21 papers · ranked by Valyu relevance
Alexander H. Liu, Wei-Ning Hsu, Michael Auli, Alexei Baevski
Unsupervised speech recognition has shown great potential to make Automatic Speech Recognition (ASR) systems accessible to every language. However, existing methods still heavily rely on hand-crafted pre-processing. Similar to the trend of making supervised speech recognition end-to-end, we introduce wav2vec-U 2.0…
Lasse Borgholt, Jakob D. Havtorn, Joakim Edin, Lars Maaløe + 1 more
'Christian Igel'] Unsupervised representation learning for speech processing has matured greatly in the last few years. Work in computer vision and natural language processing has paved the way, but speech data offers unique challenges. As a result, methods from other domains rarely translate directly. We review the…
Talha Iqbal, Adnan Elahi, William Wijns, Atif Shahzad
Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of…
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…
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…
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…
Peican Zhu, Xin Hou, Zhen Wang, Feiping Nie
Along with the flourish of the information age, massive amounts of data are generated day by day. Due to the large-scale and high-dimensional characteristics of these data, it is often difficult to achieve better decision-making in practical applications. Therefore, an efficient big data analytics method is urgently…
Hongyu Sun, Yongcai Wang, Xudong Cai, Xuewei Bai + 1 more
— Recently, a growing number of work design unsupervised paradigms for point cloud processing to alleviate the limitation of expensive manual annotation and poor transferability of supervised methods. Among them, CrossPoint follows the contrastive learning framework and exploits image and point cloud data for…
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.…
Takeo Watanabe, Yuka Sasaki, Daiki Ogawa, Kazuhisa Shibata
The question of whether we learn exposed visual features remains a subject of controversy. A prevalent computational model suggests that visual features frequently exposed to observers in natural environments are likely to be learned. However, this unsupervised learning model appears to be contradicted by the…
Shen Tu, Jerwen Jou, Guang Zhao, Jun Jiang
The study of unconscious information processing mechanism is very important to the development of the science of human consciousness. By studying unconscious processing and comparing it with consciousness processing, we can better understand the ways unconsciousness works and the origin of conscious processing. The…
Alfredo Ibias, Hector Antona, Guillem Ramirez-Miranda, Enric Guinovart
'Enric Guinovart'] Abstract. Knowledge discovery is key to understand and interpret a dataset, as well as to find the underlying relationships between its components. Unsupervised Cognition is a novel unsupervised learning algorithm that focus on modelling the learned data. This paper presents three techniques to…
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…
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…
Ning Mei, Roberto Santana, David Soto
The development of novel frameworks to understand the properties of unconscious representations and how they differ from the conscious counterparts may be critical to make progress in the neuroscience of vision consciousness. Here we re-analysed data from a within-subject, high-precision, highly-sampled fMRI study…
Tommaso Ciorli, Lorenzo Pia, Timo Stein
Breaking continuous flash suppression (bCFS) is a widely used experimental paradigm that exploits detection tasks to measure the time an initially invisible stimulus requires to escape interocular suppression and access awareness. One pretty contentious and unresolved issue is whether differences in detection times…
Ning Mei, David Soto
The development of robust frameworks to understand how the human brain represents conscious and unconscious perceptual contents is paramount to make progress in the neuroscience of consciousness. Recent functional MRI studies using multi-voxel pattern classification analyses showed that unconscious contents could be…
Eftychia Eva Kontou, Axel Walter, Oliver Alka, Julianus Pfeuffer + 5 more
Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for…
Brendan T. Hutchinson, Bradley N. Jack, Kristen Pammer, Enriqueta Canseco-Gonzalez + 1 more
A long-standing question concerns whether sensory input can reach semantic stages of processing in the absence of attention and awareness. Here, we examine whether the N400, an event related potential associated with semantic processing, can occur under conditions of inattentional blindness. By employing a novel…
Yang Xi, Lu Zhang, Cunzhen Li, Xiaopeng Lv + 1 more
4## Conclusion This study utilized EEG microstates to divide the AV information processing process into multiple sub-stages and calculated microstate attributes across multiple frequency bands to comprehensively characterize the corresponding brain activity. We propose an evaluation method based on KL_GEV for…