Cover Image

Meta-Learning Theory-Informed Inductive Biases using Deep Kernel Gaussian Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zakirov, Bahti, Tkačik, Gašper
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Neurons and Cognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Clustering Inductive Biases with Unrolled Networks
by: Huml, Jonathan, et al.
Published: (2023)

A Foundational Theory for Decentralized Sensory Learning
by: Mårtensson, Linus, et al.
Published: (2025)

Learning to Abstract Visuomotor Mappings using Meta-Reinforcement Learning
by: Velazquez-Vargas, Carlos A., et al.
Published: (2024)

Wanting to Be Understood Explains the Meta-Problem of Consciousness
by: Fernando, Chrisantha, et al.
Published: (2025)

Deep Learning Classification of EEG Responses to Multi-Dimensional Transcranial Electrical Stimulation
by: Pastor, Alexis Pomares, et al.
Published: (2025)

Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld
by: Khajehnejad, Moein, et al.
Published: (2024)

From Theory of Mind to Theory of Environment: Counterfactual Simulation of Latent Environmental Dynamics
by: Uchiyama, Ryutaro
Published: (2026)

The Quantification Horizon Theory of Consciousness
by: Le, T. R.
Published: (2017)

The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction
by: Corsico, Andrea, et al.
Published: (2025)

A Biophysical-Model-Informed Source Separation Framework For EMG Decomposition
by: Halatsis, D., et al.
Published: (2025)

Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence
by: Singh, Chaitanya
Published: (2023)

Meta-learning ecological priors from large language models explains human learning and decision making
by: Jagadish, Akshay K., et al.
Published: (2025)

Mapping Neural Theories of Consciousness onto the Common Model of Cognition
by: Rosenbloom, Paul S., et al.
Published: (2025)

A Modular Theory of Subjective Consciousness for Natural and Artificial Minds
by: Gillon, Michaël
Published: (2025)

Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability
by: Kuo, Po-Chen, et al.
Published: (2025)

Hierarchy or Heterarchy? A Theory of Long-Range Connections for the Sensorimotor Brain
by: Hawkins, Jeff, et al.
Published: (2025)

Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory
by: Varona, Miguel de Llanza, et al.
Published: (2024)

A Case for AI Consciousness: Language Agents and Global Workspace Theory
by: Goldstein, Simon, et al.
Published: (2024)

The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition
by: Pichat, Michael, et al.
Published: (2025)

RTify: Aligning Deep Neural Networks with Human Behavioral Decisions
by: Cheng, Yu-Ang, et al.
Published: (2024)

Deep Jansen-Rit Parameter Inference for Model-Driven Analysis of Brain Activity
by: Tilwani, Deepa, et al.
Published: (2024)

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow
by: He, Dongyi, et al.
Published: (2026)

Re-assessing the evidence for mental rotation abilities in children using computational models
by: Aubret, Arthur, et al.
Published: (2025)

Possible Principles for Aligned Structure Learning Agents
by: Da Costa, Lancelot, et al.
Published: (2024)

How Much is Brain Data Worth for Machine Learning?
by: Lewis, Lane, et al.
Published: (2026)

Exploring a Cognitive Architecture for Learning Arithmetic Equations
by: Gawin, Cole
Published: (2024)

Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers
by: Penchala, Sindhuja, et al.
Published: (2025)

Semi-supervised Multimodal Representation Learning through a Global Workspace
by: Devillers, Benjamin, et al.
Published: (2023)

A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness
by: Hoel, Erik
Published: (2025)

The Subject of Emergent Misalignment in Superintelligence: An Anthropological, Cognitive Neuropsychological, Machine-Learning, and Ontological Perspective
by: Imran, Muhammad Osama, et al.
Published: (2025)

GATE: Adaptive Learning with Working Memory by Information Gating in Multi-lamellar Hippocampal Formation
by: Liu, Yuechen, et al.
Published: (2025)

Learning Discrete Successor Transitions in Continuous Attractor Networks: Emergence, Limits, and Topological Constraints
by: Brownell, Daniel
Published: (2026)

CRCC: Contrast-Based Robust Cross-Subject and Cross-Site Representation Learning for EEG
by: Wong, Xiaobin, et al.
Published: (2026)

Deep Learning Inductive Biases for fMRI Time Series Classification during Resting-state and Movie-watching
by: Khodabandehloo, Behdad, et al.
Published: (2025)

Deep Learning without Weight Symmetry
by: Ji-An, Li, et al.
Published: (2024)

Bridging Critical Gaps in Convergent Learning: How Representational Alignment Evolves Across Layers, Training, and Distribution Shifts
by: Kapoor, Chaitanya, et al.
Published: (2025)

Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders
by: Hu, Jinlong, et al.
Published: (2026)

Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention for Cognitive and Psychiatric Applications
by: Bae, Sangyoon, et al.
Published: (2025)

Critical Learning Periods Emerge Even in Deep Linear Networks
by: Kleinman, Michael, et al.
Published: (2023)

ML-PWS: Estimating the Mutual Information Between Experimental Time Series Using Neural Networks
by: Reinhardt, Manuel, et al.
Published: (2025)