Aäron van den Oord @avdnoord
Research Scientist @ DeepMind avdnoord.github.io/homepage London, England Joined January 2013-
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Introducing Udio, an app for music creation and sharing that allows you to generate amazing music in your favorite styles with intuitive and powerful text-prompting. 1/11
Meet Imagen 2: our most advanced text-to-image diffusion technology. ✨ It features high-quality, photorealistic outputs and stronger consistency with your prompts. 🖼 Now available to use via @googlecloud’s #VertexAI platform. → dpmd.ai/x-imagen2
#Lyria is in production and can already used by selected creators on YT Shorts. You can find samples here: youtube.com/hashtag/dreamt…
#Lyria is in production and can already used by selected creators on YT Shorts. You can find samples here: youtube.com/hashtag/dreamt…
Beyond thrilled to share my dream project ever since I got into ML! Have been floored by #Lyria so many times - it's simply on another level 🤯 Humbled by the amazing artists supporting our tech in Dream Track and excited to see how Music AI tools boost musical creativity! 🎶❤️🎤
Beyond thrilled to share my dream project ever since I got into ML! Have been floored by #Lyria so many times - it's simply on another level 🤯 Humbled by the amazing artists supporting our tech in Dream Track and excited to see how Music AI tools boost musical creativity! 🎶❤️🎤
We're starting a new DeepMind research team in California to work on large-scale deep learning models. Apply if you want to contribute to our mission of building AGI!
We're starting a new DeepMind research team in California to work on large-scale deep learning models. Apply if you want to contribute to our mission of building AGI!
Had some fun visualising unconditional text generation process by SUNDAE: dpmd.ai/sundae Text gradually appears from noise by making denoising edits to the whole draft. A lot of impressive samples from image denoising models recently, time for text to catch up? 😀
Autoencoders can generate text well if they learn to correct themselves! Happy to share our new paper SUNDAE: we show how unrolling the autoencoder denoising process during training leads to strong results in machine translation & language modeling. dpmd.ai/sundae 1/3
Autoencoders can generate text well if they learn to correct themselves! With SUNDAE, our team shows how unrolling the autoencoder denoising process during training allows us to achieve strong results in machine translation & language modeling. Paper: dpmd.ai/sundae 1/
Self-supervised learning has made tremendous gains for simple visual tasks like recognizing objects, but interacting with the environment requires detecting, parsing, and understanding their geometry. Contrastive detection greatly facilitates these tasks. dpmd.ai/3jsOV9l
New example showing how to implement a VQ-VAE in #Keras with the PixelCNN part included. The example goes through several key ideas: optimizing discrete latent spaces, codebook sampling, and above all visualizations! keras.io/examples/gener… 1/
New example showing how to implement a VQ-VAE in #Keras with the PixelCNN part included. The example goes through several key ideas: optimizing discrete latent spaces, codebook sampling, and above all visualizations! keras.io/examples/gener… 1/
Honored to have received the MIT TR 35 innovators award. Very grateful to have been able to work with amazing colleagues on this research! @techreview technologyreview.com/innovator/aaro…
MuZero removed simulators in MBRL vs AlphaGo. VQ Models for Planning generalize to partial observable & stochastic environments. How? 1. Discretize states w/ VQVAE 2. Train a LM over states 3. Plan w/ MCTS using the LM Led by @yazhe_li & @sherjilozair arxiv.org/abs/2106.04615
How contrastive learning works on large-scale uncurated data? Want to significantly improve it in such a scenario? Check the new "Divide and Contrast (DnC)" work jointly with @avdnoord and @olivierhenaff ArXiv: arxiv.org/abs/2105.08054
What makes self-supervised learning from uncurated data so challenging? We identified the heavy-tailed distribution of image content as a limiting factor, and address it with Divide and Contrast. Leads to big gains in data-efficient and transfer learning. arxiv.org/abs/2105.08054
Nice use of VQ-VAEs for music generation!
New paper - CURL: Contrastive Unsupervised Representations for RL! We use the simplest form of contrastive learning (instance-based) as an auxiliary task in model-free RL. SoTA by *significant* margin on DMControl and Atari for data-efficiency. arxiv.org/abs/2004.04136
Apparently the models that are shepherding the health of the British people were fed viral pneumonia statistics from a different disease. The arrogance and stupidity of this is terrifying and devastating.
Unsupervised pre-training now outperforms supervised learning on ImageNet for any data regime (see figure) and also for transfer learning to Pascal VOC object detection arxiv.org/abs/1905.09272…
> "AI works like the brain!" Ahh, yes, I fondly remember my intro to linguistics class, wherein I read all of English wikipedia thousands of times until convergence, and then could finally construct better-than-random parse trees.
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Introducing Udio, an app for music creation and sharing that allows you to generate amazing music in your favorite styles with intuitive and powerful text-prompting. 1/11
Dishoom, King’s Cross, is a favorite for Google Deepminders. That’s also where I had my farewell lunch in 2019. Yum.
The best thing about London is the Indian food
This is mind-blowing technology, I played with it for hours when I got my hands on it! Huge congrats to Aaron and the team!
@lyria is finally out! Very proud of my team and the technology we have built. Probably the coolest project I've ever worked on and super excited that we are releasing Lyria in partnership with artists and the music industry!
#Lyria is a big leap forward in music generation! Currently running in a trialled YT experiment 'Dream Track'. Congratulations to @avdnoord and team 🎚️🎙️🎼🎶 youtube.com/hashtag/dreamt…
@lyria is finally out! Very proud of my team and the technology we have built. Probably the coolest project I've ever worked on and super excited that we are releasing Lyria in partnership with artists and the music industry!
Excited that this is out! When Kazuya and I started training the first versions of this model last year, we had no idea that it would turn out this good, the progress that the team has made in the last year was incredible. Thanks @avdnoord for being a fantastic team lead!
Thrilled to share #Lyria, the world's most sophisticated AI music generation system. From just a text prompt Lyria produces compelling music & vocals. Also: building new Music AI tools for artists to amplify creativity in partnership w/YT & music industry deepmind.google/discover/blog/…
🦩😎
The year is 2032. A model was trained on all images, videos and text on the web, using over 100 yottaFLOPs. It still thinks this is an image of a dog. To fix models post-hoc, check out PAINT!🎨 📜 arxiv.org/abs/2208.05592 💻 github.com/mlfoundations/… 🌐 model-patching.github.io
We're starting a new DeepMind research team in California to work on large-scale deep learning models. Apply if you want to contribute to our mission of building AGI!
Interested in exploring the limits of large-scale models? Consider joining our brand new Scalable Deep Learning team in California as a Research Engineer or Research Scientist, see job descriptions below! boards.greenhouse.io/deepmind/jobs/… boards.greenhouse.io/deepmind/jobs/…
Excited to share our latest work describing a new approach to lossy compression: ✨DiffC✨ sends an image corrupted by Gaussian noise which is then denoised by the receiver. Surprisingly, this simple approach works well despite operating in pixel space...
I am happy to report that our paper on "Algorithms for the Communication of Samples" has been accepted to ICML 2022. This problem frequently crops up in neural compression but also in differential privacy. arxiv.org/abs/2110.12805
@ilyasut Here is an idea: Ilya is becoming a philosopher.
Diffusion-based generative models are SOTA on perceptual quality, but how good are they at density estimation? Very good it turns out! 📢Announcing our new paper on diffusion models, with SOTA likelihoods & compression + new theoretical insights into this exciting model class.
New paper: Variational Diffusion Models (VDMs)! arxiv.org/abs/2107.00630 ✅ New general insights into diffusion models ✅ Simple objective ✅ Fast optimization & anytime synthesis ✅ SotA likelihoods & lossless compression Work with @TimSalimans @poolio @hojonathanho (1/n)
@avdnoord @techreview Congratulations Aäron! Hearing that voice in 2016 was astounding, and it is incredible to see how quickly WaveNet voice became a standard in the industry.
Congrats Aaron, very well deserved!
Honored to have received the MIT TR 35 innovators award. Very grateful to have been able to work with amazing colleagues on this research! @techreview technologyreview.com/innovator/aaro…
This is super well deserved. The article doesn’t even mention all the other amazing work Aäron has done (PixelCNN, VQ-VAE, the list goes on)
Honored to have received the MIT TR 35 innovators award. Very grateful to have been able to work with amazing colleagues on this research! @techreview technologyreview.com/innovator/aaro…
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