Multi-target regression
1개 벤치마크 · 논문 38편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
Materials Representation and Transfer Learning for Multi-Property Prediction
Regularization via f-Divergence: An Application to Multi-Oxide Spectroscopic Analysis
Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets
TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
Local Interpretability of Random Forests for Multi-Target Regression
Papers
Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework
Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that oversimplifies the continuous, fluid, and co…
Multi-target regressionInterpretable Multivariate Conformal Prediction with Fast Transductive Standardization
We propose a conformal prediction method for constructing tight simultaneous prediction intervals for multiple, potentially related, numerical outputs given a single input. This method can be combined with any multi-targ…
Multi-target regressionDeep Imbalanced Multi-Target Regression: 3D Point Cloud Voxel Content Estimation in Simulated Forests
Voxelization is an effective approach to reduce the computational cost of processing Light Detection and Ranging (LiDAR) data, yet it results in a loss of fine-scale structural information. This study explores whether lo…
Multi-target regressionImage GenerationPoint CloudsRegularization via f-Divergence: An Application to Multi-Oxide Spectroscopic Analysis
In this paper, we address the task of characterizing the chemical composition of planetary surfaces using convolutional neural networks (CNNs). Specifically, we seek to predict the multi-oxide weights of rock samples bas…
Multi-target regressionUtilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets
The selection of algorithms is a crucial step in designing AI services for real-world time series classification use cases. Traditional methods such as neural architecture search, automated machine learning, combined alg…
Multi-target regressionNeural Architecture SearchPrivacy PreservingTime Series+1Multi-Task Learning with Multi-Task Optimization
Multi-task learning solves multiple correlated tasks. However, conflicts may exist between them. In such circumstances, a single solution can rarely optimize all the tasks, leading to performance trade-offs. To arrive at…
Automated Theorem Provingimage-classificationImage ClassificationMulti-target regression+2