·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Learning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance6h◆Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability6h◆Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems6h◆Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs6h◆AutoTrace: From Patches to Triggers via Agentic Interprocedural Exploration6h◆Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap6h◆The Benjamini--Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests6h◆Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel6h◆Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles6h◆ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories6h◆Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy6h◆Keep Policy Gradient in Charge: Sibling-Guided Credit Distillation for Long-Horizon Tool-Use Agents6h◆Prime Fourier Embeddings: A Principled Basis for Modular Arithmetic6h◆PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition6h◆Git-Assistant: Planning-Based Support for Updating Git Repositories6h◆ReflectWorld-MM: An Entity-Oriented Multimodal Memory System for Open-Ended Video Streams6h◆MAGE: Understanding Stability-Performance Trade-offs in Multi-component Prompt Optimization6h◆A Shared Subcircuit Lets LLMs Count Down Across Tasks6h◆Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction6h◆From Words to Widgets for Controllable LLM Generation6h◆Learning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance6h◆Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability6h◆Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems6h◆Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs6h◆AutoTrace: From Patches to Triggers via Agentic Interprocedural Exploration6h◆Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap6h◆The Benjamini--Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests6h◆Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel6h◆Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles6h◆ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories6h◆Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy6h◆Keep Policy Gradient in Charge: Sibling-Guided Credit Distillation for Long-Horizon Tool-Use Agents6h◆Prime Fourier Embeddings: A Principled Basis for Modular Arithmetic6h◆PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition6h◆Git-Assistant: Planning-Based Support for Updating Git Repositories6h◆ReflectWorld-MM: An Entity-Oriented Multimodal Memory System for Open-Ended Video Streams6h◆MAGE: Understanding Stability-Performance Trade-offs in Multi-component Prompt Optimization6h◆A Shared Subcircuit Lets LLMs Count Down Across Tasks6h◆Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction6h◆From Words to Widgets for Controllable LLM Generation6h◆
News/Exact Convex Reformulations of Linear Neural Networks via Completely Positive Lifting
arxiv
PublishedMay 19, 2026 at 4:00 AM

Exact Convex Reformulations of Linear Neural Networks via Completely Positive Lifting

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

arXiv:2605.17692v1 Announce Type: new Abstract: We show that the training problem of a deep linear neural network under the squared loss admits an exact convex reformulation in a lifted space over a generalized completely positive cone. The reformulation has the same optimal value as the original no

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
arxivLearning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance6harxivEvaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability6harxivSelf-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems6harxivAre we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs6h
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