InfoNet: Neural Estimation of Mutual Information without Test-time Optimization
Date:
Oral presentation at ICML 2024 for our paper InfoNet: Neural Estimation of Mutual Information without Test-time Optimization. The talk introduced the foundation-model paradigm for mutual information estimation — a Transformer pre-trained on large-scale synthetic distributions that estimates the MI of arbitrary 1-D variable pairs in a zero-shot manner, ~100x faster than prior neural methods.