In-context learning and Occam's razor
Fuente:
arXiv
Saved in:
| Main Authors: | Elmoznino, Eric, Marty, Tom, Kasetty, Tejas, Gagnon, Leo, Mittal, Sarthak, Fathi, Mahan, Sridhar, Dhanya, Lajoie, Guillaume |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective
by: Gagnon, Leo, et al.
Published: (2025)
by: Gagnon, Leo, et al.
Published: (2025)
Does learning the right latent variables necessarily improve in-context learning?
by: Mittal, Sarthak, et al.
Published: (2024)
by: Mittal, Sarthak, et al.
Published: (2024)
A Compression Perspective on Simplicity Bias
by: Marty, Tom, et al.
Published: (2026)
by: Marty, Tom, et al.
Published: (2026)
Beyond Distribution Sharpening: The Importance of Task Rewards
by: Mittal, Sarthak, et al.
Published: (2026)
by: Mittal, Sarthak, et al.
Published: (2026)
Evaluating Interventional Reasoning Capabilities of Large Language Models
by: Kasetty, Tejas, et al.
Published: (2024)
by: Kasetty, Tejas, et al.
Published: (2024)
Deep neural networks have an inbuilt Occam's razor
by: Mingard, Chris, et al.
Published: (2023)
by: Mingard, Chris, et al.
Published: (2023)
A Complexity-Based Theory of Compositionality
by: Elmoznino, Eric, et al.
Published: (2024)
by: Elmoznino, Eric, et al.
Published: (2024)
Iterative Amortized Inference: Unifying In-Context Learning and Learned Optimizers
by: Mittal, Sarthak, et al.
Published: (2025)
by: Mittal, Sarthak, et al.
Published: (2025)
In-Context Parametric Inference: Point or Distribution Estimators?
by: Mittal, Sarthak, et al.
Published: (2025)
by: Mittal, Sarthak, et al.
Published: (2025)
Discrete, compositional, and symbolic representations through attractor dynamics
by: Nam, Andrew, et al.
Published: (2023)
by: Nam, Andrew, et al.
Published: (2023)
Amortized In-Context Bayesian Posterior Estimation
by: Mittal, Sarthak, et al.
Published: (2025)
by: Mittal, Sarthak, et al.
Published: (2025)
Statistical learning theory and Occam's razor: The core argument
by: Sterkenburg, Tom F.
Published: (2023)
by: Sterkenburg, Tom F.
Published: (2023)
Sparse Shift Autoencoders for Identifying Concepts from Large Language Model Activations
by: Joshi, Shruti, et al.
Published: (2025)
by: Joshi, Shruti, et al.
Published: (2025)
Occam's model: Selecting simpler representations for better transferability estimation
by: Singh, Prabhant, et al.
Published: (2025)
by: Singh, Prabhant, et al.
Published: (2025)
Next-token pretraining implies in-context learning
by: Riechers, Paul M., et al.
Published: (2025)
by: Riechers, Paul M., et al.
Published: (2025)
Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?
by: Ibrahim, Mark, et al.
Published: (2024)
by: Ibrahim, Mark, et al.
Published: (2024)
Accelerating Training with Neuron Interaction and Nowcasting Networks
by: Knyazev, Boris, et al.
Published: (2024)
by: Knyazev, Boris, et al.
Published: (2024)
BoxRL-NNV: Boxed Refinement of Latin Hypercube Samples for Neural Network Verification
by: Das, Sarthak
Published: (2025)
by: Das, Sarthak
Published: (2025)
In-Context Occam's Razor: How Transformers Prefer Simpler Hypotheses on the Fly
by: Deora, Puneesh, et al.
Published: (2025)
by: Deora, Puneesh, et al.
Published: (2025)
From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
by: Mueller, Aaron, et al.
Published: (2025)
by: Mueller, Aaron, et al.
Published: (2025)
Analyzing limits for in-context learning
by: Naim, Omar, et al.
Published: (2025)
by: Naim, Omar, et al.
Published: (2025)
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step
by: Dugan, Owen, et al.
Published: (2024)
by: Dugan, Owen, et al.
Published: (2024)
JEDI: Jointly Embedded Inference of Neural Dynamics
by: Jamkhandi, Anirudh, et al.
Published: (2026)
by: Jamkhandi, Anirudh, et al.
Published: (2026)
GAIA: Categorical Foundations of Generative AI
by: Mahadevan, Sridhar
Published: (2024)
by: Mahadevan, Sridhar
Published: (2024)
Universal Imitation Games
by: Mahadevan, Sridhar
Published: (2024)
by: Mahadevan, Sridhar
Published: (2024)
Universal Reinforcement Learning in Coalgebras: Asynchronous Stochastic Computation via Conduction
by: Mahadevan, Sridhar
Published: (2025)
by: Mahadevan, Sridhar
Published: (2025)
Consciousness as a Functor
by: Mahadevan, Sridhar
Published: (2025)
by: Mahadevan, Sridhar
Published: (2025)
Multi-agent cooperation through learning-aware policy gradients
by: Meulemans, Alexander, et al.
Published: (2024)
by: Meulemans, Alexander, et al.
Published: (2024)
Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
by: Kobayashi, Seijin, et al.
Published: (2025)
by: Kobayashi, Seijin, et al.
Published: (2025)
Polynomial-Time Approximability of Constrained Reinforcement Learning
by: McMahan, Jeremy
Published: (2025)
by: McMahan, Jeremy
Published: (2025)
A deep learning and machine learning approach to predict neonatal death in the context of São Paulo
by: Raihan, Mohon, et al.
Published: (2025)
by: Raihan, Mohon, et al.
Published: (2025)
Robust agents learn causal world models
by: Richens, Jonathan, et al.
Published: (2024)
by: Richens, Jonathan, et al.
Published: (2024)
TSFM in-context learning for time-series classification of bearing-health status
by: Tokic, Michel, et al.
Published: (2025)
by: Tokic, Michel, et al.
Published: (2025)
Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver
by: Marty, Tom, et al.
Published: (2023)
by: Marty, Tom, et al.
Published: (2023)
Artificial Intelligence Ecosystem for Automating Self-Directed Teaching
by: Gotavade, Tejas Satish
Published: (2024)
by: Gotavade, Tejas Satish
Published: (2024)
In-context Learning of Evolving Data Streams with Tabular Foundational Models
by: Lourenço, Afonso, et al.
Published: (2025)
by: Lourenço, Afonso, et al.
Published: (2025)
Context selectivity with dynamic availability enables lifelong continual learning
by: Barry, Martin, et al.
Published: (2023)
by: Barry, Martin, et al.
Published: (2023)
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
by: Liu, Toni J. B., et al.
Published: (2024)
by: Liu, Toni J. B., et al.
Published: (2024)
SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels
by: Yann, Rui, et al.
Published: (2025)
by: Yann, Rui, et al.
Published: (2025)
Mixture Density Networks for Classification with an Application to Product Bundling
by: Gugulothu, Narendhar, et al.
Published: (2024)
by: Gugulothu, Narendhar, et al.
Published: (2024)
Similar Items
-
Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective
by: Gagnon, Leo, et al.
Published: (2025) -
Does learning the right latent variables necessarily improve in-context learning?
by: Mittal, Sarthak, et al.
Published: (2024) -
A Compression Perspective on Simplicity Bias
by: Marty, Tom, et al.
Published: (2026) -
Beyond Distribution Sharpening: The Importance of Task Rewards
by: Mittal, Sarthak, et al.
Published: (2026) -
Evaluating Interventional Reasoning Capabilities of Large Language Models
by: Kasetty, Tejas, et al.
Published: (2024)