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Found 170 papers, 58 papers with code
基于人物特征增强的拟人句要素抽取方法研究(Research on Element Extraction of Personified Sentences Based on Enhanced Characters)
“在散文阅读理解的鉴赏类问题中, 对拟人句赏析考查比较频繁。目前, 已有的工作仅对拟人句中的本体要素进行识别并抽取, 存在要素抽取不完整的问题, 尤其是当句子中出现多个本体时, 需要确定拟人词与各个本体的对应关系。为解决这些问题, 本文提出了基于人物特征增强的拟人句要素抽取方法。该方法利用特定领域的特征, 增强句子的向量表示, 再利用条件随机场模型对拟人句中的本体和拟人词要素进行识别。在此基础上, 利用自注意力机制对要素之间的关系进行检测, 使用要素同步机制和关系同步机制进行信息交互, 用于要素识别和关系检测的输入更新。在自建的拟人数据集上进行<本体, 拟人词>抽取的比较实验, 结果表明本文提出的模型性能优于其他比较模型。”
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Recently years have witnessed a rapid development of large language models (LLMs).
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-Training
In this work, we introduce the concept of complex text style transfer tasks, and constructed complex text datasets based on two widely applicable scenarios.
Error Reduction from Stacked Regressions
In this paper, we learn these weights analogously by minimizing an estimate of the population risk subject to a nonnegativity constraint.
Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning
Moreover, we argue that object localization and description generation require different levels of scene understanding, which could be challenging for a shared set of queries to capture.
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Sleep stage classification is crucial for detecting patients' health conditions.
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data
Then, a feature discrimination approach with hybrid similarity measurement is introduced to measure and mitigate the structural and vector angle impacts of varying imaging conditions on the extracted features from SAR images.
Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking
The Bridge Module incorporates the high-level information of deep features into the shallow large-resolution features.
Towards Carbon-Free Electricity: A Comprehensive Flow-Based Framework for Power Grid Carbon Accounting and Decarbonization
This paper presents a comprehensive framework for advancing research and policy development related to decarbonization in electric power systems.
Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies
Accurate online transient stability prediction is critical for ensuring power system stability when facing disturbances.
Deep Reinforcement Learning-Based Battery Conditioning Hierarchical V2G Coordination for Multi-Stakeholder Benefits
On the grid side, load fluctuations and renewable energy consumption are considered, while on the EVA side, energy constraints and charging costs are considered.
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules.
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
In our experiments, the proposed method achieves a sensitivity of 85. 0% and specificity of 92. 6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal.
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data.
Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation
We present a novel alignment-before-generation approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts.
MotionGPT: Human Motion as a Foreign Language
Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language.
Higher-order Motif-based Time Series Classification for Forced Oscillation Source Location in Power Grids
A MECF-based unsupervised learning approach is applied in locating the source of the forced oscillation (FO), a periodic disturbance that detrimentally impacts power grids.
Coupled Attention Networks for Multivariate Time Series Anomaly Detection
To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships.
Differentially private sliced inverse regression in the federated paradigm
Sliced inverse regression (SIR), which includes linear discriminant analysis (LDA) as a special case, is a popular and powerful dimension reduction tool.
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry.
Fast global convergence of gradient descent for low-rank matrix approximation
This paper investigates gradient descent for solving low-rank matrix approximation problems.
Learning Safety Constraints from Demonstrations with Unknown Rewards
We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL), a novel approach for inferring shared constraints in a Constrained Markov Decision Process (CMDP) from a set of safe demonstrations with possibly different reward functions.
Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation
Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories.
Collective Large-scale Wind Farm Multivariate Power Output Control Based on Hierarchical Communication Multi-Agent Proximal Policy Optimization
Simulation results demonstrate that the proposed multivariate HCMAPPO can significantly increase wind farm power output compared to the traditional PID control, coordinated model-based predictive control, and multi-agent deep deterministic policy gradient algorithm.
An Object SLAM Framework for Association, Mapping, and High-Level Tasks
Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching.
Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps
However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases. To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road. Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results.
SeqTrack: Sequence to Sequence Learning for Visual Object Tracking
In this paper, we present a new sequence-to-sequence learning framework for visual tracking, dubbed SeqTrack.
Ranked #2 on
Visual Object Tracking
on TNL2K
Fulfilling Formal Specifications ASAP by Model-free Reinforcement Learning
We propose a model-free reinforcement learning solution, namely the ASAP-Phi framework, to encourage an agent to fulfill a formal specification ASAP.
Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism
The Mixture of Experts (MoE) model becomes an important choice of large language models nowadays because of its scalability with sublinear computational complexity for training and inference.
Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?
Then, a novel multi-view prompt fusion module is developed to effectively fuse information from different views to bridge the gap between 3D point cloud data and 2D pre-trained models.
PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation
The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups.
Triple Sequence Learning for Cross-domain Recommendation
To address this issue, we present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR.
A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles
Accurate state-of-health (SOH) estimation is critical to guarantee the safety, efficiency and reliability of battery-powered applications.
Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization
Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant.
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.
Visual Prompt Multi-Modal Tracking
To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters.
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering
In particular, the highest accuracy reaches 99. 68\% in evaluation, which demonstrates a good knowledge transfer ability of the proposed model for power grid topology change.
Text-Visual Prompting for Efficient 2D Temporal Video Grounding
In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long untrimmed video.
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification
In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication.
Disentangled Causal Embedding With Contrastive Learning For Recommender System
Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results.
Sketched Ridgeless Linear Regression: The Role of Downsampling
This paper proposes a dual view of overparametrization suggesting that downsampling may also help generalize.
Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan.
Optimal Inter-area Oscillation Damping Control: A Transfer Deep Reinforcement Learning Approach with Switching Control Strategy
Wide-area damping control for inter-area oscillation (IAO) is critical to modern power systems.
A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction
Meanwhile, a new benchmark for several outdoor NeRF-based tasks is established, such as novel view synthesis, surface reconstruction, and multi-modal NeRF.
End-to-End 3D Dense Captioning with Vote2Cap-DETR
Compared with prior arts, our framework has several appealing advantages: 1) Without resorting to numerous hand-crafted components, our method is based on a full transformer encoder-decoder architecture with a learnable vote query driven object decoder, and a caption decoder that produces the dense captions in a set-prediction manner.
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases.
Fan-Beam Binarization Difference Projection (FB-BDP): A Novel Local Object Descriptor for Fine-Grained Leaf Image Retrieval
Fine-grained leaf image retrieval (FGLIR) aims to search similar leaf images in subspecies level which involves very high interclass visual similarity and accordingly poses great challenges to leaf image description.
Executing your Commands via Motion Diffusion in Latent Space
We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors.
Ranked #1 on
Motion Synthesis
on KIT Motion-Language
Label Mask AutoEncoder(L-MAE): A Pure Transformer Method to Augment Semantic Segmentation Datasets
Semantic segmentation models based on the conventional neural network can achieve remarkable performance in such tasks, while the dataset is crucial to the training model process.
An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention
This study proposes an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer's encoder-decoder structure.
Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast
In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast.
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
Heterogeneous graph neural network has unleashed great potential on graph representation learning and shown superior performance on downstream tasks such as node classification and clustering.
2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study
We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks.
Learning Variational Motion Prior for Video-based Motion Capture
To improve the generalization capacity of prior space, we propose a transformer-based variational autoencoder pretrained over marker-based 3D mocap data, with a novel style-mapping block to boost the generation quality.
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance
Explainability plays an increasingly important role in machine learning.
DIICAN: Dual Time-scale State-Coupled Co-estimation of SOC, SOH and RUL for Lithium-Ion Batteries
A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper to estimate SOC, SOH, and RUL, which organizes battery measurement data into the intra-cycle and inter-cycle time scales.
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI
The final trained model was also evaluated on an independent test set by the CMRxMotion organisers, which achieved the classification accuracy of 72. 5% and Cohen's Kappa of 0. 6309 (ranked top 1 in this grand challenge).
ConvTransSeg: A Multi-resolution Convolution-Transformer Network for Medical Image Segmentation
We compared our method with many other state-of-the-art hybrid CNN and Transformer segmentation models on binary and multiple class image segmentation tasks using several public medical image datasets, including skin lesion, polyp, cell and brain tissue.
Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources
Due to the increasing popularity of electric vehicles (EVs) and the technological advancement of EV electronics, the vehicle-to-grid (V2G) technique and large-scale scheduling algorithms have been developed to achieve a high level of renewable energy and power grid stability.
Two-Stream UNET Networks for Semantic Segmentation in Medical Images
Recent advances of semantic image segmentation greatly benefit from deeper and larger Convolutional Neural Network (CNN) models.
Contrastive Deep Supervision
The success of deep learning is usually accompanied by the growth in neural network depth.
SRRT: Search Region Regulation Tracking
Trackers tend to lose the target object due to the limited search region or be interfered with by distractors due to the excessive search region.
Sequential Recommendation Model for Next Purchase Prediction
Timeliness and contextual accuracy of recommendations are increasingly important when delivering contemporary digital marketing experiences.
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning
Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields.
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization
By combining the structural reparameterization and PaS, we successfully searched out a new family of VGG-like and lightweight networks, which enable the flexibility of arbitrary width with respect to each layer instead of each stage.
Region-aware Knowledge Distillation for Efficient Image-to-Image Translation
In this paper, we propose Region-aware Knowledge Distillation ReKo to compress image-to-image translation models.
An Empirical Investigation of Representation Learning for Imitation
We propose a modular framework for constructing representation learning algorithms, then use our framework to evaluate the utility of representation learning for imitation across several environment suites.
Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling
In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search
It is able to find top 0. 16\% and 0. 29\% architectures on average on two search spaces under the budget of only 50 models.
Efficient Visual Tracking via Hierarchical Cross-Attention Transformer
Most state-of-the-art trackers are satisfied with the real-time speed on powerful GPUs.
High-Performance Transformer Tracking
First, we present a transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head.
Multi-view Multi-behavior Contrastive Learning in Recommendation
We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user.
Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems
Establishing cleaner energy generation therefore improving the sustainability of the power system is a crucial task in this century, and one of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies such as wind, solar, and nuclear.
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation
Instead of directly distilling the generated images of teachers, wavelet knowledge distillation first decomposes the images into different frequency bands with discrete wavelet transformation and then only distills the high frequency bands.
RestainNet: a self-supervised digital re-stainer for stain normalization
In this paper, we formulated stain normalization as a digital re-staining process and proposed a self-supervised learning model, which is called RestainNet.
Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction
The degradation-related features extracted from the sensor streaming data with neural networks can dramatically improve the accuracy of the RUL prediction.
CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs
In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices.
Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting
The battery capacity in different cycles can be measured with the temporal patterns extracted from the streaming sensor data based on the attention mechanism.
Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network
Transient stability prediction is critically essential to the fast online assessment and maintaining the stable operation in power systems.
AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees
Specifically, we collect a sheer number of source codes (both Java and Python) from the Alipay code repository and incorporate both syntactic and semantic code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated.
Resource allocation algorithm for MEC based on Deep Reinforcement Learning
In recent years, driven by the commercialization of the 6th Generation Communication Technology (6G), an increasing number of 6G devices connected to mobile networks produces computation-intensive tasks such as ultra-high-resolution video streaming, inter-active visual reality (VR) gaming, augmented reality (AR).
GPS: A Policy-driven Sampling Approach for Graph Representation Learning
Graph representation learning has drawn increasing attention in recent years, especially for learning the low dimensional embedding at both node and graph level for classification and recommendations tasks.
Radiomic biomarker extracted from PI-RADS 3 patients support more eìcient and robust prostate cancer diagnosis: a multi-center study
Prostate Imaging Reporting and Data System (PI-RADS) based on multi-parametric MRI classi\^ees patients into 5 categories (PI-RADS 1-5) for routine clinical diagnosis guidance.
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization
We consider stochastic optimization when one only has access to biased stochastic oracles of the objective, and obtaining stochastic gradients with low biases comes at high costs.
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels
In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
+1
Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks
Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific.
EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation
Given a query and an untrimmed video, the temporal grounding model predicts the target interval, and the predicted video clip is fed into a video translation task by generating a simplified version of the input query.
Self-supervised Tumor Segmentation through Layer Decomposition
In this paper, we target self-supervised representation learning for zero-shot tumor segmentation.
Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding
And the result shows that the performance of NER will be improved if the word specificity is incorporated into existing NER methods.
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain knowledge for the generation of readable medical reports.
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI
The proposed method was evaluated on a public brain MRI data set for age estimation.
Neural Free-Viewpoint Performance Rendering under Complex Human-object Interactions
In this paper, we propose a neural human performance capture and rendering system to generate both high-quality geometry and photo-realistic texture of both human and objects under challenging interaction scenarios in arbitrary novel views, from only sparse RGB streams.
Template-based Chatbot for Agriculture Related FAQs
Agriculture is the fundamental industry of the society, which is the basis of food supply and an important source of employment and GDP increase.
Few-shot Neural Human Performance Rendering from Sparse RGBD Videos
To fill this gap, in this paper we propose a few-shot neural human rendering approach (FNHR) from only sparse RGBD inputs, which exploits the temporal and spatial redundancy to generate photo-realistic free-view output of human activities.
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).
Interior point search for nonparametric image segmentation
Thus, our method offers robust automatic image segmentation that is simpler to use and less time-consuming than traditional snake models
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search
While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos
In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.
Transformer Tracking
The correlation operation is a simple fusion manner to consider the similarity between the template and the search region.
Ranked #5 on
Visual Tracking
on TNL2K
ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References
We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner.
Two-sided Dirichlet heat estimates of symmetric stable processes on horn-shaped regions
In this paper, we consider symmetric $\alpha$-stable processes on (unbounded) horn-shaped regions which are non-uniformly $C^{1, 1}$ near infinity.
Probability
Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility.
Optimal Clustering in Anisotropic Gaussian Mixture Models
We study the clustering task under anisotropic Gaussian Mixture Models where the covariance matrices from different clusters are unknown and are not necessarily the identical matrix.
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection
Here we present a novel self-supervised 3D Object detection framework that seamlessly integrates the geometry-aware contrast and clustering harmonization to lift the unsupervised 3D representation learning, named GCC-3D.
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search
While existing NAS methods mostly design architectures on one single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models.
Object SLAM-Based Active Mapping and Robotic Grasping
The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process that is optimized for robotic grasping.
Graph Stochastic Neural Networks for Semi-supervised Learning
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification.
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
Conditional stochastic optimization covers a variety of applications ranging from invariant learning and causal inference to meta-learning.
Multi-feature driven active contour segmentation model for infrared image with intensity inhomogeneity
Active contour model (ACM) is one of the most widely used image segmentation tools at present, but the existing methods only utilize the local or global single feature information of image to minimize the energy function, which is easy to cause false segmentations in IR images.
Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction
This strategy estimates the depth map at coarsest level, while the depth maps at finer levels are considered as the upsampled depth map from previous level with pixel-wise depth residual.
Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction
Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community.
Learning to Build User-tag Profile in Recommendation System (UTPM)
User profiling is one of the most important components in recommendation systems, where a user is profiled using demographic (e. g. gender, age, and location) and user behavior information (e. g. browsing and search history).
Online Learning and Distributed Control for Residential Demand Response
This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR).
Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things
In order to minimize the long-term average delay of the tasks, theoriginal problem is transformed into a Markov decision process (MDP).
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap
Neural architecture search (NAS) has attracted increasing attentions in both academia and industry.
Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization
In this paper, we present an approach that first transforms the original high-dimensional, constrained multiobjective optimization problem to a low-dimensional, unconstrained multiobjective optimization problem, and then evaluates each solution to the transformed problem by solving a set of simple single-objective optimization subproblems, such that the problem can be efficiently solved by existing evolutionary multiobjective algorithms.
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search
In spite of its remarkable progress, many algorithms are restricted to particular search spaces.
Ranked #12 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
GOLD-NAS: Gradual, One-Level, Differentiable
There has been a large literature of neural architecture search, but most existing work made use of heuristic rules that largely constrained the search flexibility.
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task
To alleviate this problem, we investigate the utilization of the end-to-end graph representation learning for the DDI prediction task.
AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
We base our work on the assumption that most human-made objects are constituted by parts and these parts can be well represented by generalized primitives.
AnimeGAN: A Novel Lightweight GAN for Photo Animation
The existing methods usually have some problems, among which significant problems mainly include: 1) the generated images have no obvious animated style textures; 2) the generated images lose the content of the original images; 3) the parameters of the network require the large memory capacity.
A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity
This paper presents a novel meta learning framework for feature selection (FS) based on fuzzy similarity.
Performance Optimization of a Fuzzy Entropy based Feature Selection and Classification Framework
The optimized framework was also compared with other six classical filter-based feature selection methods.
A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets
In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets.
Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation
This method is applicable to aggregate only the active (or reactive) power, and the joint active-reactive power domain.
DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation
In comparison with ResNet-based, DenseNet-based and attention network (AttnNet) based methods within the same encoder-decoder network structure, our method achieves significantly higher segmentation accuracy with fewer number of model parameters than DenseNet and AttnNet.
FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation.
A Spatially Constrained Deep Convolutional Neural Network for Nerve Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate Annotations
Most state-of-the-art deep learning methods require a large number of accurately annotated examples for model training.
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal.
A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN).
Triad State Space Construction for Chaotic Signal Classification with Deep Learning
Inspired by the well-known permutation entropy (PE), an effective image encoding scheme for chaotic time series, Triad State Space Construction (TSSC), is proposed.
Circumventing Outliers of AutoAugment with Knowledge Distillation
AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization.
Ranked #172 on
Image Classification
on ImageNet
Online Residential Demand Response via Contextual Multi-Armed Bandits
Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs.
Biased Stochastic Gradient Descent for Conditional Stochastic Optimization
Conditional Stochastic Optimization (CSO) covers a variety of applications ranging from meta-learning and causal inference to invariant learning.
Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification
Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif difference field (MDF) is proposed.
Latency-Aware Differentiable Neural Architecture Search
However, these methods suffer the difficulty in optimizing network, so that the searched network is often unfriendly to hardware.
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks.
MM Algorithms for Distance Covariance based Sufficient Dimension Reduction and Sufficient Variable Selection
Sufficient dimension reduction (SDR) using distance covariance (DCOV) was recently proposed as an approach to dimension-reduction problems.
Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters
Our approach bridges the gap from two aspects, namely, amending the estimation on the architectural gradients, and unifying the hyper-parameter settings in the search and re-training stages.
Anion charge-lattice volume dependent Li ion migration in compounds with the face-centered cubic anion frameworks
In this work, the face-centered cubic (fcc) anion frameworks were creatively constructed to study the effects of anion charge and lattice volume on the stability of lithium ion occupation and lithium ion migration.
Applied Physics
Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
The extensive experiments and ablation study demonstrate the effectiveness of our approach on the domain adaptive segmentation task.
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks.
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture.
Ranked #20 on
Neural Architecture Search
on CIFAR-10
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis
In addition, we provide a fundamental limit of the dynamic regret for any online algorithms by considering linear quadratic tracking problems.
Optimization and Control
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i. e., as long as they ensure that the neural networks are Lipschitz continuous.
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization
In this paper, we study a class of stochastic optimization problems, referred to as the \emph{Conditional Stochastic Optimization} (CSO), in the form of $\min_{x \in \mathcal{X}} \EE_{\xi}f_\xi\Big({\EE_{\eta|\xi}[g_\eta(x,\xi)]}\Big)$, which finds a wide spectrum of applications including portfolio selection, reinforcement learning, robust learning, causal inference and so on.
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search.
Attention Distillation for Learning Video Representations
To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition.
Ranked #36 on
Action Recognition
on UCF101
TightCap: 3D Human Shape Capture with Clothing Tightness Field
In this paper, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single 3D human scan, which enables numerous applications such as virtual try-on, biometrics and body evaluation.
Singing voice conversion with non-parallel data
F0 and aperiodic are obtained through the original singing voice, and used with acoustic features to reconstruct the target singing voice through a vocoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
BandNet: A Neural Network-based, Multi-Instrument Beatles-Style MIDI Music Composition Machine
In the learning stage, a sequence of stylistically uniform, multiple-channel music samples was modeled by a RNN.
Unsupervised Domain Adaptation using Generative Models and Self-ensembling
The results suggest that selfensembling is better than simple data augmentation with the newly generated data and a single model trained this way can have the best performance across all different transfer tasks.
Beyond "How may I help you?": Assisting Customer Service Agents with Proactive Responses
We study the problem of providing recommended responses to customer service agents in live-chat dialogue systems.
2PFPCE: Two-Phase Filter Pruning Based on Conditional Entropy
To overcome this challenge, we propose a novel filter-pruning framework, two-phase filter pruning based on conditional entropy, namely \textit{2PFPCE}, to compress the CNN models and reduce the inference time with marginal performance degradation.
Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play
Mobile network that millions of people use every day is one of the most complex systems in the world.
A Novel Co-design Peta-scale Heterogeneous Cluster for Deep Learning Training
Therefore, MiMatrix intrinsically solves the bandwidth bottleneck of central node in parameter server framework that is widely used in distributed DL tasks.