Copertina

Is Mamba Capable of In-Context Learning?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Grazzi, Riccardo, Siems, Julien, Schrodi, Simon, Brox, Thomas, Hutter, Frank
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
di: Siems, Julien, et al.
Pubblicazione: (2025)

Towards Understanding Subliminal Learning: When and How Hidden Biases Transfer
di: Schrodi, Simon, et al.
Pubblicazione: (2025)

Learning State-Tracking from Code Using Linear RNNs
di: Siems, Julien, et al.
Pubblicazione: (2026)

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues
di: Grazzi, Riccardo, et al.
Pubblicazione: (2024)

Concept Bottleneck Models Without Predefined Concepts
di: Schrodi, Simon, et al.
Pubblicazione: (2024)

When and How Does CLIP Enable Domain and Compositional Generalization?
di: Kempf, Elias, et al.
Pubblicazione: (2025)

Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models
di: Bhethanabhotla, Sathya Kamesh, et al.
Pubblicazione: (2024)

Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models
di: Schrodi, Simon, et al.
Pubblicazione: (2024)

Simple LLM Baselines are Competitive for Model Diffing
di: Kempf, Elias, et al.
Pubblicazione: (2026)

TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
di: Moroshan, Vladyslav, et al.
Pubblicazione: (2025)

Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems
di: Hoffmann, David T., et al.
Pubblicazione: (2023)

GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning
di: Mueller, Andreas, et al.
Pubblicazione: (2024)

What Drives Compositional Generalization? The Importance of Continuous Training Objectives in Visual Generative Models
di: Farid, Karim, et al.
Pubblicazione: (2025)

Convergence Properties of Stochastic Hypergradients
di: Grazzi, Riccardo, et al.
Pubblicazione: (2020)

Early Stopping Tabular In-Context Learning
di: Küken, Jaris, et al.
Pubblicazione: (2025)

Bayes' Power for Explaining In-Context Learning Generalizations
di: Müller, Samuel, et al.
Pubblicazione: (2024)

Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
di: Grazzi, Riccardo, et al.
Pubblicazione: (2024)

OptRot: Mitigating Weight Outliers via Data-Free Rotations for Post-Training Quantization
di: Gadhikar, Advait, et al.
Pubblicazione: (2025)

Learning invariant representations of time-homogeneous stochastic dynamical systems
di: Kostic, Vladimir R., et al.
Pubblicazione: (2023)

Do-PFN: In-Context Learning for Causal Effect Estimation
di: Robertson, Jake, et al.
Pubblicazione: (2025)

Balancing Synthetic Data and Replay for Enhancing Task-Specific Capabilities
di: Spiegelhalter, Urs, et al.
Pubblicazione: (2025)

Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
di: Ging, Simon, et al.
Pubblicazione: (2024)

Increasing LLM Coding Capabilities through Diverse Synthetic Coding Tasks
di: Abed, Amal, et al.
Pubblicazione: (2025)

Constrained Reinforcement Learning for Safe Heat Pump Control
di: Zhang, Baohe, et al.
Pubblicazione: (2024)

Unlocking In-Context Learning for Natural Datasets Beyond Language Modelling
di: Bratulić, Jelena, et al.
Pubblicazione: (2025)

Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
di: Park, Jongho, et al.
Pubblicazione: (2024)

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
di: Rakotoarison, Herilalaina, et al.
Pubblicazione: (2024)

Investigation into In-Context Learning Capabilities of Transformers
di: Chandrupatla, Rushil, et al.
Pubblicazione: (2026)

Can Mamba Learn In Context with Outliers? A Theoretical Generalization Analysis
di: Li, Hongkang, et al.
Pubblicazione: (2025)

Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
di: Helli, Kai, et al.
Pubblicazione: (2024)

Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
di: Siems, Julien, et al.
Pubblicazione: (2023)

Mamba Can Learn Low-Dimensional Targets In-Context via Test-Time Feature Learning
di: Oh, Junsoo, et al.
Pubblicazione: (2025)

Self-Correcting Bayesian Optimization through Bayesian Active Learning
di: Hvarfner, Carl, et al.
Pubblicazione: (2023)

Constrained Reinforcement Learning with Smoothed Log Barrier Function
di: Zhang, Baohe, et al.
Pubblicazione: (2024)

CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
di: Bhatt, Aditya, et al.
Pubblicazione: (2019)

c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization
di: Watanabe, Shuhei, et al.
Pubblicazione: (2022)

On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
di: Liu, Renpu, et al.
Pubblicazione: (2024)

Levin Tree Search with Context Models
di: Orseau, Laurent, et al.
Pubblicazione: (2023)

A General Framework for User-Guided Bayesian Optimization
di: Hvarfner, Carl, et al.
Pubblicazione: (2023)

Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
di: Bühler, Magnus, et al.
Pubblicazione: (2026)