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Found 165 papers, 69 papers with code
Self-Modulating Nonparametric Event-Tensor Factorization
Tensor factorization is a fundamental framework to analyze high-order interactions in data.
FedPFT: Federated Proxy Fine-Tuning of Foundation Models
Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs.
ReFT: Representation Finetuning for Language Models
LoReFT is a drop-in replacement for existing PEFTs and learns interventions that are 10x-50x more parameter-efficient than prior state-of-the-art PEFTs.
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback
Large language models (LLMs) have shown remarkable progress in automated code generation.
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability.
Multimodal Query Suggestion with Multi-Agent Reinforcement Learning from Human Feedback
In this paper, we introduce a novel Multimodal Query Suggestion (MMQS) task, which aims to generate query suggestions based on user query images to improve the intentionality and diversity of search results.
Contributing Dimension Structure of Deep Feature for Coreset Selection
Existing methods typically measure both the representation and diversity of data based on similarity metrics, such as L2-norm.
Open-Vocabulary Video Relation Extraction
A comprehensive understanding of videos is inseparable from describing the action with its contextual action-object interactions.
HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses
Our architecture involving both explicit and implicit representation is simple yet effective.
A manometric feature descriptor with linear-SVM to distinguish esophageal contraction vigor
We conducted image processing of HRM to predict the esophageal contraction vigor for assisting the evaluation of esophageal dynamic function.
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation
We propose a conditional score model to control the solution generation by the input parameters and the fidelity.
Dynamic Association Learning of Self-Attention and Convolution in Image Restoration
Thus, we propose to refine background textures with the predicted degradation prior in an association learning manner.
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
To generalize Tucker decomposition to such scenarios, we propose Functional Bayesian Tucker Decomposition (FunBaT).
Foundation Models Meet Visualizations: Challenges and Opportunities
Recent studies have indicated that foundation models, such as BERT and GPT, excel in adapting to a variety of downstream tasks.
Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias
The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Graph-based semi-supervised learning (GSSL) has long been a hot research topic.
Beyond Domain Gap: Exploiting Subjectivity in Sketch-Based Person Retrieval
2) Multi-perspective and multi-style.
An Effective Two-stage Training Paradigm Detector for Small Dataset
Learning from the limited amount of labeled data to the pre-train model has always been viewed as a challenging task.
Uncovering the Unseen: Discover Hidden Intentions by Micro-Behavior Graph Reasoning
HID presents a unique challenge in that hidden intentions lack the obvious visual representations to distinguish them from normal intentions.
A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions
Fortunately, the availability of open-source stable diffusion models and their underlying mathematical principles has enabled the academic community to extensively analyze the performance of current image generation models and make improvements based on this stable diffusion framework.
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection
Early detection of dysplasia of the cervix is critical for cervical cancer treatment.
DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting
Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets.
A Generalized Physical-knowledge-guided Dynamic Model for Underwater Image Enhancement
To tackle these challenges, we design a Generalized Underwater image enhancement method via a Physical-knowledge-guided Dynamic Model (short for GUPDM), consisting of three parts: Atmosphere-based Dynamic Structure (ADS), Transmission-guided Dynamic Structure (TDS), and Prior-based Multi-scale Structure (PMS).
NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework
We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution.
From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement
Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images.
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling
Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection.
FLGo: A Fully Customizable Federated Learning Platform
Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios.
TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting
For precise utilization of the reference features for guidance, a reference-patch alignment (Ref-PA) module is proposed to align the patch features of the reference and corrupted images and harmonize their style differences, while a reference-patch transformer (Ref-PT) module is proposed to refine the embedded reference feature.
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
In addition, we introduce a cross-view contrastive learning paradigm to model the inter-dependencies across view-specific region representations and preserve underlying relation heterogeneity.
Long term 5G network traffic forecasting via modeling non-stationarity with deep learning
However, long term prediction horizon exposes the non-stationarity of series data, which deteriorates the performance of existing approaches.
One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms
In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP.
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks.
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
Second, we set up two tracks for the first time, i. e., Anti-UAV Tracking and Anti-UAV Detection & Tracking.
Group Activity Recognition via Dynamic Composition and Interaction
Group composition tells us the location of people and their relations inside the group, while interaction reflects the relation between humans and objects outside the group.
Group Activity Recognition
Human-Object Interaction Detection
+1
Automated Spatio-Temporal Graph Contrastive Learning
In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources.
ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding
Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearances. To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visual grounding.
Adaptive Modeling of Uncertainties for Traffic Forecasting
We evaluated QuanTraffic by applying it to five representative DNN models for traffic forecasting across seven public datasets.
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency.
Ranked #1 on
Anomaly Detection
on VisA
Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization
Bayesian optimization (BO) is widely used to optimize expensive-to-evaluate black-box functions. BO first builds a surrogate model to represent the objective function and assesses its uncertainty.
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
REASON consists of Topological Causal Discovery and Individual Causal Discovery.
Good Is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification
Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re-IDentification (ReID).
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support Learning
In this paper, considering the sensitivity of different modalities to diverse tumor regions, we propose a Category Aware Group Self-Support Learning framework, called GSS, to make up for the information deficit among the modalities in the individual modal feature extraction phase.
Multilateral Semantic Relations Modeling for Image Text Retrieval
Specifically, a given query is first mapped as a probabilistic embedding to learn its true semantic distribution based on Mahalanobis distance.
HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design
Adversarial attacks on thermal infrared imaging expose the risk of related applications.
Error-aware Quantization through Noise Tempering
In this work, we incorporate exponentially decaying quantization-error-aware noise together with a learnable scale of task loss gradient to approximate the effect of a quantization operator.
Prototypical Residual Networks for Anomaly Detection and Localization
Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness.
Ranked #2 on
Supervised Anomaly Detection
on MVTec AD
(using extra training data)
Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval
Therefore, previous works pre-train their models on rich-labeled photo retrieval data (i. e., source domain) and then fine-tune them on the limited-labeled sketch-to-photo retrieval data (i. e., target domain).
Ranked #1 on
Image Retrieval
on PKU-Reid
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability
Second, constrained by the far-distance in data distribution of the sampled clients, we further minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias.
On Inferring User Socioeconomic Status with Mobility Records
The DeepSEI model incorporates two networks called deep network and recurrent network, which extract the features of the mobility records from three aspects, namely spatiality, temporality and activity, one at a coarse level and the other at a detailed level.
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Detecting anomalous trajectories has become an important task in many location-based applications.
UmeTrack: Unified multi-view end-to-end hand tracking for VR
In this paper, we present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space.
A Survey of Dataset Refinement for Problems in Computer Vision Datasets
In addition, we organize these dataset refinement methods according to the addressed data problems and provide a systematic comparative description.
Context-Enhanced Stereo Transformer
We construct our stereo depth estimation model, Context Enhanced Stereo Transformer (CSTR), by plugging CEP into the state-of-the-art stereo depth estimation method Stereo Transformer.
SQuAT: Sharpness- and Quantization-Aware Training for BERT
Quantization is an effective technique to reduce memory footprint, inference latency, and power consumption of deep learning models.
Physical Adversarial Attack meets Computer Vision: A Decade Survey
Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world.
Faith: An Efficient Framework for Transformer Verification on GPUs
In this paper, we propose Faith, an efficient framework for transformer verification on GPUs.
MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms
For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery.
Detecting Algorithmically Generated Domains Using a GCNN-LSTM Hybrid Neural Network
Domain generation algorithm (DGA) is used by botnets to build a stealthy command and control (C&C) communication channel between the C&C server and the bots.
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem
Recent research on the robustness of deep learning has shown that Vision Transformers (ViTs) surpass the Convolutional Neural Networks (CNNs) under some perturbations, e. g., natural corruption, adversarial attacks, etc.
Reference-Guided Texture and Structure Inference for Image Inpainting
Existing learning-based image inpainting methods are still in challenge when facing complex semantic environments and diverse hole patterns.
Magic ELF: Image Deraining Meets Association Learning and Transformer
Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications.
Balanced Contrastive Learning for Long-Tailed Visual Recognition
In this paper, we focus on representation learning for imbalanced data.
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution
Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates.
Nonparametric Embeddings of Sparse High-Order Interaction Events
High-order interaction events are common in real-world applications.
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
In practice, tensor data is often accompanied by temporal information, namely the time points when the entry values were generated.
Infinite-Fidelity Coregionalization for Physical Simulation
Our model can interpolate and/or extrapolate the predictions to novel fidelities, which can be even higher than the fidelities of training data.
Towards Generalizable Person Re-identification with a Bi-stream Generative Model
Guided by original pedestrian images, one stream is employed to learn a camera-invariant global feature for the CC problem via filtering cross-camera interference factors.
Domain Generalization
Generalizable Person Re-identification
Improving Generalization of Metric Learning via Listwise Self-distillation
Most deep metric learning (DML) methods employ a strategy that forces all positive samples to be close in the embedding space while keeping them away from negative ones.
Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion
Specifically, based on the two discoveries of local spatial similarity and adjacent temporal correspondence of the sequential image data, we propose a novel Target-Domain driven pseudo label Diffusion (TDo-Dif) scheme.
Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval
The large variation of viewpoint and irrelevant content around the target always hinder accurate image retrieval and its subsequent tasks.
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning
Specifically, we propose to exploit the mutual information among them via iterative up-and-down projections, where the spatial and temporal features are fully fused and distilled, helping the high-quality video reconstruction.
Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study
In this work, a semi-automatic labeling method is adopted to build a large-scale compressed video quality database, which allows us to label a large number of compressed videos with manageable human workload.
A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges
In this paper, to address the current lack of comprehensive survey of existing LJP tasks, datasets, models and evaluations, (1) we analyze 31 LJP datasets in 6 languages, present their construction process and define a classification method of LJP with 3 different attributes; (2) we summarize 14 evaluation metrics under four categories for different outputs of LJP tasks; (3) we review 12 legal-domain pretrained models in 3 languages and highlight 3 major research directions for LJP; (4) we show the state-of-art results for 8 representative datasets from different court cases and discuss the open challenges.
Unsupervised Manga Character Re-identification via Face-body and Spatial-temporal Associated Clustering
In the past few years, there has been a dramatic growth in e-manga (electronic Japanese-style comics).
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding
We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples.
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Physical modeling is critical for many modern science and engineering applications.
Visual-Tactile Sensing for Real-time Liquid Volume Estimation in Grasping
We propose a deep visuo-tactile model for realtime estimation of the liquid inside a deformable container in a proprioceptive way. We fuse two sensory modalities, i. e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor without any extra sensor calibrations. The robotic system is well controlled and adjusted based on the estimation model in real time.
Dynamic GPU Energy Optimization for Machine Learning Training Workloads
This paper presents GPOEO, an online GPU energy optimization framework for machine learning training workloads.
TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e. g., e-commerce).
Self-Adaptable Point Processes with Nonparametric Time Decays
Specifically, we use an embedding to represent each event type and model the event influence as an unknown function of the embeddings and time span.
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding
Unlike previous methods that mainly focus on closing the domain gap caused by fog -- defogging the foggy images or fogging the clear images, we propose to alleviate the domain gap by considering fog influence and style variation simultaneously.
Ranked #4 on
Domain Adaptation
on Cityscapes-to-FoggyZurich
Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification
The proposed model aims to mine the hidden semantic information and intrinsic structure information of all available data, which is suitable for few labeled samples and proportion imbalance between labeled samples and unlabeled samples problems in frontal face recognition.
Optimizing Sparse Matrix Multiplications for Graph Neural Networks
Our model is first trained offline using training matrix samples, and the trained model can be applied to any input matrix and GNN kernels with SpMM computation.
Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes
Compared with the existent works, our model not only leverages the structural information underlying the observed entry indices, but also provides extra interpretability and flexibility -- it can simultaneously estimate a set of location factors about the intrinsic properties of the tensor nodes, and another set of sociability factors reflecting their extrovert activity in interacting with others; users are free to choose a trade-off between the two types of factors.
Meta-Learning with Adjoint Methods
the initialization, we only need to run the standard ODE solver twice -- one is forward in time that evolves a long trajectory of gradient flow for the sampled task; the other is backward and solves the adjoint ODE.
GANet: Glyph-Attention Network for Few-Shot Font Generation
Font generation is a valuable but challenging task, it is time consuming and costly to design font libraries which cover all glyphs with various styles.
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge
Mitotic figure count is an important marker of tumor proliferation and has been shown to be associated with patients' prognosis.
Modularized Interaction Network for Named Entity Recognition
Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks.
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation
Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects.
Spectrum Gaussian Processes Based On Tunable Basis Functions
Spectral approximation and variational inducing learning for the Gaussian process are two popular methods to reduce computational complexity.
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.
Zero-shot Node Classification with Decomposed Graph Prototype Network
Node classification is a central task in graph data analysis.
Unsupervised Video Person Re-identification via Noise and Hard frame Aware Clustering
NHAC consists of a graph trimming module and a node re-sampling module.
Effect of Adaptive and Fixed Shared Steering Control on Distracted Driver Behavior
Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority.
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline Summarization
We meticulously analyze the corpus using well-known metrics, focusing on the style of the summaries and the complexity of the summarization task.
Federated Learning with Fair Averaging
Fairness has emerged as a critical problem in federated learning (FL).
Expanding Semantic Knowledge for Zero-shot Graph Embedding
Zero-shot graph embedding is a major challenge for supervised graph learning.
Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement
Different from existing methods tending to accomplish the relighting task directly by ignoring the fidelity and naturalness recovery, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps.
A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments
Learned indices have been proposed to replace classic index structures like B-Tree with machine learning (ML) models.
Model Rectification via Unknown Unknowns Extraction from Deployment Samples
Model deficiency that results from incomplete training data is a form of structural blindness that leads to costly errors, oftentimes with high confidence.
Deep unsupervised anomaly detection
This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting.
Image Inpainting Guided by Coherence Priors of Semantics and Textures
In this paper, we introduce coherence priors between the semantics and textures which make it possible to concentrate on completing separate textures in a semantic-wise manner.
Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric
In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i. e.}, all returned images are considered as the target.
Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System
By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle.
Towards Context-Aware Code Comment Generation
Code comments are vital for software maintenance and comprehension, but many software projects suffer from the lack of meaningful and up-to-date comments in practice.
Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning
In this article, the authors present a novel method to learn the personalized tactic of discretionary lane-change initiation for fully autonomous vehicles through human-computer interactions.
Universal Weighting Metric Learning for Cross-Modal Matching
Furthermore, we introduce a new polynomial loss under the universal weighting framework, which defines a weight function for the positive and negative informative pairs respectively.
Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks
With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs.
DTDN: Dual-task De-raining Network
Removing rain streaks from rainy images is necessary for many tasks in computer vision, such as object detection and recognition.
Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation.
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
We formulate the mutual transformations between the outputs of regression- and detection-based models as two scene-agnostic transformers which enable knowledge distillation between the two models.
3D Spectrum Mapping Based on ROI-Driven UAV Deployment
Given the explosive growth of Internet of Things (IoT) devices ranging from the two-dimensional (2D) ground to the three-dimensional (3D) space, it is a necessity to establish a 3D spectrum map to comprehensively present and effectively manage the 3D spatial spectrum resources in smart city infrastructures.
Exploring Image Enhancement for Salient Object Detection in Low Light Images
Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights.
Streaming Probabilistic Deep Tensor Factorization
Our algorithm provides responsive incremental updates for the posterior of the latent factors and NN weights upon receiving new tensor entries, and meanwhile select and inhibit redundant/useless weights.
Network Embedding with Completely-imbalanced Labels
Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research.
Road Network Metric Learning for Estimated Time of Arrival
To address the data sparsity problem, we propose the Road Network Metric Learning framework for ETA (RNML-ETA).
Sparse Gaussian Process Based On Hat Basis Functions
Based on hat basis functions, we propose a new sparse Gaussian process method to solve the unconstrained regression problem.
Physics Informed Deep Kernel Learning
Deep kernel learning is a promising combination of deep neural networks and nonparametric function learning.
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation
To address these issues, we propose Multi-Fidelity High-Order Gaussian Process (MFHoGP) that can capture complex correlations both between the outputs and between the fidelities to enhance solution estimation, and scale to large numbers of outputs.
Fusion Recurrent Neural Network
Furthermore, in order to evaluate Fusion RNN's sequence feature extraction capability, we choose a representative data mining task for sequence data, estimated time of arrival (ETA) and present a novel model based on Fusion RNN.
FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention
Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in recent years.
Neighborhood Matching Network for Entity Alignment
This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge.
Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting
Recently, deep learning based methods have achieved promising results by adopting graph convolutional network (GCN) to extract the spatial correlations and recurrent neural network (RNN) to capture the temporal dependencies.
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes
Completing a corrupted image with correct structures and reasonable textures for a mixed scene remains an elusive challenge.
Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures: A Machine Learning Based Approach
This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures.
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning
Similar trajectory search is a fundamental problem and has been well studied over the past two decades.
Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance
Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries.
Curriculum Audiovisual Learning
Associating sound and its producer in complex audiovisual scene is a challenging task, especially when we are lack of annotated training data.
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation
To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category.
Characterizing Scalability of Sparse Matrix-Vector Multiplications on Phytium FT-2000+ Many-cores
Understanding the scalability of parallel programs is crucial for software optimization and hardware architecture design.
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection
For image classification, we achieve a 1. 8x reduction in inference time with a 7. 52% improvement in accuracy, over the most-capable single DNN model.
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
There has recently been a surge in research in batch Deep Reinforcement Learning (DRL), which aims for learning a high-performing policy from a given dataset without additional interactions with the environment.
Conditional Expectation Propagation
Expectation propagation (EP) is a powerful approximate inference algorithm.
Jointly Learning Entity and Relation Representations for Entity Alignment
Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs).
Ranked #18 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods.
Ranked #20 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Zero-Shot Feature Selection via Transferring Supervised Knowledge
Feature selection, an effective technique for dimensionality reduction, plays an important role in many machine learning systems.
Robust Linear Discriminant Analysis Using Ratio Minimization of L1,2-Norms
In this paper, we propose a novel robust linear discriminant analysis method based on the L1, 2-norm ratio minimization.
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification
An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis.
Less Memory, Faster Speed: Refining Self-Attention Module for Image Reconstruction
Self-attention (SA) mechanisms can capture effectively global dependencies in deep neural networks, and have been applied to natural language processing and image processing successfully.
DotSCN: Group Re-identification via Domain-Transferred Single and Couple Representation Learning
To gain the superiority of deep learning models, we treat a group as multiple persons and transfer the domain of a labeled ReID dataset to a G-ReID target dataset style to learn single representations.
Ensemble Super-Resolution with A Reference Dataset
In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers).
Illumination-Adaptive Person Re-identification
To demonstrate the illumination issue and to evaluate our model, we construct two large-scale simulated datasets with a wide range of illumination variations.
Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images
Robust semantic segmentation of VHR remote sensing images from UAV sensors is critical for earth observation, land use, land cover or mapping applications.
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)
Robotics I.2.9; I.2.0
Lattice CNNs for Matching Based Chinese Question Answering
Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly.
Representation Learning for Spatial Graphs
Recently, the topic of graph representation learning has received plenty of attention.
Global and Local Sensitivity Guided Key Salient Object Re-augmentation for Video Saliency Detection
In this paper, based on the fact that salient areas in videos are relatively small and concentrated, we propose a \textbf{key salient object re-augmentation method (KSORA) using top-down semantic knowledge and bottom-up feature guidance} to improve detection accuracy in video scenes.
Composite Binary Decomposition Networks
Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the full-precision counterparts.
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
We experimentally show that how two mainstream compression techniques, data quantization and pruning, perform on these network architectures and the implications of compression techniques to the model storage size, inference time, energy consumption and performance metrics.
SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection
To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance.
Socially Aware Kalman Neural Networks for Trajectory Prediction
Trajectory prediction is a critical technique in the navigation of robots and autonomous vehicles.
Hyperspectral Image Classification in the Presence of Noisy Labels
The key idea of RLPA is to exploit knowledge (e. g., the superpixel based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation.
Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data.
Adaptive Selection of Deep Learning Models on Embedded Systems
This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time.
Machine Learning in Compiler Optimisation
In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity.
Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach
In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given application, targeting the Intel XeonPhi architecture.
Performance
Scale Up Event Extraction Learning via Automatic Training Data Generation
We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.
End-to-End Deep Learning of Optimization Heuristics
We develop a deep neural network that learns heuristics over raw code, entirely without using code features.
Equivalence between LINE and Matrix Factorization
LINE [1], as an efficient network embedding method, has shown its effectiveness in dealing with large-scale undirected, directed, and/or weighted networks.
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision.
Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising
In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion
Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.
UPPAAL-SMC: Statistical Model Checking for Priced Timed Automata
The focus of the survey is on the evolution of the tool - including modeling and specification formalisms as well as techniques applied - together with applications of the tool to case studies.
Logic in Computer Science Formal Languages and Automata Theory
