miscellaneous-1

Multi-target regression

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in multi-target-regression-1

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Dataset

Best Model

Actions

Google 5 qubit random Hamiltonian

Libraries

i

Use these libraries to find multi-target-regression-1 models and implementations

Datasets

SIZER

CAMELS Multifield Dataset

Replication Data for: Investigating the concentration of High Yield Investment Programs in the United Kingdom

Subtasks

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Most implemented papers

INTEL-TAU: A Color Constancy Dataset

Alexandros Iosifidis, Jenni Raitoharju, M. Gabbouj, Firas Laakom, Jarno Nikkanen•Tue Oct 22 2019

This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research, and is coherent with the new General Data Protection Regulation (GDPR).

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Materials Representation and Transfer Learning for Multi-Property Prediction

Shufeng Kong, D. Guevarra, Carla P. Gomes, J. Gregoire•Sun May 23 2021

The Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework is introduced that seamlessly integrates prediction using only a material’s composition, learning and exploitation of correlations among target properties in multitarget regression, and leveraging training data from tangential domains via generative transfer learning.

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Boosting on the shoulders of giants in quantum device calibration

A. Wozniakowski, Jayne Thompson, M. Gu, F. Binder•Tue May 12 2020

This work shows its efficacy in predicting the entire energy spectrum of a Hamiltonian on a superconducting quantum device, a key task in present quantum computer calibration, and demonstrates how artificial intelligence can be further enhanced by "standing on the shoulders of giants."

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Deep Multimodal Transfer-Learned Regression in Data-Poor Domains

Brian M. Sadler, L. McClenny, M. Haile, V. Attari, U. Braga-Neto, R. Arróyave•Mon Jun 15 2020

A Deep Multimodal Transfer-Learned Regressor (DMTL-R) for multimodal learning of image and feature data in a deep regression architecture effective at predicting target parameters in data-poor domains is proposed.

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DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection

Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha•Sun Jul 05 2020

DATE, a model of Dual-task Attentive Tree-aware tree-aware Embedding, is proposed, to classify and rank illegal trade flows that contribute the most to the overall customs revenue when caught.

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Learning the Pareto Front with Hypernetworks

Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik•Wed Oct 07 2020

The problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training is tackled, and PFL opens the door to new applications where models are selected based on preferences that are only available at run time.

179 0

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Neural Unsigned Distance Fields for Implicit Function Learning

Gerard Pons-Moll, Aymen Mir, Julian Chibane•Sun Oct 25 2020

This work proposes Neural Distance Fields (NDF), a neural network based model which predicts the unsigned distance field for arbitrary 3D shapes given sparse point clouds, and finds NDF can be used for multi-target regression with techniques that have been exclusively used for rendering in graphics.

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A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area

Robin Thibaut, E. Laloy, T. Hermans•Tue May 11 2021

This work aims to make stochastic predictions of the WHPA within the Bayesian Evidential Learning (BEL) framework, which aims to find a direct relationship between predictor and target using machine learning.

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Comparison of single and multi-task learning for predicting cognitive decline based on MRI data

J. Tohka, Vandad Imani, M. Prakash, M. Zare•Mon Sep 20 2021

It is demonstrated that ADAS-Cog change can be, to some extent, predicted based on anatomical MRI and the recommended method for learning the predictive models is a single-task regularized linear regression owing to its simplicity and good performance.

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JGPR: a computationally efficient multi-target Gaussian process regression algorithm

M. Nabati, S. Ghorashi, Reza Shahbazian•Tue May 10 2022

A low-cost multi-target Gaussian process regression (GPR) algorithm that employs a shared covariance matrix among the targets during the training phase and solves a sub-optimal cost function for optimization of hyperparameters is introduced.

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