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24 papers · ranked by Valyu relevance

01
AarXiv2017
An Overview of Multi-Task Learning in Deep Neural Networks
Sebastian Ruder
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for…
97relevance
02
PPubMed2 Mar 2016
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Andreas Holzinger
Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great…
97relevance
03
AarXiv2012
A Convex Formulation for Learning Task Relationships in Multi-Task Learning
Yu Zhang, Dit‐Yan Yeung
Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a…
96relevance
04
PPubMed28 Nov 2018
A Survey of Multi‐task Learning Methods in Chemoinformatics
Sergey Sosnin, Mariia Vashurina, Michael Withnall, Pavel Karpov + 2 more
'Maxim Fedorov' 'Igor V. Tetko'] Title: Abstract Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and…
91relevance
05
PPubMed29 Apr 2022
Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments
Abhiram Iyer, Karan Grewal, Akash Velu, Lucas Oliveira Souza + 2 more
'Jeremy Forest' 'Subutai Ahmad'] A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often…
90relevance
06
PPubMed26 Oct 2010
Inferring latent task structure for Multitask Learning by Multiple Kernel Learning
Christian Widmer, Nora C Toussaint, Yasemin Altun, Gunnar Rätsch
Background The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In…
89relevance
07
AarXiv2022
Multi-Task Learning for Visual Scene Understanding
Simon Vandenhende
A PhD is more than just another university degree. A PhD is about the pursuit of knowledge, the aspiration to figure things out and the love to explore new ideas. It is a personal journey filled with challenges, discoveries and encounters with extraordinary people. As I reflect on this incredible adventure, a feeling…
88relevance
08
PPubMed31 Oct 2022
Prediction of drug–target interactions through multi-task learning
Chaeyoung Moon, Dongsup Kim
Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. However, multi-task learning sometimes worsens the overall performance…
87relevance
09
AarXiv2022
Design Perspectives of Multitask Deep Learning Models and Applications
Yeshwant Singh, Anupam Biswas, Angshuman Bora, Debashish Malakar + 2 more
'Subham Chakraborty' 'Suman Bera'] Abstract- In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric better. Under…
87relevance
10
AarXiv29 Apr 2024
Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
Jun Yu, Yutong Dai, Xiaokang Liu, Jin Huang + 13 more
Spanning Traditional, Deep, and Pretrained Foundation Model Eras Authors: ['Jun Yu' 'Yutong Dai' 'Xiaokang Liu' 'Jin Huang' 'Yishan Shen' 'Ke Zhang' 'Rong Zhou' 'Eashan Adhikarla' 'Wenxuan Ye' 'Yixin Liu' 'Zhaoming Kong' 'Kai Zhang' 'Yilong Yin' 'Vinod Namboodiri' 'Brian D. Davison' 'Jason H. Moore' 'Yong Chen']…
87relevance
11
BbioRxiv14 Jul 2020
Multitask Learning over Shared Subspaces
Nicholas Menghi, Kemal Kacar, Will Penny
This paper uses constructs from the field of multitask machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach. We found, as hypothesised, that subject performance was significantly higher on the…
87relevance
12
BbioRxiv22 Oct 2019
Multi-Task Reinforcement Learning in Humans
Momchil S. Tomov, Eric Schulz, Samuel J. Gershman
The ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multi-task reinforcement learning. We study participants’ behavior in a novel two-step decision making task with multiple features and changing reward functions.…
86relevance
13
AarXiv2018
Learning to Multitask
Yu Zhang, Ying Wei, Qiang Yang
Multitask learning has shown promising performance in many applications and many multitask models have been proposed. In order to identify an effective multitask model for a given multitask problem, we propose a learning framework called learning to multitask (L2MT). To achieve the goal, L2MT exploits historical…
85relevance
14
AarXiv2020
A Simple General Approach to Balance Task Difficulty in Multi-Task Learning
Sicong Liang, Yu Zhang
In multi-task learning, difficulty levels of different tasks are varying. There are many works to handle this situation and we classify them into five categories, including the direct sum approach, the weighted sum approach, the maximum approach, the curriculum learning approach, and the multi-objective optimization…
84relevance
15
PPubMed23 Feb 2023
Abstract representations emerge naturally in neural networks trained to perform multiple tasks
W. Jeffrey Johnston, Stefano Fusi
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning.…
84relevance
16
CchemRxiv27 Sep 2023
Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields
Leif Jacobson, James Stevenson, Farhad Ramezanghorbani, Steven Dajnowicz + 1 more
Transferable neural network potentials have shown great promise as an avenue to increase the accuracy and applicability of existing atomistic force fields for organic molecules and inorganic materials. Training sets used to develop transferable potentials are very large, typically millions of examples, and as such, are…
82relevance
17
AarXiv2020
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
Sachin Ravi, Sebastian Musslick, Maia Hamin, Theodore L. Willke + 1 more
'Jonathan D. Cohen'] The terms multi-task learning and multitasking are easily confused. Multi-task learning refers to a paradigm in machine learning in which a network is trained on various related tasks to facilitate the acquisition of tasks. In contrast, multitasking is used to indicate, especially in the cognitive…
82relevance
18
BbioRxiv21 Oct 2021
Abstract representations emerge naturally in neural networks trained to perform multiple tasks
W. Jeffrey Johnston, Stefano Fusi
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning.…
82relevance
19
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
81relevance
20
BbioRxiv15 Aug 2022
Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
Laura Driscoll, Krishna Shenoy, David Sussillo
Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned.…
80relevance
21
BbioRxiv30 May 2019
Reward-predictive representations generalize across tasks in reinforcement learning
Lucas Lehnert, Michael L. Littman, Michael J. Frank
In computer science, reinforcement learning is a powerful framework with which artificial agents can learn to maximize their performance for any given Markov decision process (MDP). Advances over the last decade, in combination with deep neural networks, have enjoyed performance advantages over humans in many difficult…
76relevance
22
CchemRxiv25 Jan 2024
Deep learning for low-data drug discovery: hurdles and opportunities
Derek van Tilborg, Helena Brinkmann, Emanuele Criscuolo, Luke Rossen + 2 more
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches – which are notoriously…
72relevance
23
CchemRxiv29 Jan 2024
Quantifying the hardness of bioactivity prediction tasks for transfer learning
Hosein Fooladi, Steffen Hirte, Johannes Kirchmair
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These…
69relevance
24
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
65relevance

Results for multi‑task learning

An Overview of Multi-Task Learning in Deep Neural Networks

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

A Convex Formulation for Learning Task Relationships in Multi-Task Learning

A Survey of Multi‐task Learning Methods in Chemoinformatics

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

Multi-Task Learning for Visual Scene Understanding

Prediction of drug–target interactions through multi-task learning

Design Perspectives of Multitask Deep Learning Models and Applications

Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Multitask Learning over Shared Subspaces

Multi-Task Reinforcement Learning in Humans

Learning to Multitask

A Simple General Approach to Balance Task Difficulty in Multi-Task Learning

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields

Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs

Reward-predictive representations generalize across tasks in reinforcement learning

Deep learning for low-data drug discovery: hurdles and opportunities

Quantifying the hardness of bioactivity prediction tasks for transfer learning

Multi-fidelity machine learning models for improved high-throughput screening predictions

An Overview of Multi-Task Learning in Deep Neural Networks

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

A Convex Formulation for Learning Task Relationships in Multi-Task Learning

A Survey of Multi‐task Learning Methods in Chemoinformatics

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

Multi-Task Learning for Visual Scene Understanding

Prediction of drug–target interactions through multi-task learning

Design Perspectives of Multitask Deep Learning Models and Applications

Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Multitask Learning over Shared Subspaces

Multi-Task Reinforcement Learning in Humans

Learning to Multitask

A Simple General Approach to Balance Task Difficulty in Multi-Task Learning

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields

Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs

Reward-predictive representations generalize across tasks in reinforcement learning

Deep learning for low-data drug discovery: hurdles and opportunities

Quantifying the hardness of bioactivity prediction tasks for transfer learning

Multi-fidelity machine learning models for improved high-throughput screening predictions