Laurent Dinh @laurent_dinh
Internet pedestrian. ✨Content creator✨ (ML researcher). Tweets were not peer reviewed. (he/him/his). Bluesky: https://t.co/bY5ri0vqyL laurent-dinh.github.io Bay Area Joined May 2016-
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Laurent Dinh retweeted
If you are at attending ICML today, consider checking out Samara’s poster on the role of sparsity in MoEs at 11 AM PDT. Poster ID: E-2810
If you are at attending ICML today, consider checking out Samara’s poster on the role of sparsity in MoEs at 11 AM PDT. Poster ID: E-2810
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Laurent Dinh retweeted
Laurent Dinh retweeted
🚨 One question that has always intrigued me is the role of different ways to increase a model's capacity: parameters, parallelizable compute, or sequential compute? We explored this through the lens of MoEs:
On that topic, an interesting read has been “Prediction Without Futures” by @sunhahong (onlinelibrary.wiley.com/doi/10.1111/hi…).
On that topic, an interesting read has been “Prediction Without Futures” by @sunhahong (onlinelibrary.wiley.com/doi/10.1111/hi…).
Laurent Dinh retweeted
Open the schools!
Laurent Dinh retweeted
Junyoung is one of the most prolific RSs I have worked with. You won't hear much from him on social media; he doesn't like doing PR and getting into politics and dramas. He cares about delivering and contributing the best he can to projects! Big loss for GDM, huge win for MSAI!
Junyoung is one of the most prolific RSs I have worked with. You won't hear much from him on social media; he doesn't like doing PR and getting into politics and dramas. He cares about delivering and contributing the best he can to projects! Big loss for GDM, huge win for MSAI!
Laurent Dinh retweeted
Stop discarding your old gradients! Introducing AdEMAMix, a novel (first-order) optimizer capable of outperforming Adam. Let’s have a thread on momentum and the surprising relevance of very old gradients. A joint work with @GrangierDavid and @PierreAblin #ml #optimization 1/🧵
Laurent Dinh retweeted
Enjoy attention? Want to make it ~18% faster? Try out Sigmoid Attention. We replace the traditional softmax in attention with a sigmoid and a constant (not learned) scalar bias based on the sequence length. Paper: arxiv.org/abs/2409.04431 Code: github.com/apple/ml-sigmo… This was…
Laurent Dinh retweeted
Not what I think any of us meant, but I guess this means p(DOOM) is at or near 100%.
Not what I think any of us meant, but I guess this means p(DOOM) is at or near 100%.
Laurent Dinh retweeted
Very proud to have been a (very tiny) part of this work on voice transfer for individuals with dysarthria, using zero-shot cross-lingual voice transfer research.google/blog/restoring…
Laurent Dinh retweeted
🇫🇷 Il faut tout faire pour ne pas se réveiller, dans 10 jours, avec une assemblée nationale dominée par le rassemblement nationale. Plus le RN aura de sièges, plus il pourra mettre en œuvre une politique destructrice pour notre société. 1/13
Laurent Dinh retweeted
cognitive scientist: so the lesson of Clever Hans is we need.. engineer: more horses cognitive scientist: engineer: stacked horses. parallel horses. pooled horses. horse dropout. RL with horses in the loop. cognitive scientist: engineer: Hans is All You Need
Laurent Dinh retweeted
To the journalists contacting me about the AGI consensual non-consensual (cnc) sex parties— During my twenties in Silicon Valley, I ran among elite tech/AI circles through the community house scene. I have seen some troubling things around social circles of early OpenAI…
To the journalists contacting me about the AGI consensual non-consensual (cnc) sex parties— During my twenties in Silicon Valley, I ran among elite tech/AI circles through the community house scene. I have seen some troubling things around social circles of early OpenAI…
Laurent Dinh retweeted
New piece! "Predictions without Futures" - or, how the domination of predictive tech & tech predictions keeps our future horizons stuck on more of the same. Open Access @ History & Theory 1/n onlinelibrary.wiley.com/doi/10.1111/hi…
Laurent Dinh retweeted
It's live! iclr.cc
Laurent Dinh retweeted
ICLR24 Spotlight: To train general-purpose SSL models, it's important to measure the quality of representations during training. But how can we do this w/o downstream labels? We propose a new label-free metric to eval SSL models, called Linear Discrimination Analysis Rank(LiDAR)
Laurent Dinh retweeted
https://t.co/1zxt0k0C2J
Laurent Dinh retweeted
Awesome work! Repo for this is also super clean, and has support for torch, MLX, and JAX! github.com/apple/ml-aim
Awesome work! Repo for this is also super clean, and has support for torch, MLX, and JAX! github.com/apple/ml-aim
Laurent Dinh retweeted
I try to remember that whenever we talk about how much of a fucked up horror show the world is, what we mean is that the world is full of good people being wronged and beauty being destroyed, that we risk losing all of it—not a denial of the good in the world but its affirmation.
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