paper
Panel Discussion: Practical Problem Solving for Machine Learning
Guillermo Cabrera, Sungwook E. Hong, Lilianne Nakazono, David Parkinson, Yuan-Sen Ting
Panel Discussion: Practical Problem Solving for Machine Learning
Guillermo Cabrera, Sungwook E. Hong, Lilianne Nakazono, David Parkinson, Yuan-Sen Ting
paper2022-11-30English
machine learning financearxiv
Description
Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward.
Similar Books
paper
Quantitative mode stability for the wave equation on the Kerr-Newman spacetime
Damon Civin
Quantitative mode stability for the wave equation on the Kerr-Newman spacetime
paper
Risk-Aware Objective-Based Forecasting in Inertia Management
Haipeng Zhang, Ran Li, Yan Chen, Zhongda Chu, Mingyang Sun
Risk-Aware Objective-Based Forecasting in Inertia Management
paper
Chainalysis: Geography of Cryptocurrency 2023
Chainalysis
Chainalysis: Geography of Cryptocurrency 2023
paper
Periodicity in Cryptocurrency Volatility and Liquidity
Peter Reinhard Hansen, Chan Kim, Wade Kimbrough
Periodicity in Cryptocurrency Volatility and Liquidity
paper
Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
Saeideh Sekhavat, Mostafa Jamshidian, Adam Wittek, Karol Miller
Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
paper
Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I
Getachew K. Befekadu
Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I