23 papers · ranked by Valyu relevance
Chris Piech, Jonathan Spencer, Jonathan Huang, Surya Ganguli + 3 more
'Mehran Sahami' 'Leonidas Guibas' 'Jascha Sohl‐Dickstein'] Knowledge tracing—where a machine models the knowledge of a student as they interact with coursework—is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has…
Kristina V. Dylla, Dana S. Galili, Paul Szyszka, Alja Lüdke
Trace conditioning is a form of associative learning that can be induced by presenting a conditioned stimulus (CS) and an unconditioned stimulus (US) following each other, but separated by a temporal gap. This gap distinguishes trace conditioning from classical delay conditioning, where the CS and US overlap. To bridge…
Ari E. Kahn, Dani S. Bassett, Nathaniel D. Daw
Decisions in humans and other organisms depend, in part, on learning and using models that capture the statistical structure of the world, including the long-run expected outcomes of our actions. One prominent approach to forecasting such long-run outcomes is the successor representation (SR), which predicts future…
Juan M. Galeazzi, Bedeho M. W. Mender, Mariana Paredes, James M. Tromans + 4 more
'James M. Tromans' 'Benjamin D. Evans' 'Loredana Minini' 'Simon M. Stringer' 'Thomas Wennekers'] We show how hand-centred visual representations could develop in the primate posterior parietal and premotor cortices during visually guided learning in a self-organizing neural network model. The model incorporates trace…
Eyal Hadad, Roni Stern
—Modern software projects include automated tests written to check the programs' functionality. The set of functions invoked by a test is called the trace of the test, and the action of obtaining a trace is called tracing. There are many tracing tools since traces are useful for a variety of software engineering tasks…
Boram Yoon
We present a new trace estimator of the matrix whose explicit form is not given but its matrix multiplication to a vector is available. The form of the estimator is similar to the Hutchison stochastic trace estimator, but instead of the random noise vectors in Hutchison estimator, we use small number of probing vectors…
Kyle R. Hansen, Rebecca A. Mount, Sudiksha Sridhar, Ali I. Mohammed + 5 more
Trace conditioning and extinction learning depend on the hippocampus, but it remains unclear how ongoing neural activities in the hippocampus are modulated during different learning processes. To explore this question, we performed calcium imaging in a large number of individual CA1 neurons during both trace eye-blink…
Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy + 3 more
'Richard S Sutton' 'Elliot A Ludvig' 'Adam White'] We present three new diagnostic prediction problems inspired by classical-conditioning experiments to facilitate research in online prediction learning. Experiments in classical conditioning show that animals such as rabbits, pigeons, and dogs can make long temporal…
Manuel Valle Torre, Catharine Oertel, Marcus Specht
Describing and analysing learner behaviour using sequential data and analysis is becoming more and more popular in Learning Analytics. Nevertheless, we found a variety of definitions of learning sequences, as well as choices regarding data aggregation and the methods implemented for analysis. Furthermore, sequences are…
Onno Eberhard, Michael Muehlebach, Claire Vernade
Partially observable environments present a considerable computational challenge in reinforcement learning due to the need to consider long histories. Learning with a finite window of observations quickly becomes intractable as the window length grows. In this work, we introduce memory traces. Inspired by eligibility…
Marco P Lehmann, He A Xu, Vasiliki Liakoni, Michael H Herzog + 4 more
'Wulfram Gerstner' 'Kerstin Preuschoff' 'Joshua I Gold' 'Thorsten Kahnt'] In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of…
Marc Winter, Julia Mordel, Julia Mendzheritskaya, Daniel Biedermann + 8 more
Learning in asynchronous online settings (AOSs) is challenging for university students. However, the construct of learning engagement (LE) represents a possible lever to identify and reduce challenges while learning online, especially, in AOSs. Learning analytics provides a fruitful framework to analyze students'…
Abdelhamid Zouhair, El Mokhtar En-Naimi, Benaissa Amami, Hadhoum Boukachour + 2 more
'Hadhoum Boukachour' 'Patrick Person' 'Cyrille Bertelle'] In E-learning, there is still the problem of knowing how to ensure an individualized and continuous learner"s follow-up during learning process, indeed among the numerous tools proposed, very few systems concentrate on a real time learner"s follow-up. Our work…
Darby M. Losey, Jay A. Hennig, Emily R. Oby, Matthew D. Golub + 7 more
How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI…
Corson N. Areshenkoff, Anouk de Brouwer, Daniel J. Gale, Joseph Y. Nashed + 1 more
Motor learning is supported by multiple systems adapted to processing different forms of sensory information (e.g., reward versus error feedback), and by higher-order systems supporting strategic processes. Yet, the extent to which these systems recruit shared versus separate neural pathways is poorly understood. To…
Dong Ho Kang, Hyeonjeong Cha, Daein Weon
Reliable operation of multi-agent large language model (LLM) systems depends on debugging long execution traces, where the few causally decisive events are buried in unstructured logs of messages, routes, memory writes, and tool calls. The standard tool is counterfactual replay (rewind, edit, and re-run the trajectory…
Yuki Sakai, Yutaka Sakai, Yoshinari Abe, Jin Narumoto + 1 more
We may view most of our daily activities as rational action selections; however, we sometimes reinforce maladaptive behaviors despite having explicit environmental knowledge. In this study, we modeled obsessive-compulsive disorder (OCD) symptoms as implicitly learned maladaptive behaviors. Simulations in the…
Eden Wu, Sonia Castelo, Yurong Liu, Cláudio T. Silva + 1 more
LLM-powered agents increasingly tackle complex tasks by invoking tools, querying databases, executing code, and manipulating intermediate artifacts. These agents follow trajectories that are typically stored as chronological logs, obscuring the underlying dataflow -- the dependencies between their actions and the…
Dirk Tempelaar, Bart Rienties, Quan Nguyen, Vitomir Kovanovic
For decades, self-report measures based on questionnaires have been widely used in educational research to study implicit and complex constructs such as motivation, emotion, cognitive and metacognitive learning strategies. However, the existence of potential biases in such self-report instruments might cast doubts on…
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
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…
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…
Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep + 3 more
Computer aided synthesis planning is a rapidly growing field for suggesting synthetic routes for molecules of interest. The methods used are usually dependent on access to large datasets for training, but with a finite experimental budget there are limitations on how much data can be obtained from experiments. Active…