Applied scientist @AWS AI/ML. Ex @Meta|@Cambridge_Uni|@ucl|@ecentua 🇬🇷. No cats.
New York, USA
Joined December 2014
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Dionysis Manousakas retweeted
We've lost an absolute giant today. RIP Dimitri Bertsekas. His probability and optimization books got me through my masters. Massive loss for the MIT community and the field.
We're hiring Summer Interns! Join our NYC team to work on cutting-edge synthetic data generation for LLMs. This is a unique opportunity to dive into foundation models, and make a real impact. If interested, feel free to share your resume with me!
amazon.jobs/en/jobs/2793709/…
Dionysis Manousakas retweeted
Another day another launch 😬
🚀🥳 I am super excited to announce that we open sourced Renate, a #ContinualLearning library to automatically retrain and retune #DeepNeuralNetworks. This is joined work with @610v4nn1__ , @lukas_balles and Martin Wistuba!
github.com/awslabs/Renate
Dionysis Manousakas retweeted
A new release of Syne Tune is out!
syne-tune.readthedocs.io/en/…… 🥳
Our goal is to make machine learning more reproducible. The library offers a broad range of asynchronous hyperparameter optimisation algorithms that can be run locally or in the cloud.
#hpo #automl #DeepLearning
The (early) Christmas gift you all have been waiting for ;-). New release of Syne Tune! Key changes are a much better documentation, support of SageMaker warm pools and integration of YAHPO. github.com/awslabs/syne-tune… Thanks to the team for this great effort!
Dionysis Manousakas retweeted
The first version of our #hpo chapter for the D2L book is out! d2l.ai/chapter_hyperparamete… . This is joint work with @cedapprox and Matthias Seeger.
Dionysis Manousakas retweeted
We are excited to announce the general availability of #Fortuna , an #OpenSource library for #UncertaintyQuantification in #DeepLearning.
- #AWS blog post: lnkd.in/eKrnyK9h
- #GitHub repo: github.com/awslabs/fortuna
- Documentation: lnkd.in/eY9_ATj7
Dionysis Manousakas retweeted
Time to share our paper “Black-box Coreset Variational Inference” accepted to #NeurIPS2022 with @neuralvertigo and Hippolyt Ritter. We re-derive Bayesian core sets under an approximate inference lens to make them generally applicable. Paper link arxiv.org/abs/2211.02377 1/n
Dionysis Manousakas retweeted
Diffusion models like #DALLE and #StableDiffusion are state of the art for image generation, yet our understanding of them is in its infancy. This thread introduces the basics of how diffusion models work, how we understand them, and why I think this understanding is broken.🧵
Dionysis Manousakas retweeted
As many Ukrainian IT engineers right now I was left without a job 😔I am a Data Scientist with 5 years of experience in full-cycle ML models development (including deployment) and in managing data science teams. If anyone sees job positions I could fit, ping me 🙏❤️
Dionysis Manousakas retweeted
Give, I say, all your strength to your work,
make it your total concern. And don’t forget your work
even in times of trial or when you near your end.
Dionysis Manousakas retweeted
Good times! Welcome to two new members on the team, Dionysis Manousakas @neuralvertigo and Hippolyt Ritter, who will be working with me on the interface of deep learning and probabilistic inference. Very excited to work with both of you!
Dionysis Manousakas retweeted
He who hopes to grow in spirit
will have to free himself from obedience and respect.
Dionysis Manousakas retweeted
The Call for Papers for #AISTATS2020 is out at aistats.org
The submission deadline is 8 October 2019.
The conference will be held in Palermo, Italy, from 3 to 5 June 2020.
Dionysis Manousakas retweeted
Mathematicians Caucher Birkar, Alessio Figalli, Peter Scholze and Akshay Venkatesh have just been awarded the Fields Medal. Computer scientist Constantinos Daskalakis won the Nevanlinna Prize. Read our exclusive, in-depth profiles of all five winners. buff.ly/2OzGetJ
Dionysis Manousakas retweeted
Judea Pearl claims all we do in ML is curve fitting. I wrote this post to explain that claim and introduce the basics of causal inference to ML folks.
Machine Learning beyond Curve Fitting: An Intro to Causal Inference and do-Calculus
inference.vc/p/f01e8d56-5117…
Dionysis Manousakas retweeted
RIP #MaryamMirzakhani. To learn about her math and life, this piece by @EricaKlarreich is a fine place to start. quantamagazine.org/maryam-mi…