Papers Multi-target regression
“Multi-target regression” 태그가 달린 논문 38편 · 필터 해제
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+2TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
Polynomial regression is widely used and can help to express nonlinear patterns. However, considering very high polynomial orders may lead to overfitting and poor extrapolation ability for unseen data. The paper presents…
BenchmarkingMulti-target regressionregressionSymbolic RegressionLocal Interpretability of Random Forests for Multi-Target Regression
Multi-target regression is useful in a plethora of applications. Although random forest models perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in machine learning, especial…
Multi-target regressionregressionJGPR: a computationally efficient multi-target Gaussian process regression algorithm
Multi-target regression algorithms are designed to predict multiple outputs at the same time, and allow us to take all output variables into account during the training phase. Despite the recent advances, this context of…
GPRMulti-target regressionregressionComparison of single and multitask learning for predicting cognitive decline based on MRI data
The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes …
DiagnosticDomain AdaptationMulti-target regressionMulti-Task Learning+2Robust Regression via Model Based Methods
The mean squared error loss is widely used in many applications, including auto-encoders, multi-target regression, and matrix factorization, to name a few. Despite computational advantages due to its differentiability, i…
modelMulti-target regressionregressionMaterials Representation and Transfer Learning for Multi-Property Prediction
The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development…
BIG-bench Machine LearningGenerative Adversarial NetworkMulti-target regressionPrediction+3Conformal Uncertainty Sets for Robust Optimization
Decision-making under uncertainty is hugely important for any decisions sensitive to perturbations in observed data. One method of incorporating uncertainty into making optimal decisions is through robust optimization, w…
Conformal PredictionDecision MakingDecision Making Under UncertaintyMulti-target regression+2A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area
In this contribution, we predict the wellhead protection area (WHPA, target), the shape and extent of which is influenced by the distribution of hydraulic conductivity (K), from a small number of tracing experiments (pre…
Bayesian InferenceExperimental DesignMulti-target regressionMulti-target prediction for dummies using two-branch neural networks
Multi-target prediction (MTP) serves as an umbrella term for machine learning tasks that concern the simultaneous prediction of multiple target variables. Classical instantiations are multi-label classification, multivar…
BIG-bench Machine LearningMatrix CompletionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Copula-based conformal prediction for Multi-Target Regression
There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., freq…
Conformal PredictionMulti-target regressionMulti-Task LearningPrediction+2Multi-target normal behaviour models for wind farm condition monitoring
The trend towards larger wind turbines and remote locations of wind farms fuels the demand for automated condition monitoring strategies that can reduce the operating cost and avoid unplanned downtime. Normal behaviour m…
Multi-target regressionDeep tree-ensembles for multi-output prediction
Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nevertheless, they also suffer from weaknes…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation
A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of specie…
Multi-target regressionregressionNeural Unsigned Distance Fields for Implicit Function Learning
In this work we target a learnable output representation that allows continuous, high resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a Neural Network, thereby breaking previous …
Multi-target regression