Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains

31 May 2019

Papers citing "Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains"

10 / 10 papers shown

Title
Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types Muhammad Umair Danish Katarina Grolinger AI4TS 82 3 0 07 Feb 2025
Principled Weight Initialization for Hypernetworks Oscar Chang Lampros Flokas Hod Lipson 22 73 0 13 Dec 2023
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition Jorge Armando Mendez Mendez Eric Eaton KELM CLL 26 27 0 15 Jul 2022
Learning the Effect of Registration Hyperparameters with HyperMorph Andrew Hoopes Malte Hoffmann Douglas N. Greve Bruce Fischl John Guttag Adrian V. Dalca 28 38 0 30 Mar 2022
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions Vibhor Gupta Jyoti Narwariya Pankaj Malhotra L. Vig Gautam M. Shroff AI4TS 19 20 0 14 Mar 2022
Simple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings) Elliot Meyerson Xin Qiu Risto Miikkulainen 19 4 0 19 Feb 2022
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks Zhou Xian Shamit Lal H. Tung Emmanouil Antonios Platanios Katerina Fragkiadaki AI4CE 33 23 0 17 Mar 2021
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings Elliot Meyerson Risto Miikkulainen 11 12 0 05 Oct 2020
Meta-Learning in Neural Networks: A Survey Timothy M. Hospedales Antreas Antoniou P. Micaelli Amos Storkey OOD 38 1,927 0 11 Apr 2020
Learning Task Grouping and Overlap in Multi-task Learning Abhishek Kumar Hal Daumé 181 524 0 27 Jun 2012