·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Claude Fable won’t answer basic biology questions2h◆Microsoft, like, totally gets why students are booing AI-pilled graduation speakers3h◆The future of AI regulation is courting the strangest, most anxious bedfellows3h◆Google won’t just admit it’s feeding YouTube creators to its music AI3h◆‘AI-pilled’ firms spend $7,500 per employee each month on AI4h◆Microsoft restricts Claude Fable for employees over data retention concerns4h◆Google will save your Lens photos, Search Live recordings, and Translate audio for AI training4h◆How memory tools can make AI models worse5h◆Cybersecurity researchers aren’t happy about the guardrails on Anthropic’s Fable5h◆Datadog veterans launch AI coding startup Niteshift on a bet against Big AI lock-in6h◆The three hard-tech moonshots fueling SpaceX’s unbelievable IPO6h◆Warner Music acquires AI attribution startup Sureel AI6h◆Jedify raises $24M to help companies arm AI agents with context on their business7h◆Decart’s new world model can simulate hours of photorealistic driving — with some caveats8h◆Meta signs first AI data center deal in India with Reliance14h◆BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression17h◆Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning17h◆Integral Field Unit Spectroscopy with One Fiber17h◆AMEL: Accumulated Message Effects on LLM Judgments17h◆Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models17h◆Claude Fable won’t answer basic biology questions2h◆Microsoft, like, totally gets why students are booing AI-pilled graduation speakers3h◆The future of AI regulation is courting the strangest, most anxious bedfellows3h◆Google won’t just admit it’s feeding YouTube creators to its music AI3h◆‘AI-pilled’ firms spend $7,500 per employee each month on AI4h◆Microsoft restricts Claude Fable for employees over data retention concerns4h◆Google will save your Lens photos, Search Live recordings, and Translate audio for AI training4h◆How memory tools can make AI models worse5h◆Cybersecurity researchers aren’t happy about the guardrails on Anthropic’s Fable5h◆Datadog veterans launch AI coding startup Niteshift on a bet against Big AI lock-in6h◆The three hard-tech moonshots fueling SpaceX’s unbelievable IPO6h◆Warner Music acquires AI attribution startup Sureel AI6h◆Jedify raises $24M to help companies arm AI agents with context on their business7h◆Decart’s new world model can simulate hours of photorealistic driving — with some caveats8h◆Meta signs first AI data center deal in India with Reliance14h◆BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression17h◆Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning17h◆Integral Field Unit Spectroscopy with One Fiber17h◆AMEL: Accumulated Message Effects on LLM Judgments17h◆Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models17h◆
News/Minimum Distortion Quantization with Specified Output Distribution
arxiv
PublishedJune 10, 2026 at 4:00 AM

Minimum Distortion Quantization with Specified Output Distribution

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

arXiv:2606.10458v1 Announce Type: cross Abstract: We derive the optimal quantizer of a real-valued random variable $W$ with distribution $P_W$ such that 1) the distribution of the quantization output $X$ that can take $k$ values follows any specified distribution $P_X$ over $\{1,\ldots,k\}$, and 2)

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
arxivBiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression17harxivFisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning17harxivIntegral Field Unit Spectroscopy with One Fiber17harxivAMEL: Accumulated Message Effects on LLM Judgments17h
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