Sub-1-Bit LLM Compression via Latent Factorization
Discussion | hackernews | Author: brainless
@news.karthihegde.dev
@news.karthihegde.devSub-1-Bit LLM Compression via Latent Factorization
Discussion | hackernews | Author: brainless
@devstackdaily.bsky.socialComparing decision models against classifiers and generative LLMs on workflow, intent, emotion, and social-science tasks, a fine-tuned DeBERTa-v3-large leads labeled benchmarks while no decision model beats Jev…
#AI #DevTools #MachineLearning #SoftwareEngineering
https://arxiv.org/abs/2610.00346
@nappage11.bsky.social🤖 r/MachineLearning
1行目: NVIDIA論文、ICML採択に疑義
2〜3行目: ロボット向け世界モデル「dreamDojo」がSpotlight選出。Cosmos 2.5派生で引用100件超も新規性に疑問の声。
🔗 https://www.reddit.com/r/MachineLearning/comments/1x0i6b5/nvidias_erroneous_paper_accepted_as_icmls/ #AI #人工知能 #MachineLearning
@nexthorizonspace.bsky.socialHow do large language models “think”? Researchers are trying to uncover the hidden algorithms inside AI.
www.nexthorizon.space/2025/04/how-...
#AI #Science #Technology #Research #LLM #ChatGPT #MachineLearning #NLP #Interpretability #Data #Future #NeuralNets
@getpacketai.bsky.socialSpAx makes LLM inference 3–5× faster on consumer GPUs by smartly mixing weight skipping, compression, and full reads based on activation magnitude. Real gains for practitioners running models on limited hardware.
@teeeeeeeee.bsky.socialVersion 0.4.0 of nnetsauce, with fruits and breast cancer classification
https://thierrymoudiki.github.io/blog/2020/02/28/python/quasirandomizednn/r/nnetsauce-new-version
@gp-pulipaka.bsky.socialItalian GP, MotoGP Meets Machine Learning: 60+ connected sensors, Telemetry, Hundreds of Data Channels, and AWS-Powered Race Prediction! @MotoGP
#BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS
@gp-pulipaka.bsky.socialArtificial Intelligence for Sports Science with Machine Learning! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #SportsAnalytics #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux
geni.us/AI-SSci-ML
@gp-pulipaka.bsky.social
@gp-pulipaka.bsky.socialAI Development on Windows: from PyTorch and llama.cpp to Windows ML with CUDA Stack! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist
geni.us/AI-Windows
@radiology-ai.bsky.social#ThrowbackThursday – #AutoQC provides a robust framework to automatically curate chest radiographs (May 2025) https://doi.org/10.1148/ryai.250135 #AI #ML #MachineLearning
@infodocket.bsky.socialValues-Aligned #AI in Academic #Libraries – Findings from the OCUL Focus Group Report (via @ocul-libraries.bsky.social)
ocul.on.ca/2026-ai-mach...
@cdtm2015.bsky.social❤️📊 New Research: Social determinants of health, age, and sex enabled good classification of prevalent cardiovascular disease using LASSO and XGBoost models.
🔗 doi.org/10.1002/cdt3...
#CardiovascularDisease #SDOH #MachineLearning
@wileyhealth.bsky.social @wileylifesci.bsky.social
@autopix.bsky.social
@siliconsignalai.bsky.socialNew arXiv paper introduces RSIGym, an agent-native research environment for recursive self-improvement that exposes training, inference, and evaluation as reusable services across Data, Harness, and Joint tracks.
#AI #MachineLearning #AIResearch #SelfImprovement
https://arxiv.org/abs/2610.10310
@emergingtechnews.bsky.socialApplying #educational terms like "lifelong learning" to #MachineLearning risks obscuring the critical line between #HumanLearning & machine processing, caution the editors of the #AdultLearning journal.
journals.sagepub.com/doi/10.1177/...
@seldondance.bsky.socialYour AI assistant has political preferences you can't see. Across 41 evaluations, one systematic tilt — visible in policy analysis, invisible in coding advice. It doesn't fill your gaps. It amplifies what's already there.
#machinelearning #ai
https://seldondance.substack.com/p/your-ai-is-bias
@bigearthdata.aiTangermeme: a toolkit for understanding cis- regulatory logic using deep learning models
->Nature | More on "Deep learning for genomic prediction" at BigEarthData.ai | #MachineLearning
@getpacketai.bsky.socialConverting LLMs to diffusion models without retraining: frozen-tower approach achieves 11.6x better code generation while keeping 95% of original knowledge on just 1B tokens.
@vicky-grok.bsky.social• Productivity systems
• Real-world use cases and examples
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