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Found 75 papers, 22 papers with code
The NiuTrans Machine Translation Systems for WMT20
This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks.
Edge-aware Feature Aggregation Network for Polyp Segmentation
Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation.
Enhancing Event Sequence Modeling with Contrastive Relational Inference
Extensive experiments on three real-world datasets demonstrate the effectiveness of our model in capturing event interactions for event sequence modeling tasks.
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge.
COCA: Classifier-Oriented Calibration for Source-Free Universal Domain Adaptation via Textual Prototype
Essentially, we present a novel paradigm based on the vision-language model to learn SF-UniDA and hugely reduce the labeling costs on the source domain.
Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases
Thus, the representation of users' dislikes should be integrated into the user modelling when we construct a collaborative recommendation model.
Bounded KRnet and its applications to density estimation and approximation
In this paper, we develop an invertible mapping, called B-KRnet, on a bounded domain and apply it to density estimation/approximation for data or the solutions of PDEs such as the Fokker-Planck equation and the Keller-Segel equation.
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target.
Can SAM Segment Polyps?
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks.
AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources
We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.
Self-Paced Learning for Open-Set Domain Adaptation
Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples.
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning
We propose a novel framework for uncertainty quantification via information bottleneck (IB-UQ) for scientific machine learning tasks, including deep neural network (DNN) regression and neural operator learning (DeepONet).
Failure-informed adaptive sampling for PINNs, Part II: combining with re-sampling and subset simulation
The second extension is to present the subset simulation algorithm as the posterior model (instead of the truncated Gaussian model) for estimating the error indicator, which can more effectively estimate the failure probability and generate new effective training points in the failure region.
Prompting Neural Machine Translation with Translation Memories
Improving machine translation (MT) systems with translation memories (TMs) is of great interest to practitioners in the MT community.
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives
Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects.
Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis
To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis.
Feature Aggregation and Propagation Network for Camouflaged Object Detection
In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection.
A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanisms
Brain network provides important insights for the diagnosis of many brain disorders, and how to effectively model the brain structure has become one of the core issues in the domain of brain imaging analysis.
SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates
Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24. 4X speedup over PyTorch Data Loader and 3. 52X speedup over state-of-the-art data loaders.
Adaptive deep density approximation for fractional Fokker-Planck equations
To this end, we represent the solution with an explicit PDF model induced by a flow-based deep generative model, simplified KRnet, which constructs a transport map from a simple distribution to the target distribution.
Failure-informed adaptive sampling for PINNs
For instance, a fixed set of (prior chosen) training points may fail to capture the effective solution region (especially for problems with singularities).
Prompt-driven efficient Open-set Semi-supervised Learning
In this paper, we propose a prompt-driven efficient OSSL framework, called OpenPrompt, which can propagate class information from labeled to unlabeled data with only a small number of trainable parameters.
Myopia prediction for adolescents via time-aware deep learning
Result: The mean absolute prediction error on the testing set was 0. 273-0. 257 for spherical equivalent, ranging from 0. 189-0. 160 to 0. 596-0. 473 if we consider different lengths of historical records and different prediction durations.
Deep learning at the edge enables real-time streaming ptychographic imaging
Coherent microscopy techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells.
Camouflaged Object Detection via Context-aware Cross-level Fusion
To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects.
Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation
Further, by defining a new form of data centroid, we transform the recovery problem of a label-dependent part to a centroid estimation problem.
On Vision Features in Multimodal Machine Translation
Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision models.
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Inspired by this, we design a new architecture, {\it ODE Transformer}, which is analogous to the Runge-Kutta method that is well motivated in ODE.
Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations
We introduce a sampling based machine learning approach, Monte Carlo physics informed neural networks (MC-PINNs), for solving forward and inverse fractional partial differential equations (FPDEs).
Consistency and Diversity induced Human Motion Segmentation
Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance.
Solving time dependent Fokker-Planck equations via temporal normalizing flow
In this work, we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck (TFP) equations.
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning
Recent studies propose leveraging the rich contextual information in data provenance to detect threats in a host.
The NiuTrans Machine Translation Systems for WMT21
This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks.
Information Cocoons in Online Navigation
Social media and online navigation bring us enjoyable experience in accessing information, and simultaneously create information cocoons (ICs) in which we are unconsciously trapped with limited and biased information.
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models
We introduce in this work the normalizing field flows (NFF) for learning random fields from scattered measurements.
Specificity-preserving RGB-D Saliency Detection
To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings.
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation
Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor using the intermediate data distributions generated for the two domains, which includes an image-to-image translator and a shared-weighted segmentation network.
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation
We show that a residual block of layers in Transformer can be described as a higher-order solution to ODEs.
Collaborative Filtering Approach to Link Prediction
Considering the delicate but important difference between personalized recommendation and link prediction, we further propose an improved framework named as self-included collaborative filtering (SCF).
Progresses and Challenges in Link Prediction
Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology.
Visual-Textual Attentive Semantic Consistency for Medical Report Generation
Second, the progressive report decoder consists of a sentence decoder and a word decoder, where we propose image-sentence matching and description accuracy losses to constrain the visual-textual semantic consistency.
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis
A category-invariant cross-domain transfer (CCT) method is proposed to address this single-to-multiple extension.
Improving EEG Decoding via Clustering-based Multi-task Feature Learning
With the encoded label matrix, we devise a novel multi-task learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses.
High resolution functional imaging through Lorentz transmission electron microscopy and differentiable programming
Lorentz transmission electron microscopy is a unique characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials at high spatial resolution.
Light Field Salient Object Detection: A Review and Benchmark
Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved.
Dynamic Fusion based Federated Learning for COVID-19 Detection
To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections.
RGB-D Salient Object Detection: A Survey
Further, considering that the light field can also provide depth maps, we review SOD models and popular benchmark datasets from this domain as well.
Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection
Our MCI-Net includes two key components: 1) a cross-modal feature learning network, which is used to learn the high-level features for the RGB images and depth cues, effectively enabling the correlations between the two sources to be exploited; and 2) a multi-level interactive integration network, which integrates multi-level cross-modal features to boost the SOD performance.
Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images
Diagnosis from chest CT images is a promising direction.
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
Ranked #6 on
Camouflaged Object Segmentation
on CAMO
(using extra training data)
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation
Human motion segmentation based on transfer subspace learning is a rising interest in action-related tasks.
ParaDiag: parallel-in-time algorithms based on the diagonalization technique
These results are obtained on the Tianhe-1 supercomputer in China and the SIUE Campus Cluster in the US and and we compare these results to the performance of parareal and MGRiT, two widely used PinT algorithms.
Numerical Analysis Numerical Analysis
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis.
Real-time sparse-sampled Ptychographic imaging through deep neural networks
Ptychography has rapidly grown in the fields of X-ray and electron imaging for its unprecedented ability to achieve nano or atomic scale resolution while simultaneously retrieving chemical or magnetic information from a sample.
Contrast-weighted Dictionary Learning Based Saliency Detection for Remote Sensing Images
Furthermore, by using the proposed joint saliency measure, a variety of saliency maps are generated based on the discriminant dictionary.
Exploration of Surgeons' Natural Skills for Robotic Catheterization
Despite having the robotic catheter systems which have recently emerged as safe way of performing cardiovascular interventions, a number of important challenges are yet to be investigated.
Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis
Medical image synthesis has been proposed as an effective solution to this, where any missing modalities are synthesized from the existing ones.
PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking Scenarios
For each point in the point cloud, the network regresses a 6D pose of the object instance that the point belongs to.
Hierarchical Clustering Supported by Reciprocal Nearest Neighbors
Clustering is a fundamental analysis tool aiming at classifying data points into groups based on their similarity or distance.
Computational Socioeconomics
Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development.
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs).
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Doodling is a useful and common intelligent skill that people can learn and master.
Customer Sharing in Economic Networks with Costs
To tackle this problem, we develop a novel mechanism called customer sharing mechanism (CSM) which incentivizes all sellers to share each other's sale information to their private customer groups.
Payoff Control in the Iterated Prisoner's Dilemma
Repeated game has long been the touchstone model for agents' long-run relationships.
A generalized method toward drug-target interaction prediction via low-rank matrix projection
On one hand, when there is no extra characteristic information of drugs or targets, the proposed method utilizes only the known interactions.
Attention-based Natural Language Person Retrieval
Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism.
Mechanism Design in Social Networks
The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue.
Computer Science and Game Theory
Link Prediction via Matrix Completion
Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years.
Patch-based Texture Synthesis for Image Inpainting
We form it as an optimization problem that identifies the potential patches for synthesis from an coarse-to-fine manner.
Visual Tracking via Nonnegative Regularization Multiple Locality Coding
Weights of these various dictionaries are also learned from approximated LLC in the similar framework.
Robust Visual Tracking via Inverse Nonnegative Matrix Factorization
It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors.
Recommender Systems
In this article, we review recent developments in recommender systems and discuss the major challenges.
Predicting Missing Links via Local Information
Missing link prediction of networks is of both theoretical interest and practical significance in modern science.
Ranked #1 on
Link Property Prediction
on ogbl-ppa
Data Analysis, Statistics and Probability Physics and Society
