26 papers · ranked by Valyu relevance
Juan Jovel, Russell Greiner
Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data-e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of…
Yanbei Chen, Massimiliano Mancini, Xiatian Zhu, Zeynep Akata
—State-of-the-art deep learning models are often trained with a large amount of costly labeled training data. However, requiring exhaustive manual annotations may degrade the model's generalizability in the limited-label regime. Semi-supervised learning and unsupervised learning offer promising paradigms to learn from…
Paola Patricia Ariza-Colpas, Enrico Vicario, Ana Isabel Oviedo-Carrascal, Shariq Butt Aziz + 7 more
The Assisted Living Environments Research Area-AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative…
Franziska Bröker, Bradley C. Love, Peter Dayan
Title: Highlights 1. • People can learn through unsupervised or supervised means. 2. • Semi-supervised learning includes both unsupervised and supervised trials. 3. • Unsupervised trials can help or harm semi-supervised human category learning. 4. • Unsupervised trials help when aligned with knowledge reflecting…
E. Moebel, C. Kervrann
Cryo electron tomography visualizes native cells at nanometer resolution, but analysis is challenged by noise and artifacts. Recently, supervised deep learning methods have been applied to decipher the 3D spatial distribution of macromolecules. However, in order to discover unknown objects, unsupervised classification…
Yue Li, Bingyan Liu, Jinyan Deng, Yi Guo + 1 more
Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or…
Josef Kittler, Sara Atito, Muhammad Awais
Self-supervised learning (SSL) is recognized as an essential tool for building foundation models for Artificial Intelligence applications. The advances in SSL have been made thanks to vigorous arguments about the principles of SSL and through extensive empirical research. The aim of this paper is to contribute to the…
Taehoon Kim
| 1 | | Introduction to Key Concepts in Machine Learning and Deep Learning | 12 | | --- | --- | --- | --- | | | 1.1 | Introduction to Machine Learning | 12 | | | 1.2 | Supervised Learning Fundamentals | 13 | | | 1.2.1 | Generalization to New Examples | 14 | | | 1.2.2 | Underfitting and Overfitting | 14 | | | 1.3 |…
Dongshu Liu, Jérémie Laydevant, Adrien Pontlevy, Damien Querlioz + 1 more
'Julie Grollier'] Abstract. Current unsupervised learning methods depend on end-to-end training via deep learning techniques such as self-supervised learning, with high computational requirements, or employ layer-by-layer training using bio-inspired approaches like Hebbian learning, using local learning rules…
Takeo Watanabe, Yuka Sasaki, Takuro Zama, Julian R Matthews + 2 more
Unsupervised learning—learning through repeated exposure without instruction or reward—is central to both machine learning and human cognition, including language acquisition and statistical learning. However, its role in visual perceptual learning (VPL) remains debated, as previous studies have not shown VPL for…
Luis Sa-Couto, Andreas Wichert
Interest in unsupervised learning architectures has been rising. Besides being biologically unnatural, it is costly to depend on large labeled data sets to get a well-performing classification system. Therefore, both the deep learning community and the more biologically-inspired models community have focused on…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Lin Zhong, Scott Baptista, Rachel Gattoni, Jon Arnold + 3 more
Representation learning in neural networks may be implemented with supervised or unsupervised algorithms, distinguished by the availability of feedback. In sensory cortex, perceptual learning drives neural plasticity, but it is not known if this is due to supervised or unsupervised learning. Here we recorded…
Alfredo Ibias, Hector Antona, Guillem Ramirez-Miranda, Enric Guinovart + 1 more
'Enric Guinovart' 'Eduard Alarcón'] Abstract—Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propose a stateof-the-art, primitive-based, unsupervised…
Saber Kazeminasab, Sayuri Sekimitsu, Mojtaba Fazli, Mohammad Eslami + 6 more
Artificial intelligence (AI) has been increasingly used to analyze optical coherence tomography (OCT) images to better understand physiology and genetic architecture of ophthalmic diseases. However, to date, research has been limited by the inability to transfer OCT phenotypes from one dataset to another. In this work…
Francisco A. Rodrigues
– Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as…
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
Kevin Kermani Nejad, Paul Anastasiades, Loreen Hertäg, Rui Ponte Costa
The neocortex constructs an internal representation of the world, but the underlying circuitry and computational principles remain unclear. Inspired by self-supervised learning algorithms, we introduce a computational model wherein layer 2/3 (L2/3) learns to predict incoming sensory stimuli by comparing previous…
Giovanni Granato, Emilio Cartoni, Federico Da Rold, Andrea Mattera + 2 more
'Gianluca Baldassarre' 'Frederic Alexandre'] Categorical perception identifies a tuning of human perceptual systems that can occur during the execution of a categorisation task. Despite the fact that experimental studies and computational models suggest that this tuning is influenced by task-independent effects (e.g.…
Timo Flesch, Andrew Saxe, Christopher Summerfield
Title: Highlights 1. Both natural and artificial agents face the challenge of learning in ways that support effective future behaviour. 2. This may be achieved by different learning regimes, associated with distinct dynamics, and differing dimensionality and geometry of neural task representations. 3. Where two…
Noorbakhsh Amiri Golilarz, Elias Hossain, Abdoljalil Addeh, Keyan Rahimi
'Keyan Rahimi'] Abstract—In this paper, we discuss learning algorithms and their importance in different types of applications which includes training to identify important patterns and features in a straightforward, easy-to-understand manner. We will review the main concepts of artificial intelligence (AI), machine…
Jea Kwon, Sunpil Kim, Dong-Kyum Kim, Jinhyeong Joo + 3 more
While huge strides have recently been made in language-based machine learning, the ability of artificial systems to comprehend the sequences that comprise animal behavior has been lagging behind. In contrast, humans instinctively recognize behaviors by finding similarities in behavioral sequences. Here, we develop an…
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
Acoustic measurements of batteries are known to be correlated to their state-of-charge, creating opportunities for state estimation that do not rely on electrical signals. State estimators are typically parametric models fitted from data, often from the broad toolbox of machine learning. Such models can be easily…
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…