Joey Bose
@joeybose.bsky.social
Incoming Assistant Professor @Imperial College London. Post-doc @UniofOxford. Into Geometry ∩ Generative Models. @mila-quebec.bsky.social Affiliate member. Phd from @mila-quebec.bsky.social / McGill.
website: https://joeybose.github.io/
website: https://joeybose.github.io/
2.) The Superposition of Diffusion Models Using the Itô Density Estimator: openreview.net/forum?id=2o5...
The Superposition of Diffusion Models
The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant...
openreview.net
January 22, 2025 at 5:16 PM
2.) The Superposition of Diffusion Models Using the Itô Density Estimator: openreview.net/forum?id=2o5...
1.) 1. Steering masked discrete diffusion models via discrete denoising posterior prediction: openreview.net/forum?id=Omb...
Steering Masked Discrete Diffusion Models via Discrete Denoising...
Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very building blocks of life in protein sequences. However...
openreview.net
January 22, 2025 at 5:16 PM
1.) 1. Steering masked discrete diffusion models via discrete denoising posterior prediction: openreview.net/forum?id=Omb...
Reposted by Joey Bose
🧵(3/7)This is all due to an amazing team: @martaowesyou.bsky.social @lazaratan.bsky.social @joeybose.bsky.social @alextong.bsky.social
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
The Superposition of Diffusion Models Using the Itô Density Estimator
The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant co...
arxiv.org
December 28, 2024 at 2:32 PM
🧵(3/7)This is all due to an amazing team: @martaowesyou.bsky.social @lazaratan.bsky.social @joeybose.bsky.social @alextong.bsky.social
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
Reposted by Joey Bose
I had a blast working with such an amazing team! @martaowesyou.bsky.social @joeybose.bsky.social @alextong.bsky.social @k-neklyudov.bsky.social
Check out our linked for details and examples!
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
Check out our linked for details and examples!
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
The Superposition of Diffusion Models Using the Itô Density Estimator
The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant co...
arxiv.org
December 28, 2024 at 5:58 PM
I had a blast working with such an amazing team! @martaowesyou.bsky.social @joeybose.bsky.social @alextong.bsky.social @k-neklyudov.bsky.social
Check out our linked for details and examples!
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
Check out our linked for details and examples!
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
Reposted by Joey Bose
Work with an absolute dream of a team: @lazaratan.bsky.social @joeybose.bsky.social @alextong.bsky.social and @k-neklyudov.bsky.social 🤗🚀⚡️
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
The Superposition of Diffusion Models Using the Itô Density Estimator
The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant co...
arxiv.org
December 28, 2024 at 7:53 PM
Work with an absolute dream of a team: @lazaratan.bsky.social @joeybose.bsky.social @alextong.bsky.social and @k-neklyudov.bsky.social 🤗🚀⚡️
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
📄Paper: arxiv.org/abs/2412.17762
💻Code: github.com/necludov/sup...
🤗HuggingFace: huggingface.co/superdiff
Organizers continued:
Michael Bronstein @mmbronstein.bsky.social
Max Welling
Arnaud Doucet @arnauddoucet.bsky.social
Aapo Hyvärinen
Part 2/2
December 18, 2024 at 7:09 PM
Organizers continued:
Michael Bronstein @mmbronstein.bsky.social
Max Welling
Arnaud Doucet @arnauddoucet.bsky.social
Aapo Hyvärinen
Part 2/2
🙏 Of course, this is co-organized with a dream team
Tara Akhound-Sadegh
Marta [email protected]
Yuanqi Du
Sarthak [email protected]
Alex [email protected]
Kirill [email protected]
Part 1/2
Tara Akhound-Sadegh
Marta [email protected]
Yuanqi Du
Sarthak [email protected]
Alex [email protected]
Kirill [email protected]
Part 1/2
December 18, 2024 at 7:09 PM
🙏 Of course, this is co-organized with a dream team
Tara Akhound-Sadegh
Marta [email protected]
Yuanqi Du
Sarthak [email protected]
Alex [email protected]
Kirill [email protected]
Part 1/2
Tara Akhound-Sadegh
Marta [email protected]
Yuanqi Du
Sarthak [email protected]
Alex [email protected]
Kirill [email protected]
Part 1/2
⚡We have an electric lineup of speakers and panelists:
Sitan Chen(Harvard)
Rianne Van Den Berg(MSR)
Ricky Chen(Meta)
Anna Korba(ENSAE Paris, CREST)
Marylou Gabrié(ENS)
Emtiyaz Khan(RIKEN)
Grant Rotskoff(Stanford)
Francisco Vargas(Xaira, Cambridge)
Kyle Cranmer (University of Wisconsin-Madison)
Sitan Chen(Harvard)
Rianne Van Den Berg(MSR)
Ricky Chen(Meta)
Anna Korba(ENSAE Paris, CREST)
Marylou Gabrié(ENS)
Emtiyaz Khan(RIKEN)
Grant Rotskoff(Stanford)
Francisco Vargas(Xaira, Cambridge)
Kyle Cranmer (University of Wisconsin-Madison)
December 18, 2024 at 7:09 PM
⚡We have an electric lineup of speakers and panelists:
Sitan Chen(Harvard)
Rianne Van Den Berg(MSR)
Ricky Chen(Meta)
Anna Korba(ENSAE Paris, CREST)
Marylou Gabrié(ENS)
Emtiyaz Khan(RIKEN)
Grant Rotskoff(Stanford)
Francisco Vargas(Xaira, Cambridge)
Kyle Cranmer (University of Wisconsin-Madison)
Sitan Chen(Harvard)
Rianne Van Den Berg(MSR)
Ricky Chen(Meta)
Anna Korba(ENSAE Paris, CREST)
Marylou Gabrié(ENS)
Emtiyaz Khan(RIKEN)
Grant Rotskoff(Stanford)
Francisco Vargas(Xaira, Cambridge)
Kyle Cranmer (University of Wisconsin-Madison)
🚨 We invite submissions on sampling, Bayesian inference, accelerating sampling in AI4Science, Generative models in Probabilistic inference, and more!
🤖 We invite submissions along 3 tracks:
1.) Research Papers
2.) Challenges and Reflections
3.) Benchmarks and Datasets
Deadline is Deb 3 AOE!
🤖 We invite submissions along 3 tracks:
1.) Research Papers
2.) Challenges and Reflections
3.) Benchmarks and Datasets
Deadline is Deb 3 AOE!
December 18, 2024 at 7:09 PM
🚨 We invite submissions on sampling, Bayesian inference, accelerating sampling in AI4Science, Generative models in Probabilistic inference, and more!
🤖 We invite submissions along 3 tracks:
1.) Research Papers
2.) Challenges and Reflections
3.) Benchmarks and Datasets
Deadline is Deb 3 AOE!
🤖 We invite submissions along 3 tracks:
1.) Research Papers
2.) Challenges and Reflections
3.) Benchmarks and Datasets
Deadline is Deb 3 AOE!
Self Consuming Generative Models under Curated Data Provably Optimize Human Preferences (Spotlight), led by Damien Ferbach
arxiv.org/abs/2407.09499
arxiv.org/abs/2407.09499
December 7, 2024 at 2:39 AM
Self Consuming Generative Models under Curated Data Provably Optimize Human Preferences (Spotlight), led by Damien Ferbach
arxiv.org/abs/2407.09499
arxiv.org/abs/2407.09499
Metric Flow Matching for Smooth Interpolations on the Data Manifold, led by Kacper Kapusniak
arxiv.org/abs/2405.14780
arxiv.org/abs/2405.14780
December 7, 2024 at 2:39 AM
Metric Flow Matching for Smooth Interpolations on the Data Manifold, led by Kacper Kapusniak
arxiv.org/abs/2405.14780
arxiv.org/abs/2405.14780
December 7, 2024 at 2:39 AM
FoldFlow-2 for sequence-conditioned protein structure design. Led by Guillaume Huguet and James Vuckovic
arxiv.org/abs/2405.20313
arxiv.org/abs/2405.20313
December 7, 2024 at 2:39 AM
FoldFlow-2 for sequence-conditioned protein structure design. Led by Guillaume Huguet and James Vuckovic
arxiv.org/abs/2405.20313
arxiv.org/abs/2405.20313