·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Amazon responds to data center backlash, says it no longer uses NDAs3h◆Capcom is preparing for a ‘future where we create games together with AI’4h◆OpenAI safety employee resigns, claiming the company’s ‘culture is broken’5h◆Splice CEO Kakul Srivastava thinks AI emails are killing conversations6h◆An OpenAI safety employee has quit and is sounding the alarm7h◆All the AI agents that can live in your text messages7h◆Stochastic Optimal Control for Continuous-Time fMRI Representation Learning17h◆Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning17h◆Trajectory Stitching for Solving Inverse Problems with Flow-Based Models17h◆Sequential Capacity of Quantum Processes with Finite Memory17h◆Q-MINO: A Minimal-Norm Method for Quantization-Aware Training17h◆The Price of Correlated Tests: How Strict Should a Model Release Gate Be?17h◆Open Vocabulary Word Recognition From Transcribed Bangla Texts17h◆Geometry-Dependent Bounds for Online Non-Monotone DR-Submodular Maximization17h◆Scaling Collider Event Generation with Residual-Quantized Tokens17h◆TANGO: Treating Tokens as Operators17h◆Oblivious Learning and Collusive Pricing17h◆In-context Learning of Single-index Targets: Comparing Kernel and Feature Learners17h◆CF-JEPA: Improving Robustness of JEPA World Models via Controllability Factorization17h◆AF-Muon: An AdamW-Free Muon Optimizer for Tied-Embedding Models17h◆Amazon responds to data center backlash, says it no longer uses NDAs3h◆Capcom is preparing for a ‘future where we create games together with AI’4h◆OpenAI safety employee resigns, claiming the company’s ‘culture is broken’5h◆Splice CEO Kakul Srivastava thinks AI emails are killing conversations6h◆An OpenAI safety employee has quit and is sounding the alarm7h◆All the AI agents that can live in your text messages7h◆Stochastic Optimal Control for Continuous-Time fMRI Representation Learning17h◆Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning17h◆Trajectory Stitching for Solving Inverse Problems with Flow-Based Models17h◆Sequential Capacity of Quantum Processes with Finite Memory17h◆Q-MINO: A Minimal-Norm Method for Quantization-Aware Training17h◆The Price of Correlated Tests: How Strict Should a Model Release Gate Be?17h◆Open Vocabulary Word Recognition From Transcribed Bangla Texts17h◆Geometry-Dependent Bounds for Online Non-Monotone DR-Submodular Maximization17h◆Scaling Collider Event Generation with Residual-Quantized Tokens17h◆TANGO: Treating Tokens as Operators17h◆Oblivious Learning and Collusive Pricing17h◆In-context Learning of Single-index Targets: Comparing Kernel and Feature Learners17h◆CF-JEPA: Improving Robustness of JEPA World Models via Controllability Factorization17h◆AF-Muon: An AdamW-Free Muon Optimizer for Tied-Embedding Models17h◆
News/Stochastic Optimal Control for Continuous-Time fMRI Representation Learning
arxiv
PublishedOctober 3, 2026 at 4:00 AM
—neutral

Stochastic Optimal Control for Continuous-Time fMRI Representation Learning

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2502.04892v2 Announce Type: replace Abstract: Learning robust representations from functional magnetic resonance imaging (fMRI) is fundamentally challenged by the temporal irregularity and noise inherent in data from heterogeneous sources. Existing self-supervised learning (SSL) methods often

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivExploiting Exogenous Structure for Sample-Efficient Reinforcement Learning17harxivTrajectory Stitching for Solving Inverse Problems with Flow-Based Models17harxivSequential Capacity of Quantum Processes with Finite Memory17harxivQ-MINO: A Minimal-Norm Method for Quantization-Aware Training17h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
Built by Marouane Gazouzi
HomeModelsNews