Papers Parameter Prediction
“Parameter Prediction” 태그가 달린 논문 63편 · 필터 해제
FreqAdapt: Frequency-Adaptive Processing for RAW Object Detection
Existing object detection methods predominantly utilize sRGB inputs, which are compressed from RAW sensor data using Image Signal Processors (ISP) originally designed for visualization purposes. Compared to RGB images, R…
Parameter PredictionObject DetectionParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits
As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and often lead to costly layout iterations. …
Parameter PredictionGraph LearningLinear-Time Global Visual Modeling without Explicit Attention
Existing research largely attributes the global sequence modeling capability of Transformers to the explicit computation of attention weights, a process that inherently incurs quadratic computational complexity. In this …
Parameter PredictionInstance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem
Algorithm performance in combinatorial optimization is highly sensitive to parameter settings, while a single globally tuned configuration often fails to exploit the heterogeneity of instances. This limitation is particu…
Parameter PredictionDisentangle-then-Align: Non-Iterative Hybrid Multimodal Image Registration via Cross-Scale Feature Disentanglement
Multimodal image registration is a fundamental task and a prerequisite for downstream cross-modal analysis. Despite recent progress in shared feature extraction and multi-scale architectures, two key limitations remain. …
Parameter PredictionImage RegistrationBreaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which …
Parameter PredictionCoronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography
X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contras…
Parameter PredictionPseudodata-guided Invariant Representation Learning Boosts the Out-of-Distribution Generalization in Enzymatic Kinetic Parameter Prediction
Accurate prediction of enzyme kinetic parameters is essential for understanding catalytic mechanisms and guiding enzyme engineering.However, existing deep learning-based enzyme-substrate interaction (ESI) predictors ofte…
Representation LearningParameter PredictionFrom Noise to Latent: Generating Gaussian Latents for INR-Based Image Compression
Recent implicit neural representation (INR)-based image compression methods have shown competitive performance by overfitting image-specific latent codes. However, they remain inferior to end-to-end (E2E) compression app…
Parameter PredictionImage CompressionURDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model
Constructing accurate digital twins of articulated objects is essential for robotic simulation training and embodied AI world model building, yet historically requires painstaking manual modeling or multi-stage pipelines…
Parameter PredictionFrom Sound to Setting: AI-Based Equalizer Parameter Prediction for Piano Tone Replication
This project presents an AI-based system for tone replication in music production, focusing on predicting EQ parameter settings directly from audio features. Unlike traditional audio-to-audio methods, our approach output…
Parameter Prediction(Almost) Free Modality Stitching of Foundation Models
Foundation multi-modal models are often designed by stitching of multiple existing pretrained uni-modal models: for example, an image classifier with an text model. This stitching process is performed by training a conne…
Parameter PredictionOmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning
Understanding and modeling enzyme-substrate interactions is crucial for catalytic mechanism research, enzyme engineering, and metabolic engineering. Although a large number of predictive methods have emerged, they do not…
Parameter PredictionPredictionSpecificityAdam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)
The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm used for solving combinatorial optimization problems such as the Max-Cut problem. A key challenge in QAOA lies in efficiently ide…
Combinatorial OptimizationNavigateParameter PredictionSynthesis of discrete-continuous quantum circuits with multimodal diffusion models
Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based paramete…
DenoisingParameter PredictionMachine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data
Efficient nutrient management and precise fertilization are essential for advancing modern agriculture, particularly in regions striving to optimize crop yields sustainably. The AgroLens project endeavors to address this…
Parameter PredictionArtificial Intelligence in Reactor Physics: Current Status and Future Prospects
Reactor physics is the study of neutron properties, focusing on using models to examine the interactions between neutrons and materials in nuclear reactors. Artificial intelligence (AI) has made significant contributions…
Parameter PredictionBCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation
The properties of black holes and accretion flows can be inferred by fitting Event Horizon Telescope (EHT) data to simulated images generated through general relativistic ray tracing (GRRT). However, due to the computati…
DenoisingImage Generationparameter estimationParameter PredictionDeep-BrownConrady: Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data
This research addresses the challenge of camera calibration and distortion parameter prediction from a single image using deep learning models. The main contributions of this work are: (1) demonstrating that a deep learn…
Autonomous DrivingCamera CalibrationDeep LearningParameter PredictionMachine Learning-Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing
A novel framework is proposed that combines multi-resonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction in biosensing. Unlike traditional single-resonance systems, …
Parameter Prediction