24 papers · ranked by Valyu relevance
Alexey A. Melnikov, Adi Makmal, Vedran Dunjko, Hans J. Briegel
The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and…
Filip Ilievski, Barbara Hammer, Frank van Harmelen, Benjamin Paaßen + 21 more
'Sascha Saralajew' 'Ute Schmid' 'Michael Biehl' 'Marianna Bolognesi' 'Xin Dong' 'Kiril Gashteovski' 'Pascal Hitzler' 'Giuseppe Marra' 'Pasquale Minervini' 'Martin Mundt' 'Axel-Cyrille Ngonga Ngomo' 'Alessandro Oltramari' 'Gabriella Pasi' 'Zeynep G. Saribatur' 'Luciano Serafini' 'John Shawe‐Taylor' 'Vered Shwartz'…
Eugene Poh, Naser Al-Fawakhiri, Rachel Tam, Jordan A. Taylor + 1 more
To generate adaptive movements we must generalize what we have previously learned to novel situations. The generalization of adapted movements has typically been framed as a consequence of neural tuning functions that overlap for similar movement kinematics - what might be considered bottom-up generalization. However…
Jordan A. Taylor, Richard B. Ivry
The pattern of generalization following motor learning can provide a probe on the neural mechanisms underlying learning. For example, the breadth of generalization to untrained regions of space after visuomotor adaptation to targets in a restricted region of space has been attributed to the directional tuning…
Gonzague Yernaux, Wim Vanhoof
Anti-unification refers to the process of generalizing two (or more) goals into a single, more general, goal that captures some of the structure that is common to all initial goals. In general one is typically interested in computing what is often called a most specific generalization, that is a generalization that…
Hassan Aı̈t-Kaci, Gabriella Pasi
Unification and generalization are operations on two terms computing respectively their greatest lower bound and least upper bound when the terms are quasi-ordered by subsumption up to variable renaming (i.e., t1 t2 iff t1 = t2σ for some variable substitution σ). When term signatures are such that distinct functor…
Jiwon Park, Dongil Chung
People often use recognizable features to infer the value of novel consumables. This “generalization” strategy is known to be beneficial in stable environments, such that individuals can use previously learned rules and values in efficiently exploring new situations. However, it remains unclear whether and how…
Marlie C. Tandoc, Mollie Bayda, Craig Poskanzer, Eileen Cho + 3 more
Extracting shared structure across our experiences allows us to generalize our knowledge to novel contexts. How do different brain states influence this ability to generalize? Using a novel category learning paradigm, we assess the effect of both sleep and time of day on generalization that depends on the flexible…
Steven Phillips
Consistently predicting outcomes in novel situations is colloquially called “going beyond the data,” or “generalization.” Going beyond the data features in spatial and non-spatial cognition, raising the question of whether such features have a common basis-a kind of systematicity of generalization. Here, we…
Anja F. Syring, Niek Tax, Wil M. P. van der Aalst
Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the…
Ella Bar, Max Bringmann, Gennadiy Belonosov, Kristoffer Aberg + 3 more
Overgeneralization of negative experiences, in which aversive responses spread to otherwise safe stimuli, often co-occurs with sleep disruption, and both are central features of anxiety and posttraumatic stress disorder (PTSD). Here, we show that sleep shifts emotional generalization away from the negative and toward…
Celeste Michelle Condit, L Bruce Railsback
Background Biological organisms and their components are better conceived within categories based on similarity rather than on identity. Biologists routinely operate with similarity-based concepts such as "model organism" and "motif." There has been little exploration of the characteristics of the similarity-based…
Tal Neiman, Yonatan Loewenstein, Jill X. O'Reilly
In operant learning, behaviors are reinforced or inhibited in response to the consequences of similar actions taken in the past. However, because in natural environments the “same” situation never recurs, it is essential for the learner to decide what “similar” is so that he can generalize from experience in one state…
Daniel Reißner, Abel Armas-Cervantes, Marcello La Rosa
- A framework of generalization measures is defined in the field of automated process discovery that relates patterns of process behavior identified from an event log to the control-flow structures of a process model. - The framework is instantiated with a generalization measure that tests the repetitive and concurrent…
Eric B. Laber, Min Qian
In this entry we review the generalization error for classification and single-stage decision problems. We distinguish three alternative definitions of the generalization error which have, at times, been conflated in the statistics literature and show that these definitions need not be equivalent even asymptotically.…
Benno I. Simmons, Jeferson Vizentin-Bugoni, Pietro K. Maruyama, Peter A. Cotton + 16 more
Abundant pollinators are often more generalised than rare pollinators. This could be because abundance drives generalisation: neutral effects suggest that more abundant species will be more generalised simply because they have more chance encounters with potential partners. On the other hand, generalisation could drive…
Alba Lopez-Moraga, Zeynep Gültekin, Laura Luyten, Tom Beckers
Generalization of conditioned fear is adaptive for survival. However, overgeneralization of fear from threat cues to loosely similar yet safe stimuli is a hallmark of anxiety-related disorders. Such overgeneralization may moreover impact other fear learning processes. In particular, broad fear generalization might…
Yu Zhang, Jinhui Yu, Hongwei Song, Minghui Yang
Accurate determination of reaction rate constants in the combustion circumstance is very challenging both experimentally and theoretically. In this work, three supervised machine learning algorithms, including XGB, FNN and XGB-FNN, are used to develop quantitative structure−property relationship models for the…
Brett K. Hayes, Evan Heit, Caren M. Rotello
Traditionally, memory, reasoning, and categorization have been treated as separate components of human cognition. We challenge this distinction, arguing that there is broad scope for crossover between the methods and theories developed for each task. The links between memory and reasoning are illustrated in a review of…
Matthias Borgstede
The Price equation provides a formal account of selection building on a right-total mapping between two classes of individuals, which is usually interpreted as a parent-offspring relation. This paper presents a new formulation of the Price equation in terms of fuzzy set-mappings to account for structures where the…
Authors not listed
Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…
Authors not listed
The concept of aClassical Structure provides a broad mathematical framework, whereas a Hyperstructure arises via the powerset construction, and an 𝑛-Superhyperstructure is obtained by iterating this construction n times [1]. Intuitively, the n-th powerset corresponds to 𝑛 successive applications of the powerset…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…