Astronomical observations provide multimodal views of physical systems, with images and spectra capturing complementary properties of celestial objects. Scientific foundation models can learn powerful representations from these observations, but representations learned by separate models remain difficult to combine. We...
E. Lastufka, M. Drozdova, D. Schaerer et al.· 0 citations
This work test the reasoning capabilities of Neural Cellular Automata, networks of recurrent cells that use strictly local connectivity and asynchronous updates, and shows that NCAs are robust reasoners capable of dynamically modulating compute to recover efficiently from damage, and that they can scale to solve reason...
Mayalen Etcheverry, Pietro Miotti, Aidan Sirbu et al.· 0 citations
Denoising Diffusion Probabilistic Models (DDPMs) generate samples by starting from noise and repeatedly denoising while keeping each update close to the current noisy state. This behavior is effective in many continuous domains, but its role is less clear for globally constrained discrete tasks, such as Sudoku, graph c...
M. Drozdova, Stéphane Liem Nguyen, François Fleuret· 0 citations
This work adds a persistent hidden state to a diffusion denoiser and removes its timestep conditioning, leaving a single shared update that can be run to arbitrary depth, and develops an anytime solver that keeps improving with inference depth far beyond the rollout lengths and backpropagation window used in training.
M. Drozdova, Aidan Sirbu, Pietro Miotti et al.· 3 citations
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