GitHub

StatsPAI: a Python-native Stata/R replacement for applied causal inference

StatsPAI is for empirical researchers who would normally jump between Stata, R, and Python. Its goal is to make common Stata/R econometrics and causal-inference workflows feel native in Python: load a ...
IEEE

Learning to Operate Distribution Networks With Safe Deep Reinforcement Learning

Abstract: In this paper, we propose a safe deep reinforcement learning (SDRL) based method to solve the problem of optimal operation of distribution networks (OODN). We formulate OODN as a constrained ...
C&EN

Variable Neighborhood Search with Cost Function Networks To Solve Large Computational Protein Design Problems

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID. Please note: If you switch to a different device, you may be asked to login again with only ...
PNAS

Robust inattentive discrete choice

When people make decisions they often have a large volume of information at their disposal. Faced with this challenge, in practice they focus their attention on a more limited amount of salient ...
PNAS

Approximate message passing from random initialization with applications to

Approximate Message Passing (AMP) serves as both a family of efficient first-order algorithms and a powerful theoretical machinery for high-dimensional data analysis, which has found applications in a ...
Frontiers

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

With the introduction of autonomy into the precision agriculture process, environmental exploration, disaster response, and other fields, one of the global demands is to navigate autonomous vehicles ...
Frontiers

Discrete optimization using quantum annealing on sparse Ising models

This paper discusses techniques for solving discrete optimization problems using quantum annealing. Practical issues likely to affect the computation include precision limitations, finite temperature, ...