www.youtube.com/watch?v=aR20...
www.youtube.com/watch?v=aR20...
Self-supervised learning is fascinating! How can AI learn from images only without labels?
In this video, we’ll build the method from first principles and uncover the key ideas behind CLIP, MAE, SimCLR, and DINO (v1–v3).
Video link: youtu.be/oGTasd3cliM
Self-supervised learning is fascinating! How can AI learn from images only without labels?
In this video, we’ll build the method from first principles and uncover the key ideas behind CLIP, MAE, SimCLR, and DINO (v1–v3).
Video link: youtu.be/oGTasd3cliM
blog.neurips.cc/2021/12/08/t...
Review results are random and affect the achievement record of thousands of researchers globally. If review quality cannot be controlled the responsibility would fall on ACs to recheck flagged papers, which doesn't seem feasible either
blog.neurips.cc/2021/12/08/t...
Review results are random and affect the achievement record of thousands of researchers globally. If review quality cannot be controlled the responsibility would fall on ACs to recheck flagged papers, which doesn't seem feasible either
arxiv.org/abs/2506.08388
We introduce a new way to teach LLMs how to reason by learning to teach, not solve! Here, a teacher model is rewarded based on how effectively its explanations help the student model recover correct solutions.
Blog: sakana.ai/rlt
Paper: arxiv.org/abs/2506.08388
Code: github.com/SakanaAI/RLT
We introduce a new way to teach LLMs how to reason by learning to teach, not solve.
arxiv.org/abs/2506.08388
We introduce a new way to teach LLMs how to reason by learning to teach, not solve! Here, a teacher model is rewarded based on how effectively its explanations help the student model recover correct solutions.
- Good reviewers feel like accepting the papers, and try to find enough reasons to do so
- Bad reviewers feel like rejecting the papers, and try to find enough reasons to do so
They may seem equivalent but oh they are not
- Good reviewers feel like accepting the papers, and try to find enough reasons to do so
- Bad reviewers feel like rejecting the papers, and try to find enough reasons to do so
They may seem equivalent but oh they are not
#only_in_topcvconf #bully_reviewers
#only_in_topcvconf #bully_reviewers
Did you read blog posts / preprint about #ICLR2025 LLM experiment?
Were you in the #ICLR2025 review process?
As I was not in the process, I'm asking for opinions to improve review systems
Thank you for your help🙏
🤗 huggingface.co/blog/yoshito...
Did you read blog posts / preprint about #ICLR2025 LLM experiment?
Were you in the #ICLR2025 review process?
As I was not in the process, I'm asking for opinions to improve review systems
Thank you for your help🙏
🤗 huggingface.co/blog/yoshito...
I was hesitant to do that, but TIL the following update
> Update as of December 12, 2024: If you change your default Bluesky username (with the .bsky.social suffix) to a website URL, your old .bsky.social username will be reserved for you.
bsky.social/about/blog/4...
I was hesitant to do that, but TIL the following update
> Update as of December 12, 2024: If you change your default Bluesky username (with the .bsky.social suffix) to a website URL, your old .bsky.social username will be reserved for you.
bsky.social/about/blog/4...
arxiv.org/abs/2502.19842
arxiv.org/abs/2502.19842
The most important thing we build in technology and academia is not capability, but credibility. It does not matter how fast we calculate, how smart we are, or the number of products or papers we make, if we cannot answer "Why should anybody believe anything we say?"
The most important thing we build in technology and academia is not capability, but credibility. It does not matter how fast we calculate, how smart we are, or the number of products or papers we make, if we cannot answer "Why should anybody believe anything we say?"
But what would it actually take to support this in practice at the scale and speed the real world demands?
We explore this question and really push the limits of lifelong knowledge editing in the wild.
👇
But what would it actually take to support this in practice at the scale and speed the real world demands?
We explore this question and really push the limits of lifelong knowledge editing in the wild.
👇
github.com/facebookrese...
github.com/facebookrese...
www.youtube.com/watch?v=Vwjx...
www.youtube.com/watch?v=Vwjx...