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Found 104 papers, 35 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。”
FedRel: 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.
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
We evaluate the performance of the proposed model on three tasks, including code question answering, code clone detection and code refinement.
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.
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 propose POLAR, a \textbf{pol}ynomial \textbf{ar}ithmetic framework that leverages polynomial overapproximations with interval remainders for 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 #6 on
Visual Object Tracking
on LaSOT
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.
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.
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.
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.
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.
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.
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-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.
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).
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 #4 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.
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.
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.
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.
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.
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.
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.
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 #97 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 #15 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 #34 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.
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.
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition
A major emerging challenge is how to protect people's privacy as cameras and computer vision are increasingly integrated into our daily lives, including in smart devices inside homes.
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation
In this paper, we propose a novel two-step understanding method, namely Salient Relevance (SR) map, which aims to shed light on how deep CNNs recognize images and learn features from areas, referred to as attention areas, therein.
ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene
Arbitrary-oriented text detection in the wild is a very challenging task, due to the aspect ratio, scale, orientation, and illumination variations.
Sparse Photometric 3D Face Reconstruction Guided by Morphable Models
We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image.
Deep Neural Network Capacity
However, the capacity of deep neural network architecture is still a mystery to the researchers.
Detecting Small Signs from Large Images
Experimental results on a real-world conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.
ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition
In this paper, we introduce a new and challenging large-scale food image dataset called "ChineseFoodNet", which aims to automatically recognizing pictured Chinese dishes.
Motion Saliency Based Automatic Delineation of Glottis Contour in High-speed Digital Images
In recent years, high-speed videoendoscopy (HSV) has significantly aided the diagnosis of voice pathologies and furthered the understanding the voice production in recent years.
The similarity metric
A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied.
