4 papers · 1 filter
Kernel Reboot: Breaking the Boundaries of Neural Tangent Kernels for Neural Fields
Amir Mallak, Alaa Maalouf, Lior Wolf +2
Neural fields (NFs) map continuous coordinates to signals such as color or density, but fast high-quality reconstruction from sparse observations remains difficult. Classical Neura…
BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
Richard Hildebrandt, Evangelos Kourlitis, Baran Hashemi +7
We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics through nuclear and space engin…
Flow Matching Neural Processes
Hussen Abu Hamad, Dan Rosenbaum
Neural processes (NPs) are a class of models that learn stochastic processes directly from data and can be used for inference, sampling and conditional sampling. We introduce a new…
Robust Neural Processes for Noisy Data
Chen Shapira, Dan Rosenbaum
Models that adapt their predictions based on some given contexts, also known as in-context learning, have become ubiquitous in recent years. We propose to study the behavior of suc…