I'll get straight to the point. We trained 2 new models. Like BERT, but modern. ModernBERT. Not some hypey GenAI thing, but a proper workhorse model, for retrieval, classification, etc. Real practical stuff. It's much faster, more accurate, longer context, and more useful. 🧵

It will also be extremely important to analyze the strengths and limitations of the new system. Here are some examples of tasks that o3 couldn't solve on high-compute settings (even as it was generating millions of CoT search tokens and consuming thousands of dollars of compute

Görücüye çıktık! Antropoloji, programcılık, ekoloji, edebiyat, sosyoloji, psikoloji, felsefe ve tabii ki matematik. Paylaşırsanız seviniriz. Kış programlarımız Aileler için Matematik Oyunları Kampı (4'üncü sınıflar) Tarih: 7-9 Şubat 2025 Hedef kitle: 4'üncü sınıflar Program ve

Are you interested in Language Models and Knowledge Graphs? Join the 3rd Workshop on Knowledge Base Construction from Pre-Trained Language Models (KBC-LM) @ ISWC 2025 (Nara, Japan)! Jan-Christoph Kalo Simon Razniewski Sneha Singhania LM-KBC Challenge and KBC-LM workshop @ ISWC 2025 International Semantic Web Conference #iswc #iswc2025

Lise ve üniversite öğrencileri için Koç Üniversitesi Yaz Araştırma Programı ‼️Son Başvuru Tarihi: 16 Mayıs 2025  research.ku.edu.tr/tr/arastirma-a…

After supervising 20+ papers, I have highly opinionated views on writing great ML papers. When I entered the field I found this all frustratingly opaque So I wrote a guide on turning research into high-quality papers with scientific integrity! Hopefully still useful for NeurIPS

The use of AI in reviewing is a growing problem. Several of my ICCV papers have AI reviews -- one reviewer was so lazy that they left in the prompts! A common refrain that I hear is that people have difficulty writing in English and need to use AI to clean up their review.