Bruno Mlodozeniec
@brunokm.bsky.social
PhD in Deep Learning at Cambridge. Previously Microsoft Research AI resident & researcher at Qualcomm. I want to find the key to generalisation.
Reposted by Bruno Mlodozeniec
It's that time of the year! 🎁
The Apple Machine Learning Research (MLR) team in Paris is hiring a few interns, to do cool research for ±6 months 🚀🚀 & work towards publications/OSS.
Check requirements and apply: ➡️ jobs.apple.com/en-us/detail...
More❓→ ✉️ mlr_paris_internships@group.apple.com
The Apple Machine Learning Research (MLR) team in Paris is hiring a few interns, to do cool research for ±6 months 🚀🚀 & work towards publications/OSS.
Check requirements and apply: ➡️ jobs.apple.com/en-us/detail...
More❓→ ✉️ mlr_paris_internships@group.apple.com
October 17, 2025 at 1:07 PM
It's that time of the year! 🎁
The Apple Machine Learning Research (MLR) team in Paris is hiring a few interns, to do cool research for ±6 months 🚀🚀 & work towards publications/OSS.
Check requirements and apply: ➡️ jobs.apple.com/en-us/detail...
More❓→ ✉️ mlr_paris_internships@group.apple.com
The Apple Machine Learning Research (MLR) team in Paris is hiring a few interns, to do cool research for ±6 months 🚀🚀 & work towards publications/OSS.
Check requirements and apply: ➡️ jobs.apple.com/en-us/detail...
More❓→ ✉️ mlr_paris_internships@group.apple.com
How do you identify training data responsible for an image generated by your diffusion model? How could you quantify how much copyrighted works influenced the image?
In our ICLR oral paper we propose how to approach such questions scalably with influence functions.
In our ICLR oral paper we propose how to approach such questions scalably with influence functions.
April 16, 2025 at 12:45 PM
How do you identify training data responsible for an image generated by your diffusion model? How could you quantify how much copyrighted works influenced the image?
In our ICLR oral paper we propose how to approach such questions scalably with influence functions.
In our ICLR oral paper we propose how to approach such questions scalably with influence functions.
Rich Turner with other members of our group recently published a paper on Aardvark — end-to-end weather prediction with deep learning — in Nature, and it was just featured in The Guardian and Financial Times!
www.theguardian.com/technology/2...
www.theguardian.com/technology/2...
AI-driven weather prediction breakthrough reported
Researchers say Aardvark Weather uses thousands of times less computing power and is much faster than current systems
www.theguardian.com
March 21, 2025 at 1:55 PM
Rich Turner with other members of our group recently published a paper on Aardvark — end-to-end weather prediction with deep learning — in Nature, and it was just featured in The Guardian and Financial Times!
www.theguardian.com/technology/2...
www.theguardian.com/technology/2...
Myself, James and and Shreyas will be at NeurIPS presenting this work. Come chat to us if you’re interested!
I'll be at NeurIPS next week, presenting our work "A Generative Model of Symmetry Transformations." In it, we propose a symmetry-aware generative model that discovers which (approximate) symmetries are present in a dataset and can be leveraged to improve data efficiency.
🧵⬇️
🧵⬇️
December 5, 2024 at 6:23 PM
Myself, James and and Shreyas will be at NeurIPS presenting this work. Come chat to us if you’re interested!
Diffusion models are so ubiquitous, but it's difficult to find an introduction that is concise, simple and comprehensive.
My supervisor Rich Turner (with me & some other students) has written an introduction to diffusion models that fills this gap:
arxiv.org/abs/2402.04384
My supervisor Rich Turner (with me & some other students) has written an introduction to diffusion models that fills this gap:
arxiv.org/abs/2402.04384
Denoising Diffusion Probabilistic Models in Six Simple Steps
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generati...
arxiv.org
November 15, 2024 at 1:40 AM
Diffusion models are so ubiquitous, but it's difficult to find an introduction that is concise, simple and comprehensive.
My supervisor Rich Turner (with me & some other students) has written an introduction to diffusion models that fills this gap:
arxiv.org/abs/2402.04384
My supervisor Rich Turner (with me & some other students) has written an introduction to diffusion models that fills this gap:
arxiv.org/abs/2402.04384