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24 papers · ranked by Valyu relevance

01
BbioRxiv21 Aug 2023
Two determinants of dynamic adaptive learning for magnitudes and probabilities
Cedric Foucault, Florent Meyniel
Humans face a dynamic world that requires them to constantly update their knowledge. Each observation should influence their knowledge to a varying degree depending on whether it arises from a stochastic fluctuation or an environmental change. Thus, humans should dynamically adapt their learning rate based on each…
85relevance
02
AarXiv2017
Reinforcement Learning for Learning Rate Control
Chang Xu, Tao Qin, Gang Wang, Tie‐Yan Liu
Stochastic gradient descent (SGD), which updates the model parameters by adding a local gradient times a learning rate at each step, is widely used in model training of machine learning algorithms such as neural networks. It is observed that the models trained by SGD are sensitive to learning rates and good learning…
82relevance
03
PPubMed12 Feb 2026
Duration between rewards controls the rate of behavioral and dopaminergic learning
Dennis A. Burke, Annie Taylor, Huijeong Jeong, SeulAh Lee + 6 more
Learning the causes of rewards is crucial for survival. Cue-reward associative learning is controlled in the brain by mesolimbic dopamine. It is widely believed that dopamine drives learning by conveying a reward prediction error. Dopamine-based learning algorithms are generally ‘trial-based’: learning progresses…
82relevance
04
PPubMed30 Jul 2024
Learning depends on the information conveyed by temporal relationships between events and is reflected in the dopamine response to cues Timing regulates anticipation and learning
Peter D. Balsam, Eleanor H. Simpson, Kathleen Taylor, Abigail Kalmbach + 1 more
'Abigail Kalmbach' 'Charles R. Gallistel'] Contemporary theories guiding the search for neural mechanisms of learning and memory assume that associative learning results from the temporal pairing of cues and reinforcers resulting in coincident activation of associated neurons, strengthening their synaptic connection.…
81relevance
05
BbioRxiv4 May 2022
A hippocampal-anterior thalamic circuit associated with learning rate in ageing
Darya Frank, Marta Garo-Pascual, Pablo Alejandro Reyes Velasquez, Belén Frades + 3 more
Memory normally declines with ageing and these age-related cognitive changes are associated with changes in brain structure. Episodic memory retrieval has been widely studied during ageing, whereas learning has received less attention. Here we examined the neural correlates of learning rate in ageing. Our study sample…
80relevance
06
AarXiv2022
Learning, fast and slow
Markus Meister
Animals can learn efficiently from a single experience and change their future behavior in response. However, in other instances, animals learn very slowly, requiring thousands of experiences. Here I survey tasks involving fast and slow learning and consider some hypotheses for what differentiates the underlying neural…
80relevance
07
AarXiv2020
A Theory of Universal Learning
Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel + 1 more
'Amir Yehudayoff'] How quickly can a given class of concepts be learned from examples? It is common to measure the performance of a supervised machine learning algorithm by plotting its "learning curve", that is, the decay of the error rate as a function of the number of training examples. However, the classical…
78relevance
08
BbioRxiv27 May 2019
Stimulus presentation rates affect performance but not the acquired knowledge – Evidence from procedural learning
Mariann Kiss, Dezso Nemeth, Karolina Janacsek
Presentation rates – the tempo in which we encounter subsequent items – can alter both our behavioral and neural responses in cognitive domains such as learning, memory, decision-making, perception and language. However, it is still unclear to what extent presentation rates affect the momentary performance versus the…
73relevance
09
BbioRxiv14 Aug 2018
Decoupling between causal understanding and awareness during learning and inference
Jihyea Lee, Jerald D. Kralik, YuJin Cha, Jee Hang Lee + 1 more
Causal reasoning is a principal higher-cognitive ability of humans, however, much remains unknown, including (a) the type (systematic versus intermixed) and order (inductive-then-deductive or vice versa) of experience that best achieves causal-chain extraction; (b) how inferences generalize to novel problems…
73relevance
10
PPubMed1 Nov 2013
A Computational Model Associating Learning Process, Word Attributes, and Age of Acquisition Associating Word Learning to Age of Acquisition
Shohei Hidaka, Johan J. Bolhuis
We propose a new model-based approach linking word learning to the age of acquisition (AoA) of words; a new computational tool for understanding the relationships among word learning processes, psychological attributes, and word AoAs as measures of vocabulary growth. The computational model developed describes the…
69relevance
11
AarXiv2013
Generalized simulation model of teaching and its research on PC
Robert Mayer
One of the important problems of cyber pedagogy is the following: how, knowing the parameters of the student, his initial level of knowledge and the impact of the teacher to predict knowledge of student at subsequent times. Simulation method allows you to create a computer program that simulates the behavior of the…
68relevance
12
PPubMed2 Aug 2019
Using Micro-learning on Mobile Applications to Increase Knowledge Retention and Work Performance: A Review of Literature
Alexander Muacevic, John R Adler, Mrigank S Shail
Micro-learning is an educational teaching method used to train users on multiple platforms. This article will provide a brief introduction to the concepts of short-term and long-term memory, and explain how micro-learning can be used to increase retention in learners. Micro-lessons can aid in negating the Ebbinghaus…
64relevance
13
CchemRxiv10 Jul 2025
LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields
Authors not listed
Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…
62relevance
14
BbioRxiv11 Sep 2024
Curriculum Learning: sequential acquisition of task complexity enhances neuronal discriminability
Maria Shujah, Suyash Joshi, Mari Nakamura, Tania Rinaldi Barkat
Curriculum Learning (CL) is a strategy where concepts with increasing complexity are acquired sequentially. Despite its widespread application, its putative neural mechanisms are poorly understood. In this study, using a multi-staged Go/No-Go auditory discrimination paradigm for mice, we show that CL improves and…
62relevance
15
AarXiv10 Feb 2019
Continual lifelong learning with neural networks: A review
German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan + 1 more
'Stefan Wermter'] > Abstract: Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and…
61relevance
16
CchemRxiv30 May 2023
Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals
Yu Zhang, Jinhui Yu, Hongwei Song, Minghui Yang
Accurate determination of reaction rate constants in the combustion circumstance is very challenging both experimentally and theoretically. In this work, three supervised machine learning algorithms, including XGB, FNN and XGB-FNN, are used to develop quantitative structure−property relationship models for the…
60relevance
17
AarXiv22 May 2025
Human-like Forgetting Curves in Deep Neural Networks
Kline, Dylan
This study bridges cognitive science and neural network design by examining whether artificial models exhibit human-like forgetting curves. Drawing upon Ebbinghaus' seminal work on memory decay and principles of spaced repetition, we propose a quantitative framework to measure information retention in neural networks.…
60relevance
18
PPubMed9 Aug 2023
Dental Education: A Guide for Novice Tutors
Alexander Muacevic, John R Adler, Muslat A Bin Rubaia’an
The primary objective of undergraduate-level dental education is to produce proficient dental practitioners who can effectively address the oral health needs of the community and enhance the overall oral health of the population. The field of dental education is subject to continuous change that is shaped by many…
58relevance
19
PPubMed11 May 2016
Integrating neuroscience and learning: now’s the time... Editorial
Pankaj Sah, Michael Fanselow, John Hattie, Susan Magsamen + 3 more
'Jason Mattingley' 'Gregory Quirk' 'Stephen Williams'] The ability to learn and to retrieve information from memory arose early in the evolution of animals, and is present across all species, from humans to the simple roundworm (C. elegans). The capacity to learn is critical for survival, whether it be to find food and…
58relevance
20
CchemRxiv16 Sep 2021
Shaping student confidence and their perception of learning in undergraduate chemistry and biochemistry courses
Timothy Gould
This study examined factors influencing student confidence and their perception of learning in the context of undergraduate chemistry and biochemistry courses. Anonymous online surveys were used to measure the extent to which small group work influenced student confidence in solving problems compared to working…
55relevance
21
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…
54relevance
22
PPubMed25 Oct 2013
Learning to learn to expand freedom in choices
Carine Signoret
“Once an idea has taken hold of the brain it's almost impossible to eradicate. An idea that is fully formed, fully understood. That sticks, right in there somewhere. [he points to his head]” (Inception, 2010). Learning is related to knowledge that is shared between teacher and students. Whatever the use of traditional…
54relevance
23
CchemRxiv30 Jun 2021
Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction
Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep + 3 more
Computer aided synthesis planning is a rapidly growing field for suggesting synthetic routes for molecules of interest. The methods used are usually dependent on access to large datasets for training, but with a finite experimental budget there are limitations on how much data can be obtained from experiments. Active…
53relevance
24
AarXiv2018
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona + 1 more
'Yisong Yue'] In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teaching focuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of…
52relevance

Results for learning rate

Two determinants of dynamic adaptive learning for magnitudes and probabilities

Reinforcement Learning for Learning Rate Control

Duration between rewards controls the rate of behavioral and dopaminergic learning

Learning depends on the information conveyed by temporal relationships between events and is reflected in the dopamine response to cues Timing regulates anticipation and learning

A hippocampal-anterior thalamic circuit associated with learning rate in ageing

Learning, fast and slow

A Theory of Universal Learning

Stimulus presentation rates affect performance but not the acquired knowledge – Evidence from procedural learning

Decoupling between causal understanding and awareness during learning and inference

A Computational Model Associating Learning Process, Word Attributes, and Age of Acquisition Associating Word Learning to Age of Acquisition

Generalized simulation model of teaching and its research on PC

Using Micro-learning on Mobile Applications to Increase Knowledge Retention and Work Performance: A Review of Literature

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

Curriculum Learning: sequential acquisition of task complexity enhances neuronal discriminability

Continual lifelong learning with neural networks: A review

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals

Human-like Forgetting Curves in Deep Neural Networks

Dental Education: A Guide for Novice Tutors

Integrating neuroscience and learning: now’s the time... Editorial

Shaping student confidence and their perception of learning in undergraduate chemistry and biochemistry courses

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

Learning to learn to expand freedom in choices

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners

Two determinants of dynamic adaptive learning for magnitudes and probabilities

Reinforcement Learning for Learning Rate Control

Duration between rewards controls the rate of behavioral and dopaminergic learning

Learning depends on the information conveyed by temporal relationships between events and is reflected in the dopamine response to cues Timing regulates anticipation and learning

A hippocampal-anterior thalamic circuit associated with learning rate in ageing

Learning, fast and slow

A Theory of Universal Learning

Stimulus presentation rates affect performance but not the acquired knowledge – Evidence from procedural learning

Decoupling between causal understanding and awareness during learning and inference

A Computational Model Associating Learning Process, Word Attributes, and Age of Acquisition Associating Word Learning to Age of Acquisition

Generalized simulation model of teaching and its research on PC

Using Micro-learning on Mobile Applications to Increase Knowledge Retention and Work Performance: A Review of Literature

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

Curriculum Learning: sequential acquisition of task complexity enhances neuronal discriminability

Continual lifelong learning with neural networks: A review

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals

Human-like Forgetting Curves in Deep Neural Networks

Dental Education: A Guide for Novice Tutors

Integrating neuroscience and learning: now’s the time... Editorial

Shaping student confidence and their perception of learning in undergraduate chemistry and biochemistry courses

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

Learning to learn to expand freedom in choices

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners