5 papers · 1 filter
Guided Diffusion Sampling for Precipitation Forecast Interventions
Ayumu Ueyama, Kazuhiko Kawamoto, Hiroshi Kera
Extreme precipitation causes severe societal and economic damage, and weather control has long been discussed as a potential mitigation strategy. However, to the best of our knowle…
Learning Large-Scale Modular Addition with an Auxiliary Modulus
Hanato Kikuchi, Ryosuke Masuya, Kazuhiko Kawamoto +1
Learning parity functions, more general modular addition, is a challenging machine learning task due to its input sensitivity. A recent study substantially scaled modular addition…
Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding
Kazuki Yoda, Kazuhiko Kawamoto, Hiroshi Kera
The hardness of learning a function that attains a target task relates to its input-sensitivity. For example, image classification tasks are input-insensitive as minor corruptions…
Discovering Learning-Friendly Generation Orders for Sequential Computation
Yuta Sato, Kazuhiko Kawamoto, Hiroshi Kera
Sequential computation via autoregressive generation can make difficult tasks learnable, but the generation order of intermediate states strongly affects whether training succeeds.…
VarteX: Enhancing Weather Forecast through Distributed Variable Representation
Ayumu Ueyama, Kazuhiko Kawamoto, Hiroshi Kera
Weather forecasting is essential for various human activities. Recent data-driven models have outperformed numerical weather prediction by utilizing deep learning in forecasting pe…