no code implementations • 29 Mar 2024 • Musashi Hinck, Matthew L. Olson, David Cobbley, Shao-Yen Tseng, Vasudev Lal
We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs).
no code implementations • 6 Dec 2023 • Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan, Bogdan Kustowski, Weng-Keen Wong, Rushil Anirudh
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains.
1 code implementation • CVPR 2023 • Matthew L. Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Weng-Keen Wong
To this end, we introduce Cross-GAN Auditing (xGA) that, given an established "reference" GAN and a newly proposed "client" GAN, jointly identifies intelligible attributes that are either common across both GANs, novel to the client GAN, or missing from the client GAN.
2 code implementations • 24 Feb 2023 • Tobias Huber, Maximilian Demmler, Silvan Mertes, Matthew L. Olson, Elisabeth André
However, research focusing on counterfactual explanations, specifically for RL agents with visual input, is scarce and does not go beyond identifying defective agents.
no code implementations • 27 Sep 2022 • Matthew L. Olson
Artistic work leveraging Machine Learning techniques is an increasingly popular endeavour for those with a creative lean.
no code implementations • 18 Aug 2021 • Matthew L. Olson, Thuy-Vy Nguyen, Gaurav Dixit, Neale Ratzlaff, Weng-Keen Wong, Minsuk Kahng
Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data.
2 code implementations • 29 Jan 2021 • Matthew L. Olson, Roli Khanna, Lawrence Neal, Fuxin Li, Weng-Keen Wong
Our second user study investigates if counterfactual state explanations can help non-expert participants identify a flawed agent; we compare against a baseline approach based on a nearest neighbor explanation which uses images from the actual game.
no code implementations • 27 Sep 2019 • Matthew L. Olson, Lawrence Neal, Fuxin Li, Weng-Keen Wong
In this work, we introduce the concept of a counterfactual state to help humans gain a better understanding of what would need to change (minimally) in an Atari game image for the agent to choose a different action.