Papers Parameter Prediction
“Parameter Prediction” 태그가 달린 논문 63편 · 필터 해제
Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks
Background: Brain network models offer insights into brain dynamics, but the utility of model-derived bifurcation parameters as biomarkers remains underexplored. Objective: This study evaluates bifurcation parameters fro…
Parameter PredictionMulti-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification
Multimodal machine learning has achieved remarkable progress in many scenarios, but its reliability is undermined by varying sample quality. This paper finds that existing reliable multimodal classification methods not o…
InformativenessParameter PredictionLocate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors
Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding…
Parameter PredictionStellar parameter prediction and spectral simulation using machine learning
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a se…
Computational EfficiencyCPUGPUParameter PredictionTowards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction
Bolted joints are critical in engineering for maintaining structural integrity and reliability. Accurate prediction of parameters influencing their function and behavior is essential for optimal performance. Traditional …
DiversityFrictionParameter PredictionPreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence
We present PreF3R, Pose-Free Feed-forward 3D Reconstruction from an image sequence of variable length. Unlike previous approaches, PreF3R removes the need for camera calibration and reconstructs the 3D Gaussian field wit…
3D ReconstructionCamera CalibrationNovel View SynthesisParameter PredictionNovel View Acoustic Parameter Estimation
The task of Novel View Acoustic Synthesis (NVAS) - generating Room Impulse Responses (RIRs) for unseen source and receiver positions in a scene - has recently gained traction, especially given its relevance to Augmented …
3D geometryImage-to-Image Translationparameter estimationParameter PredictionAccelerating Learned Video Compression via Low-Resolution Representation Learning
In recent years, the field of learned video compression has witnessed rapid advancement, exemplified by the latest neural video codecs DCVC-DC that has outperformed the upcoming next-generation codec ECM in terms of comp…
Parameter PredictionRepresentation LearningVideo CompressionBrain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction
Aero-engine fault prediction aims to accurately predict the development trend of the future state of aero-engines, so as to diagnose faults in advance. Traditional aero-engine parameter prediction methods mainly use the …
Parameter PredictionPredictionTime SeriesOptimal Kernel Tuning Parameter Prediction using Deep Sequence Models
GPU kernels have come to the forefront of comput- ing due to their utility in varied fields, from high-performance computing to machine learning. A typical GPU compute kernel is invoked millions, if not billions of times…
GPUParameter PredictionGraph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computationa…
Combinatorial OptimizationGraph LearningParameter PredictionMinimally Supervised Learning using Topological Projections in Self-Organizing Maps
Parameter prediction is essential for many applications, facilitating insightful interpretation and decision-making. However, in many real life domains, such as power systems, medicine, and engineering, it can be very ex…
Decision MakingParameter PredictionregressionTaskBench: Benchmarking Large Language Models for Task Automation
In recent years, the remarkable progress of large language models (LLMs) has sparked interest in task automation, which involves decomposing complex tasks described by user instructions into sub-tasks and invoking extern…
BenchmarkingParameter PredictionEnhancing Deep Neural Network Training Efficiency and Performance through Linear Prediction
Deep neural networks (DNN) have achieved remarkable success in various fields, including computer vision and natural language processing. However, training an effective DNN model still poses challenges. This paper aims t…
Parameter PredictionPredictionControlling Geometric Abstraction and Texture for Artistic Images
We present a novel method for the interactive control of geometric abstraction and texture in artistic images. Previous example-based stylization methods often entangle shape, texture, and color, while generative methods…
Image GenerationImage StylizationParameter PredictionStyle Transfer+1DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms
The exponential run time of quantum simulators on classical machines and long queue times and high costs of real quantum devices present significant challenges in the efficient optimization of Variational Quantum Algorit…
Parameter PredictionPredictionForest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation
In prediction of forest parameters with data from remote sensing (RS), regression models have traditionally been trained on a small sample of ground reference data. This paper proposes to impute this sample of true predi…
DiversityImputationParameter PredictionPrediction+1ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization
In practice, digital pathology images are often affected by various factors, resulting in very large differences in color and brightness. Stain normalization can effectively reduce the differences in color and brightness…
Computational EfficiencyDiagnosticParameter PredictionBreaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks
Transfer learning is a popular technique for improving the performance of neural networks. However, existing methods are limited to transferring parameters between networks with same architectures. We present a method fo…
Neural Architecture SearchParameter PredictionTransfer LearningGHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks
Deep convolutional neural network (CNN) training via iterative optimization has had incredible success in finding optimal parameters. However, modern CNN architectures often contain millions of parameters. Thus, any give…
Adversarial RobustnessParameter PredictionQuantization