Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Transformers are Provably Optimal In-context Estimators for Wireless Communications
Pre-trained transformers exhibit the capability of adapting to new tasks through in-context learning (ICL), where they efficiently utilize a limited set of prompts without explicit model optimization. The canonical commu…
AttributeIn-Context LearningModel OptimizationLLMRec: Large Language Models with Graph Augmentation for Recommendation
The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side …
Model OptimizationRecommendation SystemsDefect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches
Additive manufacturing (AM) is gaining attention across various industries like healthcare, aerospace, and automotive. However, identifying defects early in the AM process can reduce production costs and improve producti…
Defect DetectionModel OptimizationTransfer LearningReward Model Ensembles Help Mitigate Overoptimization
Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning large language models to follow instructions. As part of this process, learned reward models are used to approximately model human …
modelModel OptimizationGraph Regularized and Feature Aware Matrix Factorization for Robust Incomplete Multi-view Clustering
In recent years, many incomplete multi-view clustering methods have been proposed to address the challenging and new clustering task on incomplete multi-view data whose part of view representations are not fully collecte…
ClusteringIncomplete multi-view clusteringModel OptimizationRepresentation LearningA Machine Learning-oriented Survey on Tiny Machine Learning
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based softw…
Model OptimizationSurveyGlobal Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting
Neural networks have revolutionized many empirical fields, yet their application to financial time series forecasting remains controversial. In this study, we demonstrate that the conventional practice of estimating mode…
Deep LearningEconometricsModel OptimizationTime Series+2Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics
Recent face presentation attack detection (PAD) leverages domain adaptation (DA) and domain generalization (DG) techniques to address performance degradation on unknown domains. However, DA-based PAD methods require acce…
counterfactualDomain AdaptationDomain GeneralizationFace Presentation Attack Detection+1FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers from significant performance degradatio…
Continual LearningFederated LearningImage ClassificationKnowledge Distillation+2Quantifying Overfitting: Introducing the Overfitting Index
In the rapidly evolving domain of machine learning, ensuring model generalizability remains a quintessential challenge. Overfitting, where a model exhibits superior performance on training data but falters on unseen data…
Data AugmentationModel OptimizationExploring Annotation-free Image Captioning with Retrieval-augmented Pseudo Sentence Generation
Recently, training an image captioner without annotated image-sentence pairs has gained traction. Previous methods have faced limitations due to either using mismatched corpora for inaccurate pseudo annotations or relyin…
Image CaptioningModel OptimizationRetrievalSentenceFederated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on …
Federated Learningimage-classificationImage ClassificationMedical Image Classification+1One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration
The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially overlapping scenarios. In this paper, we p…
Model OptimizationPoint Cloud RegistrationImproved Distribution Matching for Dataset Condensation
Dataset Condensation aims to condense a large dataset into a smaller one while maintaining its ability to train a well-performing model, thus reducing the storage cost and training effort in deep learning applications. H…
Dataset CondensationModel OptimizationMixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics
We investigate time-optimal Multi-Robot Coverage Path Planning (MCPP) for both unweighted and weighted terrains, which aims to minimize the coverage time, defined as the maximum travel time of all robots. Specifically, w…
Model OptimizationMME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for t…
BenchmarkingLanguage ModelingLanguage ModellingLarge Language Model+4Exploring Robustness of Image Recognition Models on Hardware Accelerators
As the usage of Artificial Intelligence (AI) on resource-intensive and safety-critical tasks increases, a variety of Machine Learning (ML) compilers have been developed, enabling compatibility of Deep Neural Networks (DN…
Image ClassificationModel OptimizationAD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimiza…
Anomaly DetectionModel OptimizationTime SeriesTime Series Anomaly DetectionRedundancy and Concept Analysis for Code-trained Language Models
Code-trained language models have proven to be highly effective for various code intelligence tasks. However, they can be challenging to train and deploy for many software engineering applications due to computational bo…
MemorizationModel CompressionModel OptimizationSentence+2Knowledge-refined Denoising Network for Robust Recommendation
Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods direc…
DenoisingKnowledge-Aware RecommendationModel Optimization