363 stories
I'm preparing a submission for CVPR and would appreciate your advice. I am happy with my current results, but compute is a real constraint. My institute isn't a research-focused one, and the systems available to me are slow and unreliable.
Any other one have applied for the Education Track? It seems they'll announce the result soon.
I’ve spent the last few months writing something I wish I had when I started working on ML performance engineering.
I got really into research a year ago during my final year of undergrad and maneged to get a first author paper accepted at Neurips 2026. It is a pretty impressive achievement obviously but my background is pretty shit lol.
Google Cloud AI Research has open-sourced RRSI, a framework that lets LLM agents rewrite their own prompts, tools and memory while model weights stay frozen. It adds a leakage critic, a noise floor, a cost rule and pruning so gains carry over to new tasks.
Softmax has N inputs and N outputs but it's output only has N-1 degrees of freedom because of the condition that the sum of outputs must be equal to one.
I'm one of the authors of two recent papers exploring different sources of redundant computation in attention. I'd like to share the ideas and hear feedback from people working on long-context models and attention kernels.
I want to attend and present my findings in Atlanta. How is the vibe there? Are people overly critical or are people generally open-minded?
I'm a new PhD candidate, and my thesis is about the meta-learning paradigm in medical imaging. Right now, I don't have a specific problem to work on, and my supervisor told me to explore the field and find one myself.
Some of the world's foremost AI researchers and policy leaders — including at OpenAI, Anthropic and Microsoft — are warning that AI's growing ability to automate its own development could rapidly speed up its progress.Why it matters: Such a rapid scale-up in capabilities would leave governments and…
Yesterday I shared our open-source Clash Royale simulator and its recurrent PPO agent here. A training loop is easier to understand when you can watch it, so we put a small interactive version online:
Turn a small open-source Qwen LLM into a fast, single-pass text classifier by swapping its language-modeling head The post How to Make Your Own JEV Model from an Open LLM appeared first on Towards Data Science.
Sharing our recent work, now accepted at NeurIPS: Functional Gradient Descent with Adaptive Representations. Functional GD algorithms generally outperform neural nets, but are hard to accurately implement.
A small, strange discovery in machine learning called grokking The post The AI That Learned to Understand Long After It Stopped Trying appeared first on Towards Data Science.
I benchmarked Qwen3-VL 8B Instruct (Q4_K_M, Ollama, M5 24GB, ~30s/doc) against Claude Opus 5.5, Sonnet 5 and GPT-5.6 Terra on: - receipts: CORD (Indonesia) and SROIE (Malaysia), 30 each - 20 scanned 1980s-90s invoices , answer keys human-verified - 32 real IRS forms, 4 damage levels (generated this…
Detect hidden shifts in feature relationships with adversarial validation and scikit-learn The post How to Catch Data Drift When Every Feature Looks Normal appeared first on Towards Data Science.
Hi, I work as a Data Engineer at a manufacturing company where we build engines. A significant part of my work involves ML/DL-related tasks, and I’d like to turn one of my projects into a research publication.
Fireworks AI has released Ember-1, a post-trained Kimi K3 that learns to produce shorter reasoning traces instead of lowering reasoning effort. Fireworks reports about 40% fewer tokens, with output tokens per task falling from 49.3K to 29.9K in a production A/B test at an essentially unchanged…
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