Clustering in Deep Stochastic Transformers
Fuente:
arXiv
Saved in:
| Main Authors: | Fedorov, Lev, Sander, Michaël E., Elie, Romuald, Marion, Pierre, Laurière, Mathieu |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Recent Developments in Machine Learning Methods for Stochastic Control and Games
by: Hu, Ruimeng, et al.
Published: (2023)
by: Hu, Ruimeng, et al.
Published: (2023)
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
by: Hua, Mengjian, et al.
Published: (2024)
by: Hua, Mengjian, et al.
Published: (2024)
Deep Learning for the Multiple Optimal Stopping Problem
by: Laurière, Mathieu, et al.
Published: (2025)
by: Laurière, Mathieu, et al.
Published: (2025)
Learning to Stop: Deep Learning for Mean Field Optimal Stopping
by: Magnino, Lorenzo, et al.
Published: (2024)
by: Magnino, Lorenzo, et al.
Published: (2024)
Deep Backward and Galerkin Methods for the Finite State Master Equation
by: Cohen, Asaf, et al.
Published: (2024)
by: Cohen, Asaf, et al.
Published: (2024)
Optimal Stopping in Latent Diffusion Models
by: Wu, Yu-Han, et al.
Published: (2025)
by: Wu, Yu-Han, et al.
Published: (2025)
Learning in Mean Field Games: A Survey
by: Laurière, Mathieu, et al.
Published: (2022)
by: Laurière, Mathieu, et al.
Published: (2022)
Population-Aware Imitation Learning in Mean-field Games with Common Noise
by: Lambrecht, Grégoire, et al.
Published: (2026)
by: Lambrecht, Grégoire, et al.
Published: (2026)
Convergence of Actor-Critic Learning for Mean Field Games and Mean Field Control in Continuous Spaces
by: Fouque, Jean-Pierre, et al.
Published: (2025)
by: Fouque, Jean-Pierre, et al.
Published: (2025)
MIND: Monge Inception Distance for Generative Models Evaluation
by: Berthet, Quentin, et al.
Published: (2026)
by: Berthet, Quentin, et al.
Published: (2026)
A Deep Learning Method for Optimal Investment Under Relative Performance Criteria Among Heterogeneous Agents
by: Laurière, Mathieu, et al.
Published: (2024)
by: Laurière, Mathieu, et al.
Published: (2024)
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
by: Carmona, René, et al.
Published: (2019)
by: Carmona, René, et al.
Published: (2019)
Fair Active Learning: Solving the Labeling Problem in Insurance
by: Elie, Romuald, et al.
Published: (2021)
by: Elie, Romuald, et al.
Published: (2021)
Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games
by: Angiuli, Andrea, et al.
Published: (2024)
by: Angiuli, Andrea, et al.
Published: (2024)
Connecting GANs, MFGs, and OT
by: Cao, Haoyang, et al.
Published: (2020)
by: Cao, Haoyang, et al.
Published: (2020)
How do Transformers perform In-Context Autoregressive Learning?
by: Sander, Michael E., et al.
Published: (2024)
by: Sander, Michael E., et al.
Published: (2024)
Implicit regularization of deep residual networks towards neural ODEs
by: Marion, Pierre, et al.
Published: (2023)
by: Marion, Pierre, et al.
Published: (2023)
Fair regression under localized demographic parity constraints
by: Charpentier, Arthur, et al.
Published: (2026)
by: Charpentier, Arthur, et al.
Published: (2026)
Reinforcement Learning for Finite Space Mean-Field Type Games
by: Shao, Kai, et al.
Published: (2024)
by: Shao, Kai, et al.
Published: (2024)
Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning
by: Wu, Zida, et al.
Published: (2025)
by: Wu, Zida, et al.
Published: (2025)
Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
by: Magnino, Lorenzo, et al.
Published: (2025)
by: Magnino, Lorenzo, et al.
Published: (2025)
Towards Understanding the Universality of Transformers for Next-Token Prediction
by: Sander, Michael E., et al.
Published: (2024)
by: Sander, Michael E., et al.
Published: (2024)
Global Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models
by: Gu, Zhouzhou, et al.
Published: (2024)
by: Gu, Zhouzhou, et al.
Published: (2024)
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games
by: Ocello, Antonio, et al.
Published: (2025)
by: Ocello, Antonio, et al.
Published: (2025)
Deep linear networks for regression are implicitly regularized towards flat minima
by: Marion, Pierre, et al.
Published: (2024)
by: Marion, Pierre, et al.
Published: (2024)
Joint Learning of Energy-based Models and their Partition Function
by: Sander, Michael E., et al.
Published: (2025)
by: Sander, Michael E., et al.
Published: (2025)
Control Variate Score Matching for Diffusion Models
by: Kahouli, Khaled, et al.
Published: (2025)
by: Kahouli, Khaled, et al.
Published: (2025)
Dimension-free error estimate for diffusion model and optimal scheduling
by: de Bortoli, Valentin, et al.
Published: (2025)
by: de Bortoli, Valentin, et al.
Published: (2025)
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
by: Marion, Pierre, et al.
Published: (2024)
by: Marion, Pierre, et al.
Published: (2024)
Differentiable Knapsack and Top-k Operators via Dynamic Programming
by: Vivier-Ardisson, Germain, et al.
Published: (2026)
by: Vivier-Ardisson, Germain, et al.
Published: (2026)
Loss Functions and Operators Generated by f-Divergences
by: Roulet, Vincent, et al.
Published: (2025)
by: Roulet, Vincent, et al.
Published: (2025)
Stochastic Deep Graph Clustering for Practical Group Formation
by: Park, Junhyung, et al.
Published: (2025)
by: Park, Junhyung, et al.
Published: (2025)
Diversity-Rewarded CFG Distillation
by: Cideron, Geoffrey, et al.
Published: (2024)
by: Cideron, Geoffrey, et al.
Published: (2024)
Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
by: Magnino, Lorenzo, et al.
Published: (2026)
by: Magnino, Lorenzo, et al.
Published: (2026)
Operator Learning for Families of Finite-State Mean-Field Games
by: Hofgard, William, et al.
Published: (2026)
by: Hofgard, William, et al.
Published: (2026)
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
by: Wu, Zida, et al.
Published: (2024)
by: Wu, Zida, et al.
Published: (2024)
Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction
by: Blondel, Mathieu, et al.
Published: (2025)
by: Blondel, Mathieu, et al.
Published: (2025)
Training Transformers for Mesh-Based Simulations
by: Garnier, Paul, et al.
Published: (2025)
by: Garnier, Paul, et al.
Published: (2025)
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective
by: Zaman, Muhammad Aneeq uz, et al.
Published: (2024)
by: Zaman, Muhammad Aneeq uz, et al.
Published: (2024)
Stochastic Scaling Limits and Synchronization by Noise in Deep Transformer Models
by: Agazzi, Andrea, et al.
Published: (2026)
by: Agazzi, Andrea, et al.
Published: (2026)
Similar Items
-
Recent Developments in Machine Learning Methods for Stochastic Control and Games
by: Hu, Ruimeng, et al.
Published: (2023) -
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
by: Hua, Mengjian, et al.
Published: (2024) -
Deep Learning for the Multiple Optimal Stopping Problem
by: Laurière, Mathieu, et al.
Published: (2025) -
Learning to Stop: Deep Learning for Mean Field Optimal Stopping
by: Magnino, Lorenzo, et al.
Published: (2024) -
Deep Backward and Galerkin Methods for the Finite State Master Equation
by: Cohen, Asaf, et al.
Published: (2024)