Marta Skreta
@martaowesyou.bsky.social
UofT CompSci PhD Student in Alán Aspuru-Guzik's #matterlab and Vector Institute | prev. Apple
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
3/3 🧵Work with my fellow Matter Lab quantum tunnelers: Philipp Schleich, Lasse B. Kristensen, @rovargash.bsky.social, @aspuru.bsky.social
📜 https://buff.ly/4iu8w6M
💻 https://buff.ly/3D9HaD2
See you at NeurIPS!
📜 https://buff.ly/4iu8w6M
💻 https://buff.ly/3D9HaD2
See you at NeurIPS!
December 9, 2024 at 8:39 AM
3/3 🧵Work with my fellow Matter Lab quantum tunnelers: Philipp Schleich, Lasse B. Kristensen, @rovargash.bsky.social, @aspuru.bsky.social
📜 https://buff.ly/4iu8w6M
💻 https://buff.ly/3D9HaD2
See you at NeurIPS!
📜 https://buff.ly/4iu8w6M
💻 https://buff.ly/3D9HaD2
See you at NeurIPS!
2/3 🧵Here, we enter the quantumverse with deep equilibrium networks, where we demonstrate that a single layer of a quantum circuit can perform as well or better than multiple independent layers when trained like a DEQ.
December 9, 2024 at 8:39 AM
2/3 🧵Here, we enter the quantumverse with deep equilibrium networks, where we demonstrate that a single layer of a quantum circuit can perform as well or better than multiple independent layers when trained like a DEQ.