·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics1h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models7h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders7h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA7h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging7h◆Multi-Mask Diffusion Language Models for Few-Step Generation7h◆Solar Open 2 Technical Report7h◆The Geometry of Personality: Activation Steering with Jungian Cognitive Functions7h◆Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning7h◆H$^2$SD: Hybrid Hindsight Self-Distillation7h◆LunarFM: A Shared Multimodal Representation of the Moon's Surface7h◆Prior laundering: learned priors with inherited, undetectable overconfidence7h◆Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery7h◆Prompt as a Data Type: In-Database LLM Prompt Management and Rewriting7h◆CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference7h◆Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support7h◆Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models7h◆A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data7h◆Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics7h◆Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO7h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics1h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models7h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders7h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA7h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging7h◆Multi-Mask Diffusion Language Models for Few-Step Generation7h◆Solar Open 2 Technical Report7h◆The Geometry of Personality: Activation Steering with Jungian Cognitive Functions7h◆Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning7h◆H$^2$SD: Hybrid Hindsight Self-Distillation7h◆LunarFM: A Shared Multimodal Representation of the Moon's Surface7h◆Prior laundering: learned priors with inherited, undetectable overconfidence7h◆Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery7h◆Prompt as a Data Type: In-Database LLM Prompt Management and Rewriting7h◆CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference7h◆Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support7h◆Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models7h◆A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data7h◆Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics7h◆Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO7h◆
News/Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks
arxiv
PublishedJuly 27, 2026 at 4:00 AM
—neutral

Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks

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

arXiv:2606.14954v4 Announce Type: replace-cross Abstract: We develop a general framework for analyzing representation costs induced by parameter-space regularizers in data-fitting methods. For an arbitrary parametric method, we define its representation cost and native function space, prove existenc

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
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models7harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders7harxivData Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA7harxivEnjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging7h
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 ↗
HomeModelsNews