·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Capacity and Redundancy Trade-offs in Multi-Task Learning6h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation6h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making6h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection6h◆Supervised Reward Inference6h◆SWE-Pruner Pro: The Coder LLM Already Knows What to Prune6h◆Can Multimodal Large Language Models Understand OCT?6h◆It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation6h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization6h◆Mobius Learning: Cyclic Depth Folding in Transformers6h◆AlphaOracle: Oracle bone script decipherment via human-workflow-inspired deep learning6h◆Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field6h◆FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering6h◆LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks6h◆Octopus: On-device language model for function calling of software APIs6h◆A Survey on the Verification of Reinforcement Learning Policies6h◆RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts6h◆Lomekwi: Resource-Bounded Tool Discovery in LLM Agents6h◆Expected Free Energy as Belief-Dependent Utility for rho-POMDPs6h◆Constrained Path Reasoning: Measuring When Committed Stages Earn Their Cost6h◆Capacity and Redundancy Trade-offs in Multi-Task Learning6h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation6h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making6h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection6h◆Supervised Reward Inference6h◆SWE-Pruner Pro: The Coder LLM Already Knows What to Prune6h◆Can Multimodal Large Language Models Understand OCT?6h◆It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation6h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization6h◆Mobius Learning: Cyclic Depth Folding in Transformers6h◆AlphaOracle: Oracle bone script decipherment via human-workflow-inspired deep learning6h◆Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field6h◆FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering6h◆LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks6h◆Octopus: On-device language model for function calling of software APIs6h◆A Survey on the Verification of Reinforcement Learning Policies6h◆RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts6h◆Lomekwi: Resource-Bounded Tool Discovery in LLM Agents6h◆Expected Free Energy as Belief-Dependent Utility for rho-POMDPs6h◆Constrained Path Reasoning: Measuring When Committed Stages Earn Their Cost6h◆
News/Quantizing Whisper-small: How design choices affect ASR performance
arxiv
PublishedMay 22, 2026 at 4:00 AM
—neutral

Quantizing Whisper-small: How design choices affect ASR performance

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

arXiv:2511.08093v2 Announce Type: replace-cross Abstract: Large speech recognition models like Whisper-small achieve high accuracy but are difficult to deploy on edge devices due to their high computational demand. To this end, we present a unified, cross-library evaluation of post-training quantiza

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
arxivCapacity and Redundancy Trade-offs in Multi-Task Learning6harxivPredictive Training with Latent Imagination for Visual Quadruped Navigation6harxivWhere Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making6harxivDid We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection6h
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