Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Label Noise-Resistant Mean Teaching for Weakly Supervised Fake News Detection
Fake news spreads at an unprecedented speed, reaches global audiences and poses huge risks to users and communities. Most existing fake news detection algorithms focus on building supervised training models on a large am…
Fake News DetectionModel OptimizationMedical Image Registration via Neural Fields
Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity be…
Image RegistrationMedical Image AnalysisMedical Image RegistrationModel OptimizationTowards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device. While prior FL optimizations tackled the data and system heterogeneity chall…
FairnessFederated LearningModel OptimizationRecommendation SystemsMulti-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware
The majority of IoT devices like smartwatches, smart plugs, HVAC controllers, etc., are powered by hardware with a constrained specification (low memory, clock speed and processor) which is insufficient to accommodate an…
Anomaly DetectionModel OptimizationA Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimation
One of the most important problems in system identification and statistics is how to estimate the unknown parameters of a given model. Optimization methods and specialized procedures, such as Empirical Minimization (EM) …
Model Optimizationparameter estimationCollision-Free Navigation using Evolutionary Symmetrical Neural Networks
Collision avoidance systems play a vital role in reducing the number of vehicle accidents and saving human lives. This paper extends the previous work using evolutionary neural networks for reactive collision avoidance. …
Collision AvoidanceModel OptimizationFederated Self-Supervised Learning for Acoustic Event Classification
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling framework that decouples data collection a…
ClassificationContinual LearningFederated LearningModel Optimization+1GRAND+: Scalable Graph Random Neural Networks
Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-of-the-art performance for this pro…
Data AugmentationGraph LearningModel OptimizationNode ClassificationDeep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss
Accurate emotion classification for online reviews is vital for business organizations to gain deeper insights into markets. Although deep learning has been successfully implemented in this area, accuracy and processing …
ClassificationEmotion ClassificationModel OptimizationAdaptive Discriminative Regularization for Visual Classification
How to improve discriminative feature learning is central in classification. Existing works address this problem by explicitly increasing inter-class separability and intra-class similarity, whether by constructing posit…
ClassificationContrastive LearningDecision MakingModel OptimizationPUMA: Performance Unchanged Model Augmentation for Training Data Removal
Preserving the performance of a trained model while removing unique characteristics of marked training data points is challenging. Recent research usually suggests retraining a model from scratch with remaining training …
Model OptimizationParticle-based Fast Jet Simulation at the LHC with Variational Autoencoders
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet …
Model OptimizationReliable Inlier Evaluation for Unsupervised Point Cloud Registration
Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a…
Model OptimizationPoint Cloud RegistrationReal-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion
Recently, segmentation-based scene text detection methods have drawn extensive attention in the scene text detection field, because of their superiority in detecting the text instances of arbitrary shapes and extreme asp…
BinarizationModel OptimizationScene Text DetectionSegmentation+1Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks
Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protecti…
Cloud ComputingDecision MakingDeep LearningModel Optimization+1Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition
In this work, we aim to enhance the system robustness of end-to-end automatic speech recognition (ASR) against adversarially-noisy speech examples. We focus on a rigorous and empirical "closed-model adversarial robustnes…
Adversarial RobustnessAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)Model Optimization+2Anomalib: A Deep Learning Library for Anomaly Detection
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set…
Anomaly DetectionDeep LearningModel OptimizationUnsupervised Anomaly DetectionRECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achi…
BenchmarkingModel OptimizationIntent Contrastive Learning for Sequential Recommendation
Users' interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.).However, users' underlying intents are often unobserved/latent, making it challenging…
Contrastive LearningModel OptimizationRepresentation LearningSelf-Supervised Learning+1Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)
While neural networks have advanced the frontiers in many machine learning applications, they often come at a high computational cost. Reducing the power and latency of neural network inference is vital to integrating mo…
Model OptimizationQuantization