Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Enhancing supply chain security with automated machine learning
The increasing scale and complexity of global supply chains have led to new challenges spanning various fields, such as supply chain disruptions due to long waiting lines at the ports, material shortages, and inflation. …
feature selectionFraud DetectionHyperparameter OptimizationModel OptimizationOn Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning
Recent advancements in language and vision assistants have showcased impressive capabilities but suffer from a lack of transparency, limiting broader research and reproducibility. While open-source models handle general …
Document AIModel OptimizationNatural Language UnderstandingExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking
Large language models (LLM) have recently attracted significant attention in the field of artificial intelligence. However, the training process of these models poses significant challenges in terms of computational and …
Model OptimizationQuantizationFlextron: Many-in-One Flexible Large Language Model
Training modern LLMs is extremely resource intensive, and customizing them for various deployment scenarios characterized by limited compute and memory resources through repeated training is impractical. In this paper, w…
Language ModelingLanguage ModellingLarge Language Modelmodel+1Learning Physical Simulation with Message Passing Transformer
Machine learning methods for physical simulation have achieved significant success in recent years. We propose a new universal architecture based on Graph Neural Network, the Message Passing Transformer, which incorporat…
AttributeDecoderGraph Neural NetworkModel OptimizationOn the Limitations of Fractal Dimension as a Measure of Generalization
Bounding and predicting the generalization gap of overparameterized neural networks remains a central open problem in theoretical machine learning. There is a recent and growing body of literature that proposes the frame…
Generalization BoundsLearning TheoryModel OptimizationTopological Data AnalysisFlexible and Efficient Surrogate Gradient Modeling with Forward Gradient Injection
Automatic differentiation is a key feature of present deep learning frameworks. Moreover, they typically provide various ways to specify custom gradients within the computation graph, which is of particular importance fo…
Model OptimizationDiverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce. However, real-world unlabeled data often contain unseen-class samples, which can …
Model OptimizationSIAVC: Semi-Supervised Framework for Industrial Accident Video Classification
Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial a…
Fire DetectionModel OptimizationPseudo LabelVideo ClassificationEnhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data
In this research, we introduce an innovative method for synthesizing medical images using generative adversarial networks (GANs). Our proposed GANs method demonstrates the capability to produce realistic synthetic images…
Model OptimizationDEMO: A Statistical Perspective for Efficient Image-Text Matching
Image-text matching has been a long-standing problem, which seeks to connect vision and language through semantic understanding. Due to the capability to manage large-scale raw data, unsupervised hashing-based approaches…
Image-text matchingModel OptimizationSemantic SimilaritySemantic Textual Similarity+1Double Correction Framework for Denoising Recommendation
As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in real-world recommendation scenarios (suc…
DenoisingModel OptimizationRecommendation SystemsHunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transformer structure, text encoder, and positi…
Image GenerationLanguage ModelingLanguage ModellingLarge Language Model+2AquaIntellect: A Semantic Self-learning Framework for Underwater Internet of Things Connectivity
The emerging paradigm of Non-Conventional Internet of Things (NC IoT), which is focused on the usefulness of information as opposed to the notion of high volume data collection and transmission, will be an important and …
Model OptimizationSelf-LearningMRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial MRI scans from the UK Biobank, 221 in-ho…
Model OptimizationOrgan SegmentationSegmentationTransfer LearningAutomating Code Adaptation for MLOps -- A Benchmarking Study on LLMs
This paper explores the possibilities of the current generation of Large Language Models for incorporating Machine Learning Operations (MLOps) functionalities into ML training code bases. We evaluate the performance of O…
BenchmarkingHyperparameter OptimizationModel OptimizationTranslationNeRF-Guided Unsupervised Learning of RGB-D Registration
This paper focuses on training a robust RGB-D registration model without ground-truth pose supervision. Existing methods usually adopt a pairwise training strategy based on differentiable rendering, which enforces the ph…
Model OptimizationNeRFParameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications
The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging, primarily through the adaptation of pre-trained models for specific tasks. Tr…
Computational EfficiencyModel Optimizationparameter-efficient fine-tuningSpeech Synthesis+1Proteus: Preserving Model Confidentiality during Graph Optimizations
Deep learning (DL) models have revolutionized numerous domains, yet optimizing them for computational efficiency remains a challenging endeavor. Development of new DL models typically involves two parties: the model deve…
Computational EfficiencymodelModel OptimizationTMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models
Diffusion models have emerged as preeminent contenders in the realm of generative models. Distinguished by their distinctive sequential generative processes, characterized by hundreds or even thousands of timesteps, diff…
DenoisingModel OptimizationQuantization