28 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…
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…
Pierre Yger, KD Harris
Learning is often considered to be governed by reinforcement, causing animals to increase the probability of performing rewarded behaviours, and decrease the probability of behaviours leading to harmful outcomes. However, not all learning is governed by reward. Mere exposure to a novel environment or set of sensory…
Lara Lloret Iglesias, Pablo Sanz Bellón, Amaia Pérez del Barrio, Pablo Menéndez Fernández-Miranda + 4 more
Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the…
Kamil Rocki
There exists a theory of a single general-purpose learning algorithm which could explain the principles of its operation. This theory assumes that the brain has some initial rough architecture, a small library of simple innate circuits which are prewired at birth and proposes that all significant mental algorithms can…
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…
Osvaldo Simeone
—Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper…
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…
Philipp Weidel, Renato Duarte, Abigail Morrison
Reinforcement learning is a learning paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. However, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields. This is problematic, as such…
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…
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…
Kamil Rocki
There exists a theory of a single general-purpose learning algorithm which could explain the principles its operation. It assumes the initial rough architecture, a small library of simple innate circuits which are prewired at birth. and proposes that all significant mental algorithms are learned. Given current…
O. A. Randle, Ogunduyile O.O, Tranos Zuva, N. A. Fashola
Awale games have become widely recognized across the world, for their innovative strategies and techniques which were used in evolving the agents (player) and have produced interesting results under various conditions. This paper will compare the results of the two major machine learning techniques by reviewing their…
R. Vidya, G. M. Nasira, R. P. Jaia Priyankka
- Sparse coding algorithm is an learning algorithm mainly for unsupervised feature for finding succinct, a little above high - level Representation of inputs, and it has successfully given a way for Deep learning. Our objective is to use High - Level Representation data in form of unlabeled category to help…
Anthony M. Zador
Over the last decade, artificial neural networks (ANNs), have undergone a revolution, catalyzed in large part by better tools for supervised learning. However, training such networks requires enormous data sets of labeled examples, whereas young animals (including humans) typically learn with few or no labeled…
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…
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…
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.…
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…
Hiroaki Sasaki, Takashi Takenouchi
Unsupervised representation learning is one of the most important problems in machine learning. A recent promising approach is contrastive learning: A feature representation of data is learned by solving a pseudo classification problem where class labels are automatically generated from unlabelled data. However, it is…
Tomaž Stepišnik, Dragi Kocev, Sebastian Ventura
Semi-supervised learning combines supervised and unsupervised learning approaches to learn predictive models from both labeled and unlabeled data. It is most appropriate for problems where labeled examples are difficult to obtain but unlabeled examples are readily available (e.g., drug repurposing). Semi-supervised…
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…
David Higgins, Michael Herzog
We examine the unsupervised bias hypothesis [11] as an explanation for failure to learn two bisection tasks, when task sequencing is randomly alternating (roving). This hypothesis is based on the idea that a covariance based synaptic plasticity rule, which is modulated by a reward signal, can be biased when reward is…
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…
Peyman Razmi, Mahdi Ghaemi Asl, Giorgio Canarella, Afsaneh Sadat Emami + 1 more
'Afsaneh Sadat Emami' 'Talib Al-Ameri'] This paper contributes to the literature on topology identification (TI) in distribution networks and, in particular, on change detection in switching devices’ status. The lack of measurements in distribution networks compared to transmission networks is a notable challenge. In…
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…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…
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…