Programación y frameworks en IA | 🇬🇧 Programming and frameworks in AI

R0:e3d9ea294a21c145042e5f31369de739-CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

CARLA (Counterfactual And Recourse LibrAry), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods. We have open-sourced CARLA and our experimental results on Github, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.

R0:94ecd1a3b2168ef2dbb43222545d08bc-YOLOX: Exceeding YOLO Series in 2021

YOLOX: Exceeding YOLO Series in 2021

We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in industry, we boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP.

Classification based on Topological Data Analysis

Classification based on Topological Data Analysis

Topological Data Analysis (TDA) is an emergent field that aims to discover topological information hidden in a dataset. TDA tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes an algorithm that applies TDA directly to multi-class classification problems, even imbalanced datasets, without any further ML stage

S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evaluation. In short, consider a virtual third party to whom every data-holder sends their inputs, and which computes the neural network: in our case, this virtual third party is actually a set of servers which individually learn nothing, even with a malicious (but non-colluding) adversary.

R0:3d92323b5375746d21dcb172e8950adc-Explainability in Graph Neural Networks: A Taxonomic Survey

Explainability in Graph Neural Networks: A Taxonomic Survey

We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations.

Fourier Neural Operator for Parametric Partial Differential Equations

Fourier Neural Operator for Parametric Partial Differential Equations

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution.

R0_b6959f4e35b2c24097cf630fa7cea606-Evaluating and Characterizing Human Rationales

Evaluating and Characterizing Human Rationales

Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior.

https://thebibleofai.online/wp-content/uploads/2020/09/tidy-modeling-with-r.jpg

Tidy Modeling with R

This book provides an introduction to how to use our software to create models. We focus on a dialect of R called the tidyverse that is designed to be a better interface for common tasks using R. If you’ve never heard of or used the tidyverse, Chapter 2 provides an introduction. In this book, we demonstrate how the tidyverse can be used to produce high quality models. The tools used to do this are referred to as the tidymodels packages