24 papers · ranked by Valyu relevance
Christoffer Lundbak Olesen, Nace Mikuš, Mads Hansen, Nicolas Legrand + 3 more
Biological cognition depends on learning-structured representations in ambiguous environments. Computational models of structure learning typically frame this as an inference problem, but often overlook the temporally extended dynamics that shape learning trajectories under ambiguity. In this paper, we reframe…
Karl Friston, Lancelot Da Costa, Alexander Tschantz, Alex Kiefer + 9 more
'Tommaso Salvatori' 'Victorita Neacsu' 'Magnus Koudahl' 'Conor Heins' 'Noor Sajid' 'Dimitrije Marković' 'Thomas Parr' 'Tim Verbelen' 'Christopher L. Buckley'] This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data…
Niloufar Razmi, Xufeng Caesar Dai, Leah Bakst, Matthew R. Nassar
People rapidly recalibrate their expectations about the world in the face of surprising observations. This recalibration should depend on the temporal structure of the environment, however how people should and do learn temporal structures remains unknown. To examine this gap, we developed a Bayesian model that infers…
Dominik Garber, József Fiser
Transfer learning, the re-application of previously learned higher-level regularities to novel input, is a key challenge in cognition. While previous empirical studies investigated human transfer learning in supervised or reinforcement learning for explicit knowledge, it is unknown whether such transfer occurs during…
Daniel L. Kimmel, Kimberly L. Stachenfeld, Nikolaus Kriegeskorte, Stefano Fusi + 2 more
Abstraction and generalization are essential for flexible decision-making in novel situations. Recent work in humans and monkeys has shown how abstract variables are encoded by the representational geometry of neural population activity. However, these observations—which are typically made after learning has…
Anna Székely, Balázs Török, Mariann Kiss, Karolina Janacsek + 2 more
'Dezső Németh' 'Gergő Orbán'] Title: Abstract Transfer learning, the reuse of newly acquired knowledge under novel circumstances, is a critical hallmark of human intelligence that has frequently been pitted against the capacities of artificial learning agents. Yet, the computations relevant to transfer learning have…
Timo Flesch, Andrew Saxe, Christopher Summerfield
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how tasks can be acquired and coded in ways that minimise mutual interference. We review recent work that has explored the geometry and…
Xiangjuan Ren, Muzhi Wang, Tingting Qin, Fang Fang + 2 more
Humans naturally seek knowledge, yet integrating vast, fragmented information remains challenging. Traditionally, knowledge acquisition has relied on random walks within network—an unguided and inefficient process. We introduce compressive learning, a framework that embeds higher-order structural features—specifically…
Zeki Doruk Erden
Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure. A recently proposed developmental, gradient-free learning framework addresses these limitations by learning a discrete, topological model of its inputs through local variation and…
Lennart Luettgau, Nan Chen, Tore Erdmann, Sebastijan Veselic + 3 more
An exceptional human ability to adapt to the dynamics of novel environments relies on abstracting and generalizing past experiences. While previous research has examined how humans generalize isolated sequential processes, we know little concerning the neural mechanisms that enable adaptation to the more complex…
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…
Julia Steinberg, Haim Sompolinsky
A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically…
Timo Flesch, Andrew Saxe, Christopher Summerfield
Title: Highlights 1. Both natural and artificial agents face the challenge of learning in ways that support effective future behaviour. 2. This may be achieved by different learning regimes, associated with distinct dynamics, and differing dimensionality and geometry of neural task representations. 3. Where two…
Jacob Russin, Ellie Pavlick, Michael J. Frank
Human learning embodies a striking duality: sometimes, we appear capable of following logical, compositional rules and benefit from structured curricula (e.g., in formal education), while other times, we rely on an incremental approach or trial-and-error, learning better from curricula that are randomly interleaved.…
Rajat Saxena, Bruce L. McNaughton
continual learning Authors: ['Rajat Saxena' 'Bruce L. McNaughton'] The authors thank Jeffery Krichmar, Artur Luczak, Aaron Bornstein, Robert Bain, Janina Ferbinteanu, Justin Shobe, Wing Ning, and Aditi Bishnoi for their insightful comments. We also thank Aditi Bishnoi for helping us with graphic illustrations. This…
Zhang, Qi
Explicit (declarative) memory, the memory that can be "declared" in words or languages, is made up of two dissociated parts: episodic memory and semantic memory. This dissociation has its neuroanatomical basis—episodic memory is mostly associated with the hippocampus and semantic memory with the neocortex. The two…
Authors not listed
Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the…
N. Menghi, S. Vigano’, W. J. Johnston, S. Elnagar + 2 more
Learning depends not only on the content of what we learn, but also on how we learn and on how experiences are structured over time. To investigate how task similarity and training regime interact during learning, we trained participants on spatial and conceptual learning tasks that shared either similar or distinct…
Anthony Onwuli, Keith T. Butler, Aron Walsh
High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and…
Alexandra R. van den Berg, Pieter R. Roelfsema, Sander M. Bohte, Shenbing Kuang
'Shenbing Kuang'] The acquisition of knowledge and skills does not occur in isolation but learning experiences amalgamate within and across domains. The process through which learning can accelerate over time is referred to as learning-to-learn or meta-learning. While meta-learning can be implemented in recurrent…
Paul Francoeur, Daniel Penaherrera, David Koes
The immense size of chemical space, the relative scarcity of high quality data, and the cost of running experiments to accurately measure molecular properties makes active learning (AL) an attractive approach to efficiently explore the space and train high-quality models for molecular property prediction. While AL is…
Laxmi R. Iyer, Ali A. Minai
—Learning meaningful sentences is different from learning a random set of words. When humans understand the meaning, the learning occurs relatively quickly. What mechanisms enable this to happen? In this paper, we examine the learning of novel sequences in familiar situations. We embed the Small World of Words…
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
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…