Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Robust Unsupervised Fault Diagnosis For High-Dimensional Nonlinear Noisy Data
Traditional fault diagnosis methods struggle to handle fault data, with complex data characteristics such as high dimensions and large noise. Deep learning is a promising solution, which typically works well only when la…
DiagnosticDimensionality ReductionFault DiagnosisModel OptimizationTemporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks
Spiking Neural Networks (SNNs), inspired by the human brain, offer significant computational efficiency through discrete spike-based information transfer. Despite their potential to reduce inference energy consumption, a…
Computational EfficiencyKnowledge DistillationModel OptimizationGroup Relative Policy Optimization for Image Captioning
Image captioning tasks usually use two-stage training to complete model optimization. The first stage uses cross-entropy as the loss function for optimization, and the second stage uses self-critical sequence training (S…
DiversityImage CaptioningModel OptimizationGradient-Guided Annealing for Domain Generalization
Domain Generalization (DG) research has gained considerable traction as of late, since the ability to generalize to unseen data distributions is a requirement that eludes even state-of-the-art training algorithms. In thi…
Domain Generalizationimage-classificationImage ClassificationModel OptimizationAdvanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications
This study presents a comparative analysis of deep learning methodologies such as BERT, FinBERT and ULMFiT for sentiment analysis of earnings call transcripts. The objective is to investigate how Natural Language Process…
Computational EfficiencyDecision MakingManagementModel Optimization+1Scaling LLM Pre-training with Vocabulary Curriculum
Modern language models rely on static vocabularies, fixed before pretraining, in contrast to the adaptive vocabulary acquisition observed in human language learning. To bridge this gap, we introduce vocabulary curriculum…
Model OptimizationVertical Federated Continual Learning via Evolving Prototype Knowledge
Vertical Federated Learning (VFL) has garnered significant attention as a privacy-preserving machine learning framework for sample-aligned feature federation. However, traditional VFL approaches do not address the challe…
Continual LearningFederated LearningModel OptimizationPrivacy Preserving+1Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods employ data valuation to discern high-qu…
Data ValuationFairnessModel OptimizationRecommendation SystemsSycEval: Evaluating LLM Sycophancy
Large language models (LLMs) are increasingly applied in educational, clinical, and professional settings, but their tendency for sycophancy -- prioritizing user agreement over independent reasoning -- poses risks to rel…
Model OptimizationSemiHMER: Semi-supervised Handwritten Mathematical Expression Recognition using pseudo-labels
In recent years, deep learning with Convolutional Neural Networks (CNNs) has achieved remarkable results in the field of HMER (Handwritten Mathematical Expression Recognition). However, it remains challenging to improve …
DecoderModel OptimizationPseudo LabelA Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame Prediction
Recent advances in video generation models demonstrate their potential as world simulators, but they often struggle with videos deviating from physical laws, a key concern overlooked by most text-to-video benchmarks. We …
Model OptimizationOptical Flow EstimationVideo GenerationTrust-Aware Diversion for Data-Effective Distillation
Dataset distillation compresses a large dataset into a small synthetic subset that retains essential information. Existing methods assume that all samples are perfectly labeled, limiting their real-world applications whe…
Dataset DistillationModel OptimizationOn-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile Devices
We present On-device Sora, the first model training-free solution for diffusion-based on-device text-to-video generation that operates efficiently on smartphone-grade devices. To address the challenges of diffusion-based…
DenoisingModel OptimizationText-to-Video GenerationVideo GenerationEdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection
This paper introduces EdgeMLOps, a framework leveraging Cumulocity IoT and thin-edge.io for deploying and managing machine learning models on resource-constrained edge devices. We address the challenges of model optimiza…
Asset ManagementManagementModel OptimizationQuantization+1MME-Industry: A Cross-Industry Multimodal Evaluation Benchmark
With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain l…
MMEModel OptimizationOptical Character Recognition (OCR)Framework for Progressive Knowledge Fusion in Large Language Models Through Structured Conceptual Redundancy Analysis
The organization of latent knowledge within large-scale models poses unique challenges when addressing overlapping representations and optimizing contextual accuracy. Conceptual redundancies embedded across layers often …
Adversarial RobustnessModel OptimizationConsolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?
The evolving requirements of Internet of Things (IoT) applications are driving an increasing shift toward bringing intelligence to the edge, enabling real-time insights and decision-making within resource-constrained env…
Code GenerationModel OptimizationEnhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach
A Large Language Model (LLM) tends to generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt. To achieve…
Large Language ModelModel EditingModel OptimizationEvolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such knowledge through the incorporation of ma…
Evolutionary AlgorithmsModel OptimizationSurveyBeyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers, providing enhanced reliability and red…
Distributed ComputingFederated LearningModel OptimizationTransfer Learning