215 stories
Hey r/MachineLearning, I wrote this blog post on semi-supervised learning. Blog post: https://stefankeselj.com/entries/semi_supervision. https://preview.redd.it/2fh9jw7ahbuh1.png?width=1502&format=png&auto=webp&s=529115c47d0029c69e84803ec93ae81949f92bbf I think it's a useful but underrated…
Lunara introduces a novel Diffusion Mixture Transformer architecture with fewer than 10B active parameters for modeling artistic intelligence in image generation.
UT-based small models, despite being trained from scratch on these tasks without internet-scale pre-training, consistently outperform most standard Transformer-based Large Language models (LLMs) by a significant margin. 👈
Got assigned to Paris location but want to go to Sydney. Anyone wants to go yo Paris but assigned to Sydney? Want to switch with you.
We test three state-of-the-art multi-modal large language models (GPT-4o, Gemini-1.5-Pro, Gemini-1.5-Flash) and find that they fail dramatically when generalization requires that the rules of the game must be manipulated and combined.
UCLA’s Trustworthy AI Lab is running a tournament on October 16 where AI agents compete in Pokémon Showdown, Werewolf, Red Alert, and Honor of Kings. It’s open to everyone, including remote participants, with a $5,000 total prize pool secured thus far and support from Oracle, Replit, Matcherino,…
For those who have gone through the KDD review process, how did it go? Did you submit a rebuttal or decide to withdraw? Did reviewers respond or change their scores afterward? Curious to hear whether rebuttals actually made a difference, especially for papers with initially low scores.
I’m an industry professional working in data platform engineering, and I’m just getting started in research. I tried creating an OpenReview account, but my profile is awaiting activation.
If you're moving a RAG prototype to production, this is the checklist I wish I'd had.
Are they sending to all reviewers or some selected reviewers? Just got an email about complimentary registration.
I'm developing a benchmark for a low resource language and I don't want it to be leaked and used for training when it is being used to get predictions. For locally run models it shouldn't be a problem, but for models that are only accessible via API, it is.
Here is the story: Nvidia has published this work (with source code available) called dreamDojo which is a world model for robotics based of their prior work Cosmos 2.5 which is cited about 100 times and got ICML’s spotlight.
Write-up + demo: https://drksci.com/labs-phosphene So this started as a bit of a gripe. Modern terminal renderers are seriously impressive and seriously complicated. GPU glyph atlases, texture caches, custom shaders, HarfBuzz shaping, ligatures, damage tracking, grid diffing, dirty-row uploads.
As the title suggests I'm currently trying to make a modal to identify which app is being used. The thing is I don't have labelled data and generating it is out of question since that is a lot of work ( I need to detect like 3-5K apps give or take)
https://preview.redd.it/i2c0cdkg04uh1.png?width=2532&format=png&auto=webp&s=fdfed9fe7ed2cef8e45791110a5152ff163a0ce8 TLDR: The question we answer: how do you learn from experts with different objectives?
Neurips says that camera ready has to be submitted by editing original submission on open review. But no such edit option is visible to me. Is it same for other authors??
If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:
The setup is roughly: humans take recent work from top-tier ML/AI conferences, turn part of it into a well-defined task with an evaluator, and then let an agent iteratively modify the solution and search for a better score.
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