Cover Image

Compositional meta-learning through probabilistic task inference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bakermans, Jacob J. W., Tano, Pablo, Riveland, Reidar, Findling, Charles, Pouget, Alexandre
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Neurons and Cognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Transitive inference as probabilistic preference learning
by: Mannella, Francesco, et al.
Published: (2023)

Emergent time-keeping mechanisms in a deep reinforcement learning agent performing an interval timing task
by: Pednekar, Amrapali, et al.
Published: (2025)

Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects
by: Bishop, William E., et al.
Published: (2026)

Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks
by: Roffet, Facundo, et al.
Published: (2024)

NLP4Neuro: Sequence-to-sequence learning for neural population decoding
by: Morra, Jacob J., et al.
Published: (2025)

Contribution of task-irrelevant stimuli to drift of neural representations
by: Pashakhanloo, Farhad
Published: (2025)

Scalable inference of functional neural connectivity at submillisecond timescales
by: Medvedeva, Arina, et al.
Published: (2025)

Flexible task abstractions emerge in linear networks with fast and bounded units
by: Sandbrink, Kai, et al.
Published: (2024)

Disentangling Representations through Multi-task Learning
by: Vafidis, Pantelis, et al.
Published: (2024)

Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning
by: Tchetchenian, Ari, et al.
Published: (2024)

Adversarial construction as a potential solution to the experiment design problem in large task spaces
by: Godara, Prakhar, et al.
Published: (2026)

Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
by: Nan, Mu, et al.
Published: (2026)

EEG-to-fMRI synthesis of task-evoked and spontaneous brain activity: addressing issues of statistical significance and generalizability
by: Mehta, Neil, et al.
Published: (2025)

EEG-estimated functional connectivity, and not behavior, differentiates Parkinson's patients from health controls during the Simon conflict task
by: Sun, Xiaoxiao, et al.
Published: (2024)

Sparks of cognitive flexibility: self-guided context inference for flexible stimulus-response mapping by attentional routing
by: Sommers, Rowan P., et al.
Published: (2025)

Stimulus symmetries can confound representational similarity analyses
by: Pashakhanloo, Farhad, et al.
Published: (2026)

An introduction to reinforcement learning for neuroscience
by: Jensen, Kristopher T.
Published: (2023)

A ghost mechanism: An analytical model of abrupt learning in recurrent networks
by: Dinc, Fatih, et al.
Published: (2025)

Prediction horizon shapes representations in predictive learning
by: Ratzon, Aviv, et al.
Published: (2025)

Reward function compression facilitates goal-dependent reinforcement learning
by: Molinaro, Gaia, et al.
Published: (2025)

Multi-intention Inverse Q-learning for Interpretable Behavior Representation
by: Zhu, Hao, et al.
Published: (2023)

Separating the what and how of compositional computation to enable reuse and continual learning
by: Shan, Haozhe, et al.
Published: (2025)

Using machine learning methods to predict cognitive age from psychophysiological tests
by: Tyurina, Daria D., et al.
Published: (2025)

Active learning of neural population dynamics using two-photon holographic optogenetics
by: Wagenmaker, Andrew, et al.
Published: (2024)

POCO: Scalable Neural Forecasting through Population Conditioning
by: Duan, Yu, et al.
Published: (2025)

Hierarchical Latent Structure Learning through Online Inference
by: Aitsahalia, Ines, et al.
Published: (2026)

Unsupervised learning of multiscale switching dynamical system models from multimodal neural data
by: Kim, DongKyu, et al.
Published: (2025)

Cortex and subcortex play distinct roles over learning when cortical memory is limited
by: Farrell, Matthew, et al.
Published: (2026)

HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification
by: Jalali, Parnian, et al.
Published: (2023)

DCHO: A Decomposition-Composition Framework for Predicting Higher-Order Brain Connectivity to Enhance Diverse Downstream Applications
by: Li, Weibin, et al.
Published: (2025)

Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks
by: Anthes, Daniel, et al.
Published: (2023)

Interpretable Features for the Assessment of Neurodegenerative Diseases through Handwriting Analysis
by: Thebaud, Thomas, et al.
Published: (2024)

Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data
by: Geenjaar, Eloy, et al.
Published: (2025)

Unsupervised discovery of the shared and private geometry in multi-view data
by: Koukuntla, Sai, et al.
Published: (2024)

Free Energy Projective Simulation (FEPS): Active inference with interpretability
by: Pazem, Joséphine, et al.
Published: (2024)

On efficient computation in active inference
by: Paul, Aswin, et al.
Published: (2023)

Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex
by: Yu, Muquan, et al.
Published: (2025)

Machine learning approaches to uncover the neural mechanisms of motivated behaviour: from ADHD to individual differences in effort and reward sensitivity
by: Trinh, Nam
Published: (2026)

Active inference for action-unaware agents
by: Torresan, Filippo, et al.
Published: (2025)

NeuroXVocal: Detection and Explanation of Alzheimer's Disease through Non-invasive Analysis of Picture-prompted Speech
by: Ntampakis, Nikolaos, et al.
Published: (2025)