1 citations · 1 across the 6 of their papers we have counts for
6 papers
Comparing explicit likelihood and likelihood-free simulation-based inference for weak lensing cosmic shear
Simone Vinciguerra, Nicolas Martinet, Marco Gatti
Simulation-based inference (SBI) has become a major tool for extracting cosmological information from weak-lensing (WL) surveys, particularly from non-Gaussian observables. We comp…
T-FIX: Text-Based Explanations with Features Interpretable to eXperts
Shreya Havaldar, Weiqiu You, Chaehyeon Kim +12
As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users are often domain experts who expect not just answers, but explanations that mirror p…
The FIX Benchmark: Extracting Features Interpretable to eXperts
Helen Jin, Shreya Havaldar, Chaehyeon Kim +10
Feature-based methods are commonly used to explain model predictions, but these methods often implicitly assume that interpretable features are readily available. However, this is…
Dimensionality Reduction Techniques for Statistical Inference in Cosmology
Minsu Park, Marco Gatti, Bhuvnesh Jain
We explore linear and non-linear dimensionality reduction techniques for statistical inference of parameters in cosmology. Given the importance of compressing the increasingly comp…
Improving Convolutional Neural Networks for Cosmological Fields with Random Permutation
Kunhao Zhong, Marco Gatti, Bhuvnesh Jain
Convolutional Neural Networks (CNNs) have recently been applied to cosmological fields -- weak lensing mass maps and galaxy maps. However, cosmological maps differ in several ways…
Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups
Weiqiu You, Helen Qu, Marco Gatti +2
Self-attributing neural networks (SANNs) present a potential path towards interpretable models for high-dimensional problems, but often face significant trade-offs in performance.…