Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion
Neural network (NN)-based Digital Predistortion (DPD) stands out in improving signal quality in wideband radio frequency (RF) power amplifiers (PAs) employing complex modulation. However, NN DPDs usually rely on a large …
Model OptimizationQuantizationFedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model
Federated learning(FL) is used for distributed scenarios to train artificial intelligence(AI) models while ensuring users' privacy. In federated learning scenario, the server generally never knows about users' data. This…
Brain Tumor SegmentationFederated LearningModel OptimizationTransfer LearningHybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings
Arabic dialect recognition presents a significant challenge in speech technology due to the linguistic diversity of Arabic and the scarcity of large annotated datasets, particularly for underrepresented dialects. This re…
Model OptimizationSelf-Supervised LearningG$^{2}$D: Boosting Multimodal Learning with Gradient-Guided Distillation
Multimodal learning aims to leverage information from diverse data modalities to achieve more comprehensive performance. However, conventional multimodal models often suffer from modality imbalance, where one or a few mo…
Knowledge DistillationModel OptimizationEnterprise Large Language Model Evaluation Benchmark
Large Language Models (LLMs) ) have demonstrated promise in boosting productivity across AI-powered tools, yet existing benchmarks like Massive Multitask Language Understanding (MMLU) inadequately assess enterprise-speci…
Language Model EvaluationLanguage ModelingLanguage ModellingLarge Language Model+4From Tiny Machine Learning to Tiny Deep Learning: A Survey
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). Whil…
AutoMLModel OptimizationNeural Architecture SearchQuantization+1Intelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency
This study explores the effectiveness of predictive maintenance models and the optimization of intelligent Operation and Maintenance (O&M) systems in improving wind power generation efficiency. Through qualitative resear…
Data IntegrationModel OptimizationBotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
Transferring extensive knowledge from relevant social networks has emerged as a promising solution to overcome label scarcity in detecting social bots and other anomalies with GNN-based models. However, effective transfe…
Domain AdaptationGRAPH DOMAIN ADAPTATIONModel OptimizationTransfer LearningGFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO
The ability to train high-performing reward models with few-shot data is critical for enhancing the efficiency and scalability of Reinforcement Learning from Human Feedback (RLHF). We propose a data augmentation and expa…
Data AugmentationModel OptimizationA Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions
Vietnamese document analysis and recognition (DAR) is a crucial field with applications in digitization, information retrieval, and automation. Despite advancements in OCR and NLP, Vietnamese text recognition faces uniqu…
Computational Efficiencydocument understandingDomain AdaptationInformation Retrieval+2RewardAnything: Generalizable Principle-Following Reward Models
Reward Models, essential for guiding Large Language Model optimization, are typically trained on fixed preference datasets, resulting in rigid alignment to single, implicit preference distributions. This prevents adaptat…
Instruction FollowingLarge Language ModelModel OptimizationMulti-Modal Learning with Bayesian-Oriented Gradient Calibration
Multi-Modal Learning (MML) integrates information from diverse modalities to improve predictive accuracy. However, existing methods mainly aggregate gradients with fixed weights and treat all dimensions equally, overlook…
Model OptimizationBEDI: A Comprehensive Benchmark for Evaluating Embodied Agents on UAVs
With the rapid advancement of low-altitude remote sensing and Vision-Language Models (VLMs), Embodied Agents based on Unmanned Aerial Vehicles (UAVs) have shown significant potential in autonomous tasks. However, current…
Model OptimizationTask PlanningEvaluating Large Language Model with Knowledge Oriented Language Specific Simple Question Answering
We introduce KoLasSimpleQA, the first benchmark evaluating the multilingual factual ability of Large Language Models (LLMs). Inspired by existing research, we created the question set with features such as single knowled…
Global FactsLanguage ModelingLanguage ModellingLarge Language Model+2MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets
The data sparsity problem significantly hinders the performance of recommender systems, as traditional models rely on limited historical interactions to learn user preferences and item properties. While incorporating mul…
Model OptimizationMultimodal RecommendationRecommendation SystemsR&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large language models and multi-agent systems, …
Code GenerationModel OptimizationLearning to Insert for Constructive Neural Vehicle Routing Solver
Neural Combinatorial Optimisation (NCO) is a promising learning-based approach for solving Vehicle Routing Problems (VRPs) without extensive manual design. While existing constructive NCO methods typically follow an appe…
Model OptimizationPositionvalidBridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking
In the realm of information retrieval, users often engage in multi-turn interactions with search engines to acquire information, leading to the formation of sequences of user feedback behaviors. Leveraging the session co…
Document RankingInformation RetrievalKnowledge DistillationModel OptimizationMSDformer: Multi-scale Discrete Transformer For Time Series Generation
Discrete Token Modeling (DTM), which employs vector quantization techniques, has demonstrated remarkable success in modeling non-natural language modalities, particularly in time series generation. While our prior work S…
Model OptimizationTime SeriesTime Series GenerationSurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis
Survival analysis, which estimates the probability of event occurrence over time from censored data, is fundamental in numerous real-world applications, particularly in high-stakes domains such as healthcare and risk ass…
BenchmarkingModel OptimizationSurvival AnalysisUncertainty Quantification