Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
An empirical investigation into audio pipeline approaches for classifying bird species
This paper is an investigation into aspects of an audio classification pipeline that will be appropriate for the monitoring of bird species on edges devices. These aspects include transfer learning, data augmentation and…
Audio ClassificationClassificationData AugmentationModel Optimization+1Few-Shot Charge Prediction with Multi-Grained Features and MutualInformation
“Charge prediction aims to predict the final charge for a case according to its fact descriptionand plays an important role in legal assistance systems. With deep learning based methods prediction on high-frequency charg…
Model OptimizationPredictionUsing deep learning to detect patients at risk for prostate cancer despite benign biopsies
Background: Transrectal ultrasound guided systematic biopsies of the prostate is a routine procedure to establish a prostate cancer diagnosis. However, the 10-12 prostate core biopsies only sample a relatively small volu…
Hyperparameter OptimizationModel OptimizationModel SelectionSpecificity+1Multi-objective Asynchronous Successive Halving
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e.g., accuracy) of machine learning models. However, in a plethora of real-world applications, accuracy is only one…
FairnessHyperparameter OptimizationLanguage ModelingLanguage Modelling+2Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Medical imaging plays a pivotal role in diagnosis and treatment in clinical practice. Inspired by the significant progress in automatic image captioning, various deep learning (DL)-based methods have been proposed to gen…
Decision MakingImage CaptioningModel OptimizationSentenceMulti-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
Bid optimization for online advertising from single advertiser's perspective has been thoroughly investigated in both academic research and industrial practice. However, existing work typically assume competitors do not …
Model OptimizationMulti-agent Reinforcement LearningAffinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic s…
Box-supervised Instance SegmentationGraph Neural NetworkInstance SegmentationModel Optimization+4Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations
Partial differential equations (PDEs) that fit scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects, such as physics and finance. The data-driven discovery …
Density EstimationModel OptimizationA keyword-driven approach to science
To a good extent, words can be understood as corresponding to patterns or categories that appeared in order to represent concepts and structures that are particularly important or useful in a given time and space. Words …
Model OptimizationCMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
Malicious applications of deepfakes (i.e., technologies generating target facial attributes or entire faces from facial images) have posed a huge threat to individuals' reputation and security. To mitigate these threats,…
Adversarial AttackFace SwappingModel OptimizationBatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
As the applications of deep learning models on edge devices increase at an accelerating pace, fast adaptation to various scenarios with varying resource constraints has become a crucial aspect of model deployment. As a r…
AllGPUHardware Aware Neural Architecture SearchModel Optimization+2Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization
The Foreign Exchange (Forex) is a large decentralized market, on which trading analysis and algorithmic trading are popular. Research efforts have been focusing on proof of efficiency of certain technical indicators. We …
Algorithmic TradingModel OptimizationFedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework
With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. To improve the performance of the …
Federated LearningModel OptimizationA Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4.0 on the edge computing level. FL solves the dilemma in which enterprise…
Edge-computingFederated LearningModel OptimizationNeural Process for Black-Box Model Optimization Under Bayesian Framework
There are a large number of optimization problems in physical models where the relationships between model parameters and outputs are unknown or hard to track. These models are named as black-box models in general becaus…
Bayesian OptimizationModel OptimizationLow-Fidelity End-to-End Video Encoder Pre-training for Temporal Action Localization
Temporal action localization (TAL) is a fundamental yet challenging task in video understanding. Existing TAL methods rely on pre-training a video encoder through action classification supervision. This results in a task…
Action ClassificationAction LocalizationGPUModel Optimization+3Model Complexity of Deep Learning: A Survey
Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized …
Deep LearningmodelModel OptimizationModel Selection+1Few-Shot Graph Learning for Molecular Property Prediction
The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery. The existing deep neural network methods usually require large training da…
AttributeDrug DiscoveryGraph LearningGraph Neural Network+7DEUP: Direct Epistemic Uncertainty Prediction
Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence. While existing work focuses on using the variance of the Bayesian posterior due to parameter uncertainty as a …
Active Learningimage-classificationImage ClassificationModel Optimization+2Video Analytics on IoT devices
Deep Learning (DL) combined with advanced model optimization methods such as RC-NN and Edge2Train has enabled offline execution of large networks on the IoT devices. In this paper, we compare the modern Deep Learning (DL…
Deep LearningModel Optimization