27 papers · ranked by Valyu relevance
Stefano Recanatesi, Matthew Farrell, Guillaume Lajoie, Sophie Deneve + 2 more
Neural networks have achieved many recent successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations. Similarly, biological neural circuits and in…
Ashena Gorgan Mohammadi, Manu Srinath Halvagal, Friedemann Zenke
Tracking prey or recognizing a lurking predator is as crucial for survival as anticipating their actions. To guide behavior, the brain must extract information about object identities and their dynamics from entangled sensory inputs. How it accomplishes this feat remains an open question. Predictive coding theories…
Antonino Greco, Clara Rastelli, Leonardo Bonetti, Christoph Braun + 1 more
A longstanding question in cognitive science is whether the human brain learns sensory regularities that are irrelevant to ongoing behavior, a phenomenon known as incidental associative learning. Here, we provide evidence at the single subject level that humans indeed acquired such incidental associations and reveal…
Vanessa Ferdinand, Amy Yu, Sarah Marzen
Organisms can solve complex tasks despite having limited cognitive resources when those resources are used optimally. Doing so optimally makes an organism “resource-rational”. In this paper, we show for the first time that humans are resource-rational at prediction. In a novel sequence learning experiment, participants…
Teo Sušnjak
A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies…
Jaehoon Shin, Jee Hang Lee, Sang Wan Lee
Human reward learning is constrained by environmental structure. Stable environments facilitate reward prediction but limit learning experiences^1–8^, while uncertain environments hinder predictability and learnability^9–12^. We propose a novel framework extending these boundaries through “meta-prediction” – predicting…
Feiyue Qiu, Guodao Zhang, Xin Sheng, Lei Jiang + 4 more
E-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently…
Matthew Oyeleye, Tianhua Chen, Sofya Titarenko, Grigoris Antoniou + 2 more
'Keun Ho Ryu' 'Nipon Theera-Umpon'] Heart disease, caused by low heart rate, is one of the most significant causes of mortality in the world today. Therefore, it is critical to monitor heart health by identifying the deviation in the heart rate very early, which makes it easier to detect and manage the heart’s function…
Bo Cao, Russell Greiner, Andrew Greenshaw, Jie Sui + 1 more
'Amaryllis Mavragani'] Title: Abstract Recent applications of artificial intelligence (AI) and machine learning in medicine, psychology, and social sciences have led to common terminological confusions. In this paper, we review emerging evidence from systematic reviews documenting widespread misuse of key terms…
Danilo Bzdok, John P. A. Ioannidis
The last decades saw dramatic progress in brain research. These advances were often buttressed by probing single variables to make circumscribed discoveries, typically through null hypothesis significance testing. New ways for generating massive data fueled tension between the traditional methodology, used to infer…
Wei‐Hung Weng
- Understand the basics of machine learning techniques and the reasons behind why they are useful for solving clinical prediction problems. - Understand the intuition behind some machine learning models, including regression, decision trees, and support vector machines. - Understand how to apply these models to…
Beren Millidge, Mufeng Tang, Mahyar Osanlouy, Nicol S. Harper + 1 more
One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of…
Markus Conci, Martina Zellin, Hermann J. Müller
Generating predictions for task-relevant goals is a fundamental requirement of human information processing, as it ensures adaptive success in our complex natural environment. Clark ([4]) proposed a model of hierarchical predictive processing, in which perception, attention, and learning are unified within a coherent…
J Orpella, E Mas-Herrero, P Ripollés, J Marco-Pallarés + 1 more
Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline…
Ian Lundberg, Rachel Brown-Weinstock, Susan Clampet-Lundquist, Sarah Pachman + 4 more
'Sarah Pachman' 'Timothy J. Nelson' 'Vicki Yang' 'Kathryn Edin' 'Matthew J. Salganik'] Title: Significance Scientists and decision-makers routinely make life outcome predictions: they use information from the past to predict what will happen to someone in the future. These predictions, whether made by human experts or…
Nihal Dadheech
In our research journey, we undertook a comprehensive exploration of protein-protein interaction (PPI) prediction, with a primary focus on unraveling the intricate web of interactions involving the SARS-CoV-2 virus. Our research endeavor encompassed a multi-faceted approach that seamlessly integrated data…
Edeh Michael Onyema, Khalid K. Almuzaini, Fergus Uchenna Onu, Devvret Verma + 3 more
'Devvret Verma' 'Ugboaja Samuel Gregory' 'Monika Puttaramaiah' 'Rockson Kwasi Afriyie'] The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast…
Kennedy E. Ehimwenma, Safiya Al Sharji, Maruf A. Raheem
The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a…
Ričards Marcinkevičs, Ece Özkan, Julia E. Vogt
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this 'data deluge'. This broad nontechnical overview provides a gentle…
Authors not listed
Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
Gargi Roy, Dalia Chakrabarty
We introduce parametrisation of that property of the available training dataset, that necessitates an inhomogeneous correlation structure for the function that is learnt as a model of the relationship between the pair of variables, observations of which comprise the considered training data. We refer to a…
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
Authors not listed
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV) are two important targets in current drug discovery, mainly due to the COVID-19 pandemic and the MERS-CoV outbreaks in recent years. An important target of both SARS-CoV-2 and MERS-CoV is the main…
Peter C. Austin, Frank E. Harrell Jr, Douglas S. Lee, Ewout W. Steyerberg
Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses in specific datasets. We used Monte Carlo simulations to determine when machine learning methods perform better than statistical learning methods in a specific setting.…
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…
Jules Schleinitz, Maxime Langevin, Yanis Smail, Benjamin Wehnert + 2 more
Synthetic yield prediction using machine learning is intensively studied. Previous work focused on two categories of datasets: High-Throughput Experimentation data, as an ideal case study and datasets extracted from proprietary databases, which are known to have a strong reporting bias towards high yields. However…
Shuo Yang
Shuo Yang Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Doctor of Philosophy in the School of Informatics, Computing, and Engineering Indiana University December 2017 Accepted by the Graduate Faculty, Indiana University, in partial fulfillment of…