Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
CACER: Clinical Concept Annotations for Cancer Events and Relations
Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationship between cancer drugs and their associ…
Event ExtractionIn-Context LearningModel OptimizationRelation ExtractionReinforcement Learning without Human Feedback for Last Mile Fine-Tuning of Large Language Models
Reinforcement learning is used to align language models with human preference signals after first pre-training the model to predict the next token of text within a large corpus using likelihood maximization. Before being…
Abstractive Text SummarizationImitation LearningModel Optimizationreinforcement-learning+1Super-Resolution works for coastal simulations
Learning fine-scale details of a coastal ocean simulation from a coarse representation is a challenging task. For real-world applications, high-resolution simulations are necessary to advance understanding of many coasta…
Model OptimizationSuper-ResolutionAdaptive Variational Continual Learning via Task-Heuristic Modelling
Variational continual learning (VCL) is a turn-key learning algorithm that has state-of-the-art performance among the best continual learning models. In our work, we explore an extension of the generalized variational co…
Continual LearningModel OptimizationUDD: Dataset Distillation via Mining Underutilized Regions
Dataset distillation synthesizes a small dataset such that a model trained on this set approximates the performance of the original dataset. Recent studies on dataset distillation focused primarily on the design of the o…
Dataset DistillationModel OptimizationSubgroup Analysis via Model-based Rule Forest
Machine learning models are often criticized for their black-box nature, raising concerns about their applicability in critical decision-making scenarios. Consequently, there is a growing demand for interpretable models …
Decision MakingInterpretable Machine LearningmodelModel Optimization+1Reliable Multi-modal Medical Image-to-image Translation Independent of Pixel-wise Aligned Data
The current mainstream multi-modal medical image-to-image translation methods face a contradiction. Supervised methods with outstanding performance rely on pixel-wise aligned training data to constrain the model optimiza…
Image RegistrationImage-to-Image TranslationMedical Image RegistrationModel Optimization+1TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines
Smaller machine learning models, with less complex architectures and sensor inputs, can benefit wearable sensor-based human activity recognition (HAR) systems in many ways, from complexity and cost to battery life. In th…
Activity RecognitionHuman Activity RecognitionKnowledge DistillationModel OptimizationMeta-Learning Empowered Meta-Face: Personalized Speaking Style Adaptation for Audio-Driven 3D Talking Face Animation
Audio-driven 3D face animation is increasingly vital in live streaming and augmented reality applications. While remarkable progress has been observed, most existing approaches are designed for specific individuals with …
3D Face AnimationMeta-LearningModel OptimizationNoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models
In this paper, we introduce Nested Low-Rank Adaptation (NoRA), a novel approach to parameter-efficient fine-tuning that extends the capabilities of Low-Rank Adaptation (LoRA) techniques. Vanilla LoRA overlooks pre-traine…
Model Optimizationparameter-efficient fine-tuningFedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy
Dose prediction plays a key role in knowledge-based planning (KBP) by automatically generating patient-specific dose distribution. Recent advances in deep learning-based dose prediction methods necessitates collaboration…
Federated LearningModel OptimizationPredictionOptimizing Cox Models with Stochastic Gradient Descent: Theoretical Foundations and Practical Guidances
Optimizing Cox regression and its neural network variants poses substantial computational challenges in large-scale studies. Stochastic gradient descent (SGD), known for its scalability in model optimization, has recentl…
Model OptimizationregressionST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification
Painting classification plays a vital role in organizing, finding, and suggesting artwork for digital and classic art galleries. Existing methods struggle with adapting knowledge from the real world to artistic images du…
Decision MakingModel OptimizationStyle TransferInterpreting artificial neural networks to detect genome-wide association signals for complex traits
Investigating the genetic architecture of complex diseases is challenging due to the multifactorial and interactive landscape of genomic and environmental influences. Although genome-wide association studies (GWAS) have …
Feature ImportanceModel OptimizationUncertainty QuantificationSuperior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification Tasks
This study introduces novel superior scoring rules called Penalized Brier Score (PBS) and Penalized Logarithmic Loss (PLL) to improve model evaluation for probabilistic classification. Traditional scoring rules like Brie…
Model OptimizationModel SelectionMulti-class Classificationscoring rule+1Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation
Despite the substantial progress in deep learning, its adoption in industrial robotics projects remains limited, primarily due to challenges in data acquisition and labeling. Previous sim2real approaches using domain ran…
Dataset GenerationDeep LearningInstance SegmentationModel Optimization+4Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labels
Segmenting tumors in histological images is vital for cancer diagnosis. While fully supervised models excel with pixel-level annotations, creating such annotations is labor-intensive and costly. Accurate histopathology i…
Image SegmentationKnowledge DistillationModel OptimizationMultiple Instance Learning+2Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical Investigation
The proliferation of complex deep learning (DL) models has revolutionized various applications, including computer vision-based solutions, prompting their integration into real-time systems. However, the resource-intensi…
Action DetectionBenchmarkingimage-classificationImage Classification+2TRAWL: Tensor Reduced and Approximated Weights for Large Language Models
Recent research has shown that pruning large-scale language models for inference is an effective approach to improving model efficiency, significantly reducing model weights with minimal impact on performance. Interestin…
Language ModellingLarge Language ModelModel OptimizationTensor DecompositionImproving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching
Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingua…
Cross-Lingual TransferData AugmentationModel OptimizationSentence+1