20 papers · ranked by Valyu relevance
Florian Beck, Johannes Fürnkranz
Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest…
Florian Beck, Johannes Fürnkranz
Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest…
Amir Levi, Noam Aviv, Eran Stark
Learning from examples and adapting to new rules are fundamental attributes of human cognition. However, it is unclear what conditions allow for fast and successful learning, especially in non-human subjects. To determine how rapidly freely-moving mice can learn a new rule, we designed a fully automated two-alternative…
Joshua S. Rule, Steven T. Piantadosi, Andrew Cropper, Kevin Ellis + 2 more
Throughout their lives, humans seem to learn a variety of rules for things like applying category labels, following procedures, and explaining causal relationships. These rules are often algorithmically rich but are nonetheless acquired with minimal data and computation. Symbolic models based on program learning…
F. Bouchacourt, S. Tafazoli, M.G. Mattar, T.J. Buschman + 1 more
When performing a task in a changing world, sometimes we switch between rules already learned; at other times we must learn rules anew. Often we must do both, switching between known rules while also constantly re-estimating them. Here, we show these two processes, rule switching and rule learning, rely on distinct but…
Van Quoc Phuong Huynh, Johannes Fürnkranz, Florian Beck
Conventional rule learning algorithms aim at fnding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. Instead, we propose an efcient algorithm that aims at fnding…
Sankhanava Kundu, Daniel keren, Samma Zidan, Amit Kumar + 2 more
Training rodents in a particularly difficult olfactory-discrimination task results with acquisition of high skill to perform the task superbly, termed ‘rule-learning’. We show that rule-learning occurs abruptly, in a “light bulb moment”. Using whole-cell patch-clamp recordings in the piriform cortex of…
Lincen Yang, Matthijs van Leeuwen
Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while learning rules directly from numeric variables is understudied; 2)…
Giulia Vilone, Luca Longo
Understanding the inferences of data-driven, machine-learned models can be seen as a process that discloses the relationships between their input and output. These relationships consist and can be represented as a set of inference rules. However, the models usually do not explicit these rules to their end-users who…
Florian Seiffarth
A common problem of classical neural network architectures is that additional information or expert knowledge cannot be naturally integrated into the learning process. To overcome this limitation, we propose a two-step approach consisting of (1) generating rule functions from knowledge and (2) using these rules to…
Florian Bähner, Tzvetan Popov, Nico Boehme, Selina Hermann + 6 more
Rapid learning in complex and changing environments is a hallmark of intelligent behavior. Humans achieve this in part through abstract concepts applicable to multiple, related situations. It is unclear, however, whether some of the underlying computational mechanisms also exist in other species. We combined…
Catherine L. Williams, Jennifer C. Roop
Conceptual learning is discrimination between new examples and nonexamples and generalization to new examples. Conceptual learning can be demonstrated after practice with differential reinforcement of the correct response and is influenced by procedural variables during practice. However, less research has been done…
Sofia Fregni, Uta Wolfensteller, Hannes Ruge
We used fMRI to investigate the neural changes and representational dynamics associated with different learning modes during initial learning and subsequent implementation of previously acquired stimulus-response (S-R) associations. We compared instruction-based learning (INS) and trial-and-error learning (TE) via a…
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…
Authors not listed
Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
Jeri L. Little, Jexy A. Nepangue, Heidi Kloos
Grouping information into categories enables us to learn, integrate, and apply new information. Presenting items from different categories sequentially (i.e., interleaving) is often more effective than presenting items from a single category sequentially (i.e., blocking), particularly when evaluating learning using…
Authors not listed
SynTemp is a framework designed to extract and hierarchically cluster reaction templates from large-scale reaction data repositories. Reaction templates are partial Imaginary Transition State graphs representing the reaction center as well as surrounding context. These graphs are equivalent to Double Pushout graph…
Authors not listed
Data-driven strategies are reshaping computational materials design by accelerating the prediction of novel compounds with targeted functionalities. Beyond high-throughput screening, the integration of generative artificial intelligence enables exploration across vast chemical spaces comprising millions of known and…
Friedrich Hastedt, Rowan M. Bailey, Klaus Hellgardt, Sophia N. Yaliraki + 2 more
Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box interpretability, obscure model limitations and impede progress in the field. We…
Authors not listed
The Kanamori-Goodenough-Anderson rules are a textbook heuristic for predicting magnetism. They connect bond angles to magnetic ordering for some transition metal compounds. Such domain knowledge is of high importance for building predictive machine learning models in scenarios with scarce data. Yet, there has been no…