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Found 74 papers, 10 papers with code
ActPerFL: Active Personalized Federated Learning
Inspired by Bayesian hierarchical models, we develop ActPerFL, a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients’ training.
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain.
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle
Therefore, guaranteeing the robustness of hard examples is crucial for improving the final robustness of the model.
Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization
Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems and have received a lot of attention in the past decades.
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
In this paper, we empirically prove that this problem is associated with the mixup of the activation values between less discriminative foreground regions and the background.
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition
A dynamic pointer network (DYPN) is introduced as the TSP solver, which takes city locations as inputs and immediately outputs a permutation of nodes.
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer
The model is trained using a deep reinforcement learning method offline, and is used to optimize the UAV routing problem online.
Self-Aware Personalized Federated Learning
In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned.
SuperMVS: Non-Uniform Cost Volume For High-Resolution Multi-View Stereo
In this paper, we propose a free-moving hypothesis plane method for dynamic and non-uniform sampling in a wide depth range to build the cost volume, which not only greatly reduces the number of planes but also finers sampling, for both of reducing computational cost and improving accuracy, named Non-Uniform Cost Volume.
Leveraging Search History for Improving Person-Job Fit
As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates.
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation
In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation.
Ranked #59 on
Instance Segmentation
on COCO test-dev
RRL:Regional Rotation Layer in Convolutional Neural Networks
Known solutions include the enhancement of training data and the increase of rotation invariance by globally merging the rotation equivariant features.
Federated Learning Challenges and Opportunities: An Outlook
Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs.
End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study
This paper presents a novel framework and a distributed algorithm that can enable ANs and CNs to autonomously "cooperate" with each other to dynamically negotiate their local QoS budgets and to collectively meet E2E QoS goals by sharing only their estimates of the global constraint functions, without disclosing their local decision variables.
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Further, we propose Scale-Invariant (SI) adversarial defense mechanism based on the cosine angle matrix, which can be embedded into the popular adversarial defenses.
Bayesian Promised Persuasion: Dynamic Forward-Looking Multiagent Delegation with Informational Burning
A revelation-principle-like design regime is established to show that the persuasion with belief hierarchies can be fully characterized by correlating the randomization of the agents' local BPD mechanisms with the persuasion as a direct recommendation of the future promises.
End-to-end Alexa Device Arbitration
We introduce a variant of the speaker localization problem, which we call device arbitration.
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack
However, the defense methods that have good effect in natural images may not be suitable for medical diagnostic tasks.
Pre-training Graph Neural Network for Cross Domain Recommendation
Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain.
FedProc: Prototypical Contrastive Federated Learning on Non-IID data
In this paper, we propose FedProc: prototypical contrastive federated learning, which is a simple and effective federated learning framework.
M3D-VTON: A Monocular-to-3D Virtual Try-On Network
Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Informational Design of Dynamic Multi-Agent System
We propose a direct information design approach that incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors.
Low Pass Filter for Anti-aliasing in Temporal Action Localization
This paper aims to verify the existence of aliasing in TAL methods and investigate utilizing low pass filters to solve this problem by inhibiting the high-frequency band.
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images.
GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS
Object detection is widely used on embedded devices.
On the Equilibrium Elicitation of Markov Games Through Information Design
An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the designer, such that the design process avoids the predictions of the agents' strategic selection behaviors.
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems
This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP).
Hyperspectral Image Denoising With Realistic Data
On the other hand, we propose an accurate HSI noise model which matches the distribution of real data well and can be employed to synthesize realistic dataset.
Generative Partial Visual-Tactile Fused Object Clustering
More specifically, we first do partial visual and tactile features extraction from the partial visual and tactile data, respectively, and encode the extracted features in modality-specific feature subspaces.
Improving the Certified Robustness of Neural Networks via Consistency Regularization
A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to be effective to train neural networks that are certifiably robust to the attacker.
On Front-end Gain Invariant Modeling for Wake Word Spotting
Since the WW model is trained with the AFE-processed audio data, its performance is sensitive to AFE variations, such as gain changes.
Investigating Constraint Relationship in Evolutionary Many-Constraint Optimization
This paper contributes to the treatment of extensive constraints in evolutionary many-constraint optimization through consideration of the relationships between pair-wise constraints.
Correlated Differential Privacy: Feature Selection in Machine Learning
In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover, the privacy issue of data correlation in learning is guaranteed.
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data.
Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination
A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.
Fairness Constraints in Semi-supervised Learning
Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.
Re-weighting and 1-Point RANSAC-Based PnP Solution to Handle Outliers
We propose a fast PnP solution named R1PPnP to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme.
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture
Aiming at channel compression, a novel convolutional construction named compact convolution is proposed to embrace the progress in spatial convolution, channel grouping and pooling operation.
MIE: A Medical Information Extractor towards Medical Dialogues
Electronic Medical Records (EMRs) have become key components of modern medical care systems.
Evolving Metric Learning for Incremental and Decremental Features
Online metric learning has been widely exploited for large-scale data classification due to the low computational cost.
Bootstrap inference for quantile-based modal regression
Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by smoothing the linear quantile regression estimator, and develop two bootstrap methods, a novel pivotal bootstrap and the nonparametric bootstrap, for our conditional mode estimator.
Statistics Theory Methodology Statistics Theory
kDecay: Just adding k-decay items on Learning-Rate Schedule to improve Neural Networks
On the ImageNet, accuracy is improved by 1. 25\%.
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing
Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.
Adaptive Trajectory Estimation with Power Limited Steering Model under Perturbation Compensation
The experiment of trajectory estimation demonstrates the convergence of AdaTE, and the better robust to the biased prior statistics and the observation drift compared with EKF, UKF and sparse MAP.
Information Theory Robotics Systems and Control Systems and Control Information Theory G.3.13; J.2.7
Visual Tactile Fusion Object Clustering
To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering.
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks
As a further optimization, we propose a density-adaptive regular-block (DARB) pruning that outperforms prior structured pruning work with high pruning ratio and decoding efficiency.
Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network
Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match.
Hyperspectral Image Reconstruction Using Deep External and Internal Learning
To solve the low spatial and/or temporal resolution problem which the conventional hypelrspectral cameras often suffer from, coded snapshot hyperspectral imaging systems have attracted more attention recently.
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning.
Ranked #3 on
Link Prediction
on FB15k
(MR metric)
"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer
So we propose a two step approach "Mask and Infill".
Mixed-Supervised Dual-Network for Medical Image Segmentation
The model is trained jointly in a multi-task learning setting.
FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
Most current image super-resolution (SR) methods based on convolutional neural networks (CNNs) use residual learning in network structural design, which favors to effective back propagation and hence improves SR performance by increasing model scale.
ARMIN: Towards a More Efficient and Light-weight Recurrent Memory Network
In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks.
Data-Driven Machine Learning Techniques for Self-healing in Cellular Wireless Networks: Challenges and Solutions
As an important component in SON, self-healing is defined as a network paradigm where the faults of target networks are mitigated or recovered by automatically triggering a series of actions such as detection, diagnosis and compensation.
Deep Reinforcement Learning for Multi-objective Optimization
The solutions can be directly obtained by a simple forward calculation of the neural network; thereby, no iteration is required and the MOP can be always solved in a reasonable time.
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
The task-specific classifiers are required to be homogeneous-structured to ease the parameter prediction, so the meta-learning approaches could only handle few-shot learning problems where the tasks share a uniform number of classes.
Imbalanced Sentiment Classification Enhanced with Discourse Marker
Imbalanced data commonly exists in real world, espacially in sentiment-related corpus, making it difficult to train a classifier to distinguish latent sentiment in text data.
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction
Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI).
Acceleration of the NVT-flash calculation for multicomponent mixtures using deep neural network models
Phase equilibrium calculation, also known as flash calculation, has been extensively applied in petroleum engineering, not only as a standalone application for separation process but also an integral component of compositional reservoir simulation.
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network
Single image super-resolution (SISR), especially that based on deep learning techniques, is an effective and promising alternative technique to improve the current spatial resolution of magnetic resonance (MR) images.
Channel Splitting Network for Single MR Image Super-Resolution
The proposed CSN model divides the hierarchical features into two branches, i. e., residual branch and dense branch, with different information transmissions.
Ranked #3 on
Super-Resolution
on IXI
SEQUENCE MODELLING WITH AUTO-ADDRESSING AND RECURRENT MEMORY INTEGRATING NETWORKS
Processing sequential data with long term dependencies and learn complex transitions are two major challenges in many deep learning applications.
Joint Camera Spectral Sensitivity Selection and Hyperspectral Image Recovery
Hyperspectral image (HSI) recovery from a single RGB image has attracted much attention, whose performance has recently been shown to be sensitive to the camera spectral sensitivity (CSS).
Convolutional Neural Networks based Intra Prediction for HEVC
To overcome this problem, in this paper, an intra prediction convolutional neural network (IPCNN) is proposed for intra prediction, which exploits the rich context of the current block and therefore is capable of improving the accuracy of predicting the current block.
Step-by-step Erasion, One-by-one Collection: A Weakly Supervised Temporal Action Detector
Weakly supervised temporal action detection is a Herculean task in understanding untrimmed videos, since no supervisory signal except the video-level category label is available on training data.
A Survey of Model Compression and Acceleration for Deep Neural Networks
Methods of parameter pruning and quantization are described first, after that the other techniques are introduced.
Limits on Axion Couplings from the first 80-day data of PandaX-II Experiment
We report new searches for the solar axions and galactic axion-like dark matter particles, using the first low-background data from PandaX-II experiment at China Jinping Underground Laboratory, corresponding to a total exposure of about $2. 7\times 10^4$ kg$\cdot$day.
High Energy Physics - Experiment Solar and Stellar Astrophysics High Energy Physics - Phenomenology
A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement Learning
Existing action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational overhead and deviates from the human perception procedure.
Bootstrapping Face Detection with Hard Negative Examples
Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks.
Simulating user learning in authoritative technology adoption: An agent based model for council-led smart meter deployment planning in the UK
In this paper we investigate user learning in authoritative technology adoption by developing an agent-based model using the case of council-led smart meter deployment in the UK City of Leeds.
Modelling Office Energy Consumption: An Agent Based Approach
In this paper, we develop an agent-based model which integrates four important elements, i. e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, based on a case study, to simulate the energy consumption in office buildings.
Dynamic Privacy For Distributed Machine Learning Over Network
Privacy-preserving distributed machine learning becomes increasingly important due to the recent rapid growth of data.
Modelling Electricity Consumption in Office Buildings: An Agent Based Approach
In this paper, we develop an agent-based model which integrates four important elements, i. e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, to simulate the electricity consumption in office buildings.
