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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
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…
97relevance
03
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
04
PPubMed9 Oct 2020
Integrating Machine Learning with Human Knowledge
Changyu Deng, Xunbi Ji, Colton Rainey, Jianyu Zhang + 1 more
Title: Summary Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement…
97relevance
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…
92relevance
06
PPubMed12 Nov 2019
Multi-task learning with a natural metric for quantitative structure activity relationship learning
Noureddin Sadawi, Ivan Olier, Joaquin Vanschoren, Jan N. van Rijn + 5 more
'Jeremy Besnard' 'Richard Bickerton' 'Crina Grosan' 'Larisa Soldatova' 'Ross D. King'] The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task…
92relevance
07
PPubMed10 May 2023
Efficient multi-task learning with adaptive temporal structure for progression prediction
Menghui Zhou, Yu Zhang, Tong Liu, Yun Yang + 1 more
In this paper, we propose a novel efficient multi-task learning formulation for the class of progression problems in which its state will continuously change over time. To use the shared knowledge information between multiple tasks to improve performance, existing multi-task learning methods mainly focus on feature…
91relevance
08
AarXiv2016
Self-Paced Multi-Task Learning
Changsheng Li, Fan Wei, Junchi Yan, Weishan Dong + 2 more
'Zha Hongyuan'] Multi-task learning is a paradigm, where multiple tasks are jointly learnt. Previous multi-task learning models usually treat all tasks and instances per task equally during learning. Inspired by the fact that humans often learn from easy concepts to hard ones in the cognitive process, in this paper, we…
90relevance
09
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…
89relevance
10
PPubMed20 Mar 2008
Improving prediction accuracy of tumor classification by reusing genes discarded during gene selection
Jack Y Yang, Guo-Zheng Li, Hao-Hua Meng, Mary Qu Yang + 1 more
Background Since the high dimensionality of gene expression microarray data sets degrades the generalization performance of classifiers, feature selection, which selects relevant features and discards irrelevant and redundant features, has been widely used in the bioinformatics field. Multi-task learning is a novel…
89relevance
11
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…
88relevance
12
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']…
88relevance
13
BbioRxiv21 Jul 2021
Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures in reinforcement learning
Rex G Liu, Michael J Frank
A hallmark of human intelligence, but challenging for reinforcement learning (RL) agents, is our ability to compositionally generalise: to recompose familiar knowledge components in novel ways to solve new problems. For instance, a talented musician can conceivably transfer her knowledge of flute fingerings and guitar…
88relevance
14
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…
87relevance
15
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.…
87relevance
16
AarXiv11 Sep 2020
Learning an Interpretable Graph Structure in Multi-Task Learning
Shujian Yu, Francesco Alesiani, Ammar Shaker, Wenzhe Yin
—We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph. Unlike existing multi-task learning methodologies, the graph structure is not assumed to be known a priori or estimated separately in a preprocessing step. Instead…
86relevance
17
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…
85relevance
18
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…
84relevance
19
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.…
84relevance
20
PPubMed29 Nov 2021
Transformational machine learning: Learning how to learn from many related scientific problems
Ivan Olier, Oghenejokpeme I. Orhobor, Tirtharaj Dash, Andy M. Davis + 3 more
'Larisa N. Soldatova' 'Joaquin Vanschoren' 'Ross D. King'] Title: Significance Machine learning (ML) is the branch of artificial intelligence (AI) that develops computational systems that learn from experience. In supervised ML, the ML system generalizes from labelled examples to learn a model that can predict the…
83relevance
21
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…
81relevance
22
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…
80relevance
23
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
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

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

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

Integrating Machine Learning with Human Knowledge

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

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Efficient multi-task learning with adaptive temporal structure for progression prediction

Self-Paced Multi-Task Learning

Multi-Task Learning for Visual Scene Understanding

Improving prediction accuracy of tumor classification by reusing genes discarded during gene selection

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

Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures in reinforcement learning

Learning to Multitask

Multi-Task Reinforcement Learning in Humans

Learning an Interpretable Graph Structure in Multi-Task Learning

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

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

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

Transformational machine learning: Learning how to learn from many related scientific problems

Quantifying the hardness of bioactivity prediction tasks for transfer learning

Multitask Learning over Shared Subspaces

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

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

An Overview of Multi-Task Learning in Deep Neural Networks

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

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

Integrating Machine Learning with Human Knowledge

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

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Efficient multi-task learning with adaptive temporal structure for progression prediction

Self-Paced Multi-Task Learning

Multi-Task Learning for Visual Scene Understanding

Improving prediction accuracy of tumor classification by reusing genes discarded during gene selection

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

Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures in reinforcement learning

Learning to Multitask

Multi-Task Reinforcement Learning in Humans

Learning an Interpretable Graph Structure in Multi-Task Learning

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

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

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

Transformational machine learning: Learning how to learn from many related scientific problems

Quantifying the hardness of bioactivity prediction tasks for transfer learning

Multitask Learning over Shared Subspaces

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

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