Search Results for author: Luis Perez

Found 6 papers, 4 papers with code

The Micro-Aggregated Profit Share

no code implementations22 Sep 2023 Thomas Hasenzagl, Luis Perez

In particular, the aggregate markup has gone up from 10% of price over marginal cost in 1970 to 23% in 2020, and aggregate returns to scale have risen from 1. 00 to 1. 13.

ETA Prediction with Graph Neural Networks in Google Maps

no code implementations25 Aug 2021 Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Veličković

Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike.

Graph Representation Learning

Mastering Terra Mystica: Applying Self-Play to Multi-agent Cooperative Board Games

1 code implementation21 Feb 2021 Luis Perez

In this paper, we explore and compare multiple algorithms for solving the complex strategy game of Terra Mystica, hereafter abbreviated as TM.

Board Games

Automatic Code Generation using Pre-Trained Language Models

1 code implementation21 Feb 2021 Luis Perez, Lizi Ottens, Sudharshan Viswanathan

Specifically, we propose an end-to-end machine learning model for code generation in the Python language built on-top of pre-trained language models.

Code Generation

The Effectiveness of Data Augmentation in Image Classification using Deep Learning

1 code implementation13 Dec 2017 Luis Perez, Jason Wang

In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification.

Data Augmentation General Classification +1

Predicting Yelp Star Reviews Based on Network Structure with Deep Learning

1 code implementation11 Dec 2017 Luis Perez

In this paper, we tackle the real-world problem of predicting Yelp star-review rating based on business features (such as images, descriptions), user features (average previous ratings), and, of particular interest, network properties (which businesses has a user rated before).

Image Classification

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