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Found 24 papers, 7 papers with code
Federated PCA on Grassmann Manifold for IoT Anomaly Detection
Experimental results on the UNSW-NB15 and TON-IoT datasets show that our proposed methods offer performance in anomaly detection comparable to nonlinear baselines, while providing significant improvements in communication and memory efficiency, underscoring their potential for securing IoT networks.
Search Intenion Network for Personalized Query Auto-Completion in E-Commerce
Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions. Today's QAC systems in real-world scenarios face two major challenges:1)intention equivocality(IE): during the user's typing process, the prefix often contains a combination of characters and subwords, which makes the current intention ambiguous and difficult to model. 2)intention transfer (IT):previous works make personalized recommendations based on users' historical sequences, but ignore the search intention transfer. However, the current intention extracted from prefix may be contrary to the historical preferences.
Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization
Federated Learning (FL) has emerged as a groundbreaking distributed learning paradigm enabling clients to train a global model collaboratively without exchanging data.
Bridging the Gap: Sketch-Aware Interpolation Network for High-Quality Animation Sketch Inbetweening
Hand-drawn 2D animation workflow is typically initiated with the creation of sketch keyframes.
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge.
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder
Latest federated learning (FL) methods started to focus on how to use unlabeled data in clients for training due to users' privacy concerns, high labeling costs, or lack of expertise.
FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysis
Most existing federated learning methods assume that clients have fully labeled data to train on, while in reality, it is hard for the clients to get task-specific labels due to users' privacy concerns, high labeling costs, or lack of expertise.
Random Padding Data Augmentation
Random Padding is parameter-free, simple to construct, and compatible with the majority of CNN-based recognition models.
Holistic Label Correction for Noisy Multi-Label Classification
The issues are, that we do not understand why label dependence is helpful in the problem, and how to learn and utilize label dependence only using training data with noisy multiple labels.
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks
To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.
Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks
In this paper, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration.
Understanding Partial Multi-Label Learning via Mutual Information
To deal with ambiguities in partial multilabel learning (PML), state-of-the-art methods perform disambiguation by identifying ground-truth labels directly.
Fast Multi-label Learning
The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process.
Boosting ship detection in SAR images with complementary pretraining techniques
In this paper, to resolve the problem of inconsistent imaging perspective between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique, which transfers the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset.
The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches from Google Earth images.
A Comprehensive Evaluation Framework for Deep Model Robustness
To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i. e., data and model).
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning.
Will\_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy
Mixing languages are widely used in social media, especially in multilingual societies like India.
Will\_Go at SemEval-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on BERT
Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years.
Federated Learning with Nesterov Accelerated Gradient
It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far.
Online Metric Learning for Multi-Label Classification
Specifically, in order to learn the new $k$NN-based metric, we first project instances in the training dataset into the label space, which make it possible for the comparisons of instances and labels in the same dimension.
An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on Bert
Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years.
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.
