Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning

1 code implementation • 4 Sep 2024 • Raphael Lafargue, Luke Smith, Franck Vermet, Mathias Löwe, Ian Reid, Vincent Gripon, Jack Valmadre

The predominant method for computing confidence intervals (CI) in few-shot learning (FSL) is based on sampling the tasks with replacement, i. e.\ allowing the same samples to appear in multiple tasks.

Few-Shot Learning

Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning

no code implementations • 5 Jul 2021 • Mathias Löwe, Jennifer Villareale, Evan Freed, Aleksanteri Sladek, Jichen Zhu, Sebastian Risi

In this paper, we focus on the adversarial player strategy aspect in the game iNNk, in which players try to communicate secret code words through drawings with the goal of not being deciphered by a NN.

Ensemble Learning Transfer Learning

Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation

no code implementations • 29 Mar 2021 • Mathias Löwe, Per Lunnemann Hansen, Sebastian Risi

For a BM, making predictions with the lowest expected loss requires integrating over the posterior distribution.

Active Learning Deep Learning

Player-AI Interaction: What Neural Network Games Reveal About AI as Play

no code implementations • 15 Jan 2021 • Jichen Zhu, Jennifer Villareale, Nithesh Javvaji, Sebastian Risi, Mathias Löwe, Rush Weigelt, Casper Harteveld

The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research.

iNNk: A Multi-Player Game to Deceive a Neural Network

no code implementations • 17 Jul 2020 • Jennifer Villareale, Ana Acosta-Ruiz, Samuel Arcaro, Thomas Fox, Evan Freed, Robert Gray, Mathias Löwe, Panote Nuchprayoon, Aleksanteri Sladek, Rush Weigelt, Yifu Li, Sebastian Risi, Jichen Zhu

This paper presents iNNK, a multiplayer drawing game where human players team up against an NN.