combines two 1st-level modules: direct estimation module and a segmentation module.
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.
Triple Attention Mixed Link Network for Single Image Super Resolution
Single image super resolution is of great importance as a low-level computer vision task.
Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention.
Improving the Robustness of Speech Translation
Although neural machine translation (NMT) has achieved impressive progress recently, it is usually trained on the clean parallel data set and hence cannot work well when the input sentence is the production of the automatic speech recognition (ASR) system due to the enormous errors in the source.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images
In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD).
Computed Tomography (CT)
Finding Pulmonary Nodules In Large-Scale Ct Images
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
In this paper, we propose a novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks.
Ranked #76 on
Semantic Segmentation
on NYU Depth v2
Smoothing the Geometry of Probabilistic Box Embeddings
However, the hard edges of the boxes present difficulties for standard gradient based optimization; that work employed a special surrogate function for the disjoint case, but we find this method to be fragile.
Joint Intensity and Spatial Metric Learning for Robust Gait Recognition
This paper describes a joint intensity metric learning method to improve the robustness of gait recognition with silhouette-based descriptors such as gait energy images.
Multi-Scale Learning for Low-Resolution Person Re-Identification
In real world person re-identification (re-id), images of people captured at very different resolutions from different locations need be matched.
Partial Person Re-Identification
We address a new partial person re-identification (re-id) problem, where only a partial observation of a person is available for matching across different non-overlapping camera views.
Do Subsampled Newton Methods Work for High-Dimensional Data?
Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information.
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term.
Inpatient2Vec: Medical Representation Learning for Inpatients
In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning.
Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks
Visible watermark plays an important role in image copyright protection and the robustness of a visible watermark to an attack is shown to be essential.
Cross-view Relation Networks for Mammogram Mass Detection
Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage.
ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
Such pixel-level dilation rates produce optimal receptive fields so that the information of objects with different sizes can be extracted at the corresponding scale.
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Specifically, to handle two types of data instances involved in S$^3$VM, TSGS$^3$VM samples a labeled instance and an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient.
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization
To address this problem, in this paper, we propose a novel scalable quadruply stochastic gradient algorithm (QSG-S2AUC) for nonlinear semi-supervised AUC optimization.
Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks
In this study, we explore the fusion of the two kinds of features and claim that radiomics features can be complementary to deep-learning features.
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET).
Multi-label Detection and Classification of Red Blood Cells in Microscopic Images
As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples.
Improving One-shot NAS by Suppressing the Posterior Fading
In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning.
Communication-Efficient Local Decentralized SGD Methods
Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication.
Prediction stability as a criterion in active learning
Recent breakthroughs made by deep learning rely heavily on large number of annotated samples.
A General Early-Stopping Module for Crowdsourced Ranking
The number of microtasks depends on the budget allocated for the problem.
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms
In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms.
Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84. 5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules.
Safe Sample Screening for Robust Support Vector Machine
To address this challenge, in this paper, we propose two safe sample screening rules for RSVM based on the framework of concave-convex procedure (CCCP).
Time-constrained Adaptive Influence Maximization
The well-known influence maximization problem aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process.
Social and Information Networks
Communication-Efficient Distributed SVD via Local Power Iterations
As a practical surrogate of OPT, sign-fixing, which uses a diagonal matrix with $\pm 1$ entries as weights, has better computation complexity and stability in experiments.
TEDL: A Text Encryption Method Based on Deep Learning
Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters.
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations.
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network
To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures.
Height estimation from single aerial images using a deep ordinal regression network
Previous researches have extensively studied the problem of height estimation from aerial images based on stereo or multi-view image matching.
Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features
Existing gait recognition approaches typically focus on learning identity features that are invariant to covariates (e. g., the carrying status, clothing, walking speed, and viewing angle) and seldom involve learning features from the covariate aspect, which may lead to failure modes when variations due to the covariate overwhelm those due to the identity.
Modeling Discourse Structure for Document-level Neural Machine Translation
Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder.
CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data
We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities.
Geometry-Aware Segmentation of Remote Sensing Images via Implicit Height Estimation
Recent studies have shown the benefits of using additional elevation data (e. g., DSM) for enhancing the performance of the semantic segmentation of aerial images.
Unsupervised Learning of 3D Point Set Registration
Moreover, for a pair of source and target point sets, existing deep learning mechanisms require explicitly designed encoders to extract both deep spatial features from unstructured point clouds and their spatial correlation representation, which is further fed to a decoder to regress the desired geometric transformation for point set alignment.
Few-shot Object Detection on Remote Sensing Images
In this paper, we introduce a few-shot learning-based method for object detection on remote sensing images where only a few annotated samples are provided for the unseen object categories.
Xiaomi's Submissions for IWSLT 2020 Open Domain Translation Task
This paper describes the Xiaomi{'}s submissions to the IWSLT20 shared open domain translation task for Chinese{\textless}-{\textgreater}Japanese language pair.
DeepTracking-Net: 3D Tracking with Unsupervised Learning of Continuous Flow
In this paper, we aim at handling the problem of 3D tracking, which provides the tracking of the consecutive frames of 3D shapes.
3DMotion-Net: Learning Continuous Flow Function for 3D Motion Prediction
To approach this problem, we propose a self-supervised approach that leverages the power of the deep neural network to learn a continuous flow function of 3D point clouds that can predict temporally consistent future motions and naturally bring out the correspondences among consecutive point clouds at the same time.
Unsupervised Learning of Global Registration of Temporal Sequence of Point Clouds
Global registration of point clouds aims to find an optimal alignment of a sequence of 2D or 3D point sets.
Simulating multi-exit evacuation using deep reinforcement learning
We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping.
GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor
More specifically, for a given group we first define an optimizable Group Latent Descriptor (GLD) to characterize the gruopwise relationship among a group of point sets.
Robust Image Matching By Dynamic Feature Selection
Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching.
Object Detection in the Context of Mobile Augmented Reality
Ordinary object detection approaches process information from the images only, and they are oblivious to the camera pose with regard to the environment and the scale of the environment.
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data
In this paper, we focus on nonlinear learning with kernels, and propose a federated doubly stochastic kernel learning (FDSKL) algorithm for vertically partitioned data.
Gait Recognition from a Single Image using a Phase-Aware Gait Cycle Reconstruction Network
Specifically, a phase estimation network is introduced for the input single image, and the gait cycle reconstruction network exploits the estimated phase to mitigate the dependence of an encoded feature on the phase of that single image.
Unsupervised Partial Point Set Registration via Joint Shape Completion and Registration
To bridge the performance gaps between partial point set registration with full point set registration, we proposed to incorporate a shape completion network to benefit the registration process.
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it.
Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector
Point cloud registration is the process of aligning a pair of point sets via searching for a geometric transformation.
Box-To-Box Transformation for Modeling Joint Hierarchies
In Patel et al. (2020), the authors demonstrate that only the transitive reduction is required, and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes.
Reading Comprehension as Natural Language Inference: A Semantic Analysis
We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence
In this paper, we propose a novel meta-learning based 3D point signature model, named 3Dmetapointsignature (MEPS) network, that is capable of learning robust point signatures in 3D shapes.
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds
In this paper, we propose a meta-learning based 3D registration model, named 3D Meta-Registration, that is capable of rapidly adapting and well generalizing to new 3D registration tasks for unseen 3D point clouds.
Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs
Regularized MDPs serve as a smooth version of original MDPs.
Retrieving and ranking short medical questions with two stages neural matching model
Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services.
Convolutional Neural Network for Behavioral Modeling and Predistortion of Wideband Power Amplifiers
We design a pre-designed filter using the convolutional layer to extract the basis functions required for the PA forward or reverse modeling.
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
Physics Guided Machine Learning Methods for Hydrology
The goal of this work is to incorporate our understanding of physical processes and constraints in hydrology into machine learning algorithms, and thus bridge the performance gap while reducing the need for large amounts of data compared to traditional data-driven approaches.
Modeling Homophone Noise for Robust Neural Machine Translation
In this paper, we propose a robust neural machine translation (NMT) framework.
Cost-Effective Federated Learning Design
In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence.
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types.
Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos
Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds.
Time Series Analysis
Video Forensics
Cryptography and Security
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications
In this work, we study a novel class of projection-based algorithms for linearly constrained problems (LCPs) which have a lot of applications in statistics, optimization, and machine learning.
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
Collaborative filtering has been largely used to advance modern recommender systems to predict user preference.
vrCAPTCHA: Exploring CAPTCHA Designs in Virtual Reality
With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) designs for VR devices.
Human-Computer Interaction
FedPower: Privacy-Preserving Distributed Eigenspace Estimation
The low communication power and the possible privacy breaches of data make the computation of eigenspace challenging.
Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection
Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance.
Towards Multi-Scale Style Control for Expressive Speech Synthesis
This paper introduces a multi-scale speech style modeling method for end-to-end expressive speech synthesis.
Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood
In this paper, we propose a deep attributed network representation learning via attribute enhanced neighborhood (DANRL-ANE) model to improve the robustness and effectiveness of node representations.
Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning
However, such a decoder only exploits the preorder traversal based preceding actions, which are insufficient to ensure correct action predictions.
Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation
A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information.
Reading Comprehension as Natural Language Inference:A Semantic Analysis
We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding
We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task.
A More Compact Object Detector Head Network with Feature Enhancement and Relational Reasoning
Without losing generality, we can also build a more lighter head network for other multi-stage detectors by assembling our method.
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes
To achieve this, we treat the shape segmentation as a point labeling problem in the metric space.
RigNet: Repetitive Image Guided Network for Depth Completion
However, blurry guidance in the image and unclear structure in the depth still impede the performance of the image guided frameworks.
Ranked #2 on
Depth Completion
on KITTI Depth Completion
Statistical Estimation and Inference via Local SGD in Federated Learning
Both the methods are communication efficient and applicable to online data.
Fine-Grained Few Shot Learning with Foreground Object Transformation
As a data augmentation method, FOT can be conveniently applied to any existing few shot learning algorithm and greatly improve its performance on FG-FSL tasks.
Cost-Effective Federated Learning in Mobile Edge Networks
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data.
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Furthermore, we show that KGSSL is relatively more robust to distortion than baseline methods, and outperforms the baseline model by 35\% when plugging in KGSSL inferred characteristics.
HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection
This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection.
Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation
Knowledge distillation usually transfers the knowledge from a pre-trained cumbersome teacher network to a compact student network, which follows the classical teacher-teaching-student paradigm.
Feasible Architecture for Quantum Fully Convolutional Networks
Fully convolutional networks are robust in performing semantic segmentation, with many applications from signal processing to computer vision.
Video Instance Segmentation by Instance Flow Assembly
Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes.
Improved Loss Function-Based Prediction Method of Extreme Temperatures in Greenhouses
By increasing the weight of extreme temperature samples and reducing the possibility of misjudging extreme temperature as normal, the proposed loss function can enhance the prediction results in extreme situations.
融合情感分析的隐式反问句识别模型(Implicit Rhetorical Questions Recognition Model Combined with Sentiment Analysis)
“反问是现代汉语中一种常用的修辞手法, 根据是否含有反问标记可分为显式反问句与隐式反问句。其中隐式反问句表达的情感更为丰富, 表现形式也十分复杂, 对隐式反问句的识别更具挑战性。本文首先扩充了汉语反问句语料库, 语料库规模达到10000余句, 接着针对隐式反问句的特点, 提出了一种融合情感分析的隐式反问句识别模型。模型考虑了句子的语义信息, 上下文信息, 并借助情感分析任务辅助识别隐式反问句。实验结果表明, 本文提出的模型在隐式反问句识别任务上取得了良好的性能。”
Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network
We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78. 9%, 80. 7% and 80. 7% at 2 false positives per volume at 0mm, 2mm and 5mm localization error, which is superior to the state-of-the-art methods.
Reinforcement Learning Enhanced Explainer for Graph Neural Networks
To address these issues, we propose a RL-enhanced GNN explainer, RG-Explainer, which consists of three main components: starting point selection, iterative graph generation and stopping criteria learning.
Shallow geothermal energy potential for heating and cooling of buildings with regeneration under climate change scenarios
Shallow ground-source heat pumps (GSHPs) are a promising technology for contributing to the decarbonisation of the energy sector.
Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation
Based on this representation, we introduce a cropping-free temporal fusion approach to model the temporal consistency between video frames.
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation
Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability.
Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information
The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities.
Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers.
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models
Motivated by the observation that model-free methods like behavioral retargeting (BR) and item-based collaborative filtering (ItemCF) hit different parts of the user-item relevance compared to neural sequential recommendation models, we propose a novel model-agnostic training approach called WSLRec, which adopts a three-stage framework: pre-training, top-$k$ mining, and fine-tuning.
A density peaks clustering algorithm with sparse search and K-d tree
It is proved that the algorithm can effectively reduce the computational complexity of the original DPC from $O(n^2K)$ to $O(n(n^{1-1/K}+k))$.
Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion
To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery.
A Learning Convolutional Neural Network Approach for Network Robustness Prediction
Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.
EEG based Emotion Recognition: A Tutorial and Review
Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic.
THE DCASE 2021 CHALLENGE TASK 6 SYSTEM: AUTOMATED AUDIO CAPTIONING WITH WEAKLY SUPERVISED PRE-TRAING AND WORD SELECTION METHODS
Our solution focuses on solving two problems in automated audio captioning: data insufficiency and word selection indeterminacy.
Ranked #1 on
Audio captioning
on Clotho
(using extra training data)
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022
Our system is built based on the Transformer model with novel techniques borrowed from our recent research work.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System
Industrial search and recommendation systems mostly follow the classic multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and re-ranking stages.
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization
Adaptive algorithms like AdaGrad and AMSGrad are successful in nonconvex optimization owing to their parameter-agnostic ability -- requiring no a priori knowledge about problem-specific parameters nor tuning of learning rates.
Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution
With our proposed QCM, the downstream fusion module receives visual features that are more discriminative and focused on the desired object described in the expression, leading to more accurate predictions.
Dual Power Spectrum Manifold and Toeplitz HPD Manifold: Enhancement and Analysis for Matrix CFAR Detection
These advantages benefit from the geometry of the Toeplitz Hermitian positive definite (HPD) manifold $\mathcal{M}_{\mathcal{T}H_{++}}$, but the sophisticated geometry also results in some challenges for geometric detectors, such as the implementation of the enhanced detector to improve the SCR (signal-to-clutter ratio) and the analysis of the detection performance.
Neural Neural Textures Make Sim2Real Consistent
Unpaired image translation algorithms can be used for sim2real tasks, but many fail to generate temporally consistent results.
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent.
BIT-Xiaomi’s System for AutoSimTrans 2022
This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge.
Online Video Instance Segmentation via Robust Context Fusion
We propose a robust context fusion network to tackle VIS in an online fashion, which predicts instance segmentation frame-by-frame with a few preceding frames.
Towards Cross-speaker Reading Style Transfer on Audiobook Dataset
This paper aims to introduce a chunk-wise multi-scale cross-speaker style model to capture both the global genre and the local prosody in audiobook speeches.
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning.
Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction
To learn it applicably, we propose a general clause-level encoding model named EA-GAT comprising E-GAT and Activation Sort.
Probabilistic Inverse Modeling: An Application in Hydrology
We propose uncertainty based learning method that offers 6\% improvement in $R^2$ for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17\% reduction in uncertainty (40\% in presence of noise) and 4\% higher coverage rate for basin characteristics.
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint
To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses.
Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications
To address this issue, we further propose a new strategy which augments a training segment with an initial value of the target variable from the timestep right before the starting of the training segment.
Pixel-Aligned Non-parametric Hand Mesh Reconstruction
The decoder aggregate both local image features in pixels and geometric features in vertices.
Disentangled and Robust Representation Learning for Bragging Classification in Social Media
Researching bragging behavior on social media arouses interest of computational (socio) linguists.
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
Adaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameter-agnostic manner, and empirically achieve faster convergence for solving minimization problems.
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation.
Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space
Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane.
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion
Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation.
Panoramic Video Salient Object Detection with Ambisonic Audio Guidance
In this paper, we aim to tackle the video salient object detection problem for panoramic videos, with their corresponding ambisonic audios.
Near-optimal Policy Identification in Active Reinforcement Learning
Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces.
Recovering Surveillance Video Using RF Cues
We make use of an auto-encoder-based structure to extract pose features from WiFi frames.
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution
We develop a hybrid dynamic-Transformer block(HDTB) that integrates the MHDLSA and SparseGSA for both local and global feature exploration.
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking
In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the SeeGera model, which is based on the family of self-supervised variational graph auto-encoder (VGAE).
Decision-Making Context Interaction Network for Click-Through Rate Prediction
Click-through rate (CTR) prediction is crucial in recommendation and online advertising systems.
Recurrent Structure Attention Guidance for Depth Super-Resolution
Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery.
Structure Flow-Guided Network for Real Depth Super-Resolution
Specifically, our framework consists of a cross-modality flow-guided upsampling network (CFUNet) and a flow-enhanced pyramid edge attention network (PEANet).
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data
We study the statistical inference of nonlinear stochastic approximation algorithms utilizing a single trajectory of Markovian data.
Mask-guided BERT for Few Shot Text Classification
The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting.
MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network
Conclusions: The experimental result demonstrates the effectiveness of the proposed MM-SFENet on the localization and classification of bladder cancer.
Statistical Analysis of Karcher Means for Random Restricted PSD Matrices
Non-asymptotic statistical analysis is often missing for modern geometry-aware machine learning algorithms due to the possibly intricate non-linear manifold structure.
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks.
HopFIR: Hop-wise GraphFormer with Intragroup Joint Refinement for 3D Human Pose Estimation
The HGF module groups the joints by k-hop neighbors and applies a hopwise transformer-like attention mechanism to these groups to discover latent joint synergies.
Ranked #141 on
3D Human Pose Estimation
on Human3.6M
Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge.
Non-aligned supervision for Real Image Dehazing
In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network.
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards
Building upon AdaOFUL, we propose VARA for linear MDPs, which achieves a tighter variance-aware regret bound of $\widetilde{O}(d\sqrt{HG^*K})$.
Sufficient Control of Complex Networks
In this paper, we propose to study on sufficient control of complex networks which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters.
Digital staining in optical microscopy using deep learning -- a review
Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology.
When Brain-inspired AI Meets AGI
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.
Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics
We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions.
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples.
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format.
Differentiate ChatGPT-generated and Human-written Medical Texts
We focus on analyzing the differences between medical texts written by human experts and generated by ChatGPT, and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT.
Asymptotic Behaviors and Phase Transitions in Projected Stochastic Approximation: A Jump Diffusion Approach
Additionally, we propose the Debiased LPSA (DLPSA) as a practical application of our jump diffusion approximation result.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
FreeLM: Fine-Tuning-Free Language Model
FreeLM outperforms large models e. g., GPT-3 and InstructGPT, on a range of language understanding tasks in experiments.
SPP-CNN: An Efficient Framework for Network Robustness Prediction
This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks.
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction
Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction.
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario
In this paper, we introduce gradient descent into black-box tuning scenario through knowledge distillation.
StereoVAE: A lightweight stereo matching system through embedded GPUs
We present a lightweight system for stereo matching through embedded GPUs.
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image
Our key contributions are: (1) We parameterize the geometry and appearance of the object using a multi-scale global feature extractor, which avoids frequent point-wise feature retrieval and camera dependency.
Artificial General Intelligence for Medical Imaging
In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.
A Graph Transformer-Driven Approach for Network Robustness Learning
Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks.
Shapley Value on Probabilistic Classifiers
Data valuation has become an increasingly significant discipline in data science due to the economic value of data.
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
To mitigate this brittleness, we propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting LLMs to generate explicit pieces of knowledge evidence in the form of structure triple.
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning.
Lexical Speaker Error Correction: Leveraging Language Models for Speaker Diarization Error Correction
Speaker diarization (SD) is typically used with an automatic speech recognition (ASR) system to ascribe speaker labels to recognized words.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Radiology-GPT: A Large Language Model for Radiology
We introduce Radiology-GPT, a large language model for radiology.
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.
Segment Anything Model (SAM) for Radiation Oncology
Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy.
Higher-order Graph Attention Network for Stock Selection with Joint Analysis
H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis.
