24 papers · ranked by Valyu relevance
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…
Amir Ramezani Dooraki
Artificial intelligence recently had a great advancements caused by the emergence of new processing power and machine learning methods. Having said that, the learning capability of artificial intelligence is still at its infancy comparing to the learning capability of human and many animals. Many of the current…
Benjamin Lansdell, Konrad P. Körding
In good old-fashioned artificial intelligence (GOFAI), humans specified systems that solved problems. Much of the recent progress in AI has come from replacing human insights by learning. However, learning itself is still usually built by humans – specifically the choice that parameter updates should follow the…
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…
George Leu, Jiangjun Tang
Machine education is an emerging research field that focuses on the problem which is inverse to machine learning. To date, the literature on educating machines is still in its infancy. A fairly low number of methodology and method papers are scattered throughout various formal and informal publication avenues, mainly…
Krishna Teja Challa, Abida Sayed, Yogesh Acharya
This article was migrated. The article was marked as recommended. Objectives: Education is a dynamic process that has to be refined periodically. Lack of innovative teaching techniques in academics makes medical curricula inadequate in making a significant stride towards the future. The objective of this review is to…
Joseph Scott German, Guofeng Cui, Chenliang Xu, Robert A. Jacobs + 1 more
'Ming Bo Cai'] We propose the “runtime learning” hypothesis which states that people quickly learn to perform unfamiliar tasks as the tasks arise by using task-relevant instances of concepts stored in memory during mental training. To make learning rapid, the hypothesis claims that only a few class instances are used…
Andreas C. Bryhn, Andrea Belgrano
The Land-Sea Interface (LSI) is where land and sea meet, not only in physical terms, but also with regards to a large variety of ecological and societal aspects. The United Nations has proclaimed the period 2021-2030 the Ocean Decade, which entails striving for a sustainable use of the ocean and teaching and learning…
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…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
Mingcan Yu, Junying Wang
Although principles of neuroscience like reinforcement learning, visual perception and attention have been applied in machine learning models, there is a huge gap between machine learning and mammalian learning. Based on the advances in neuroscience, we propose the "context sequence theory" to give a common explanation…
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov + 2 more
'Matthew E. Taylor' 'Peter Stone'] Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the…
Alexander Muacevic, John R Adler, Shubhajeet Roy, Narendra Kumar + 6 more
'Vaishali Singh' 'Sarvesh Singh' 'Rahul Kumar' 'Jay Tewari' 'Darshit Samaiya' 'Amod K Sachan'] Background: To develop doctors with appropriate knowledge of health and diseases, reasonable medical abilities, and a positive attitude toward patients and their families, it is important to reexamine the methods used to…
Paul Francoeur, Daniel Penaherrera, David Koes
The immense size of chemical space, the relative scarcity of high quality data, and the cost of running experiments to accurately measure molecular properties makes active learning (AL) an attractive approach to efficiently explore the space and train high-quality models for molecular property prediction. While AL is…
Vishwa Goudar, Barbara Peysakhovich, David J. Freedman, Elizabeth A. Buffalo + 1 more
Learning-to-learn, a progressive speedup of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on…
Michael J. Lee, James J. DiCarlo
A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming…
Authors not listed
With the development of universal machine learning interatomic potentials, a rapidly growing number of chemical space datasets appear. One of the biggest challenges is that these datasets are mostly generated at different quantum chemical (QC) levels. However, a general framework scalable to learning across both…
Michael L. Mack, Bradley C. Love, Alison R. Preston
Learning systems must constantly decide whether to create new representations or update existing ones. For example, a child learning that a bat is a mammal and not a bird would be best served by creating a new representation, whereas updating may be best when encountering a second similar bat. Characterizing the neural…
Dominik Garber, József Fiser
Transfer learning, the re-application of previously learned higher-level regularities to novel input, is a key challenge in cognition. While previous empirical studies investigated human transfer learning in supervised or reinforcement learning for explicit knowledge, it is unknown whether such transfer occurs during…
N. Menghi, S. Vigano’, W. J. Johnston, S. Elnagar + 2 more
Learning depends not only on the content of what we learn, but also on how we learn and on how experiences are structured over time. To investigate how task similarity and training regime interact during learning, we trained participants on spatial and conceptual learning tasks that shared either similar or distinct…
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…
Martin Lövdén, Isabelle Hansson, Toms Voits
Learning is central across many disciplines, yet its definition remains fragmented, hindering theoretical progress and cross-disciplinary communication. We review debates on the definition of learning and propose a unifying, umbrella, definition: learning is a system’s processing of information from its environment…
Yuanqi Du, Chenru Duan, Andres Bran, Anna Sotnikova + 5 more
Large language models (LLMs) have demonstrated outstanding capabilities in general problem-solving and been shown to improve productivity in certain domains. Thanks to their flexibility, recent work has leveraged them for diverse scientific applications, ranging from predictive modeling, scientific Q&A, and even as…