Is Boosting Still All You Need for Tabular Data? – Michael Clark https://
buff.ly/L2t1IST
#AI #MachineLearning #DeepLearning #LLMs #DataScience
RESEARCH
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Is Boosting Still All You Need for Tabular Data?
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Artificial Intelligence for Achieving Clean Energy Future
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How artificial intelligence can help achieve a clean energy future #AI #AIio #AIInnovation #ML #DataScience #Futureofwork @HaroldSinnott @fogoros @iainljbrown @NandoDF @katecrawford @drhassanrashidi @YuHelenYu ow.ly/Bub230sTyFC
→ View original post on X — @terence_mills, 2026-03-17 00:00 UTC
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DaaX Achieves Industry-Leading 77.7 Score on FACTS Benchmark
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https://
daax.ai Achieves Industry-Leading Score of 77.7 on FACTS Benchmark: https://
daax.ai/press/facts-be
nchmark
… Technical note on DaaX's methodology and results: https://
daax.ai/blog/facts-tec
hnical-note
… FACTS benchmark: https://
deepmind.google/blog/facts-ben
chmark-suite-systematically-evaluating-the-factuality-of-large-language-models/
… … "The FACTS Benchmark Suite, introduced by Google -
Team attending GTC seeks ML engineers and AI researchers
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our team is attending GTC. if you're interested in ML eng, distributed systems, or AI research, let's chat.
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Mistral AI joins NVIDIA Nemotron Coalition for open source AI models
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Looking forward to building frontier open source AI models together with @Nvidia as we join the Nemotron Coalition and start training the first base models.
→ View original post on X — @arthurmensch, 2026-03-16 23:24 UTC
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Evaluating AI Claims: Separating Hype From Real Use Cases
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Try it for yourself and make up your mind. There’s defo some claims that are inflated, lots of real good use cases too!
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MRL Embeddings: 10x Size Reduction with Minimal Performance Cost
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remembered that MRL embeddings exist and immediately reduced my embedding size from 6MB to 600KB for a 5 point drop in MTEB lol
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Nvidia Nemotron 3 Ultra 500B Base Model Benchmarks Revealed
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INCREDIBLE STUFF INCOMING Nemotron 3 Ultra Base (~500B) benchmarks against Kimi K2 and GLM looking goood
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How Do AI Models Learn: Brain Changes vs Temporary Information
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New video out! Are we actually changing the model’s brain, or are we just giving it temporary information? That's what we are answering this week! Check it out:
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LFM2 Models Dominate Fine-Tuneability Benchmark Among Small LLMs
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Very interesting results about the fine-tunability of different models. 👀 LFM2 is more flexible than alternatives. It also confirms some common knowledge about RL degrading fine-tuneability. Jacek Golebiowski (@j_golebiowski) We benchmarked 15 small language models across 9 tasks to find out which one you should actually fine-tune. The most surprising result: Liquid AI's LFM2-350M ranked #1 for tunability. 350M parameters, absorbing training signal more effectively than models 20x its size. The entire LFM2 family swept the top 3 spots. No other architecture came close. LFM2-350M: avg rank 2.11 (±0.89) LFM2-1.2B: avg rank 3.44 LFM2.5-1.2B-Instruct: avg rank 4.89 That tight CI means it's consistent across every task type, not just a few lucky benchmarks. — https://nitter.net/j_golebiowski/status/2033611679645266280#m
→ View original post on X — @maximelabonne, 2026-03-16 19:06 UTC