Ali Geisa

According to our database, Ali Geisa authored at least 9 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Simple Lifelong Learning Machines.

Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Tyler M. Tomita, Haoyin Xu, Ali Geisa, Qingyang Wang, Gido M. van de Ven, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M. White, Carey E. Priebe

IEEE Trans. Pattern Anal. Mach. Intell., November, 2025

2023

Polarity Is All You Need to Learn and Transfer Faster.

Qingyang Wang, Michael Alan Powell, Eric W. Bridgeford, Ali Geisa, Joshua T. Vogelstein

Proceedings of the International Conference on Machine Learning, 2023

Why do networks have inhibitory/negative connections?

Qingyang Wang, Michael A. Powell, Ali Geisa, Eric Bridgeford, Carey E. Priebe, Joshua T. Vogelstein

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Why Do Networks Need Negative Weights?

Qingyang Wang, Michael A. Powell, Ali Geisa, Eric Bridgeford, Joshua T. Vogelstein

CoRR, 2022

Out-of-distribution Detection Using Kernel Density Polytopes.

Jayanta Dey, Ashwin De Silva, Will LeVine, Jong M. Shin, Haoyin Xu, Ali Geisa, Tiffany Chu, Leyla Isik, Joshua T. Vogelstein

CoRR, 2022

Prospective Learning: Back to the Future.

Joshua T. Vogelstein, Timothy D. Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam S. Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael L. Platt, Lyle H. Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang

CoRR, 2022

2021

Towards a theory of out-of-distribution learning.

Ali Geisa, Ronak D. Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Carey E. Priebe, Joshua T. Vogelstein

CoRR, 2021

Inducing a hierarchy for multi-class classification problems.

Hayden S. Helm, Weiwei Yang, Sujeeth Bharadwaj, Kate Lytvynets, Oriana Riva, Christopher M. White, Ali Geisa, Carey E. Priebe

CoRR, 2021

2020

A partition-based similarity for classification distributions.

Hayden S. Helm, Ronak D. Mehta, Brandon Duderstadt, Weiwei Yang, Christopher M. White, Ali Geisa, Joshua T. Vogelstein, Carey E. Priebe

CoRR, 2020