Multi-target regression
12 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Multi-target regression
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Datasets
SIZER
CAMELS Multifield Dataset
Most implemented papers
Materials Representation and Transfer Learning for Multi-Property Prediction
gomes-lab/H-CLMP
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To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning.
INTEL-TAU: A Color Constancy Dataset
firasl/BoCF
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In this paper, we describe a new large dataset for illumination estimation.
Boosting on the shoulders of giants in quantum device calibration
Here we introduce a new approach to machine learning that is able to leverage prior scientific discoveries in order to improve generalizability over a scientific model.
Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
levimcclenny/multimodal_transfer_learned_regression
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In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc.
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
Roytsai27/Dual-Attentive-Tree-aware-Embedding
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KDD 2020
Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes.
Learning the Pareto Front with Hypernetworks
AvivNavon/pareto-hypernetworks
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ICLR 2021
Here, we tackle the problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training.
Neural Unsigned Distance Fields for Implicit Function Learning
NDF represent surfaces at high resolutions as prior implicit models, but do not require closed surface data, and significantly broaden the class of representable shapes in the output.
A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area
The uncertainty range of the posterior WHPA distribution is affected by the number and position of data sources (injection wells).
Comparison 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.
JGPR: 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.
