1 code implementation • 8 May 2024 • Scott C. Lowe, Benjamin Misiuk, Isaac Xu, Shakhboz Abdulazizov, Amit R. Baroi, Alex C. Bastos, Merlin Best, Vicki Ferrini, Ariell Friedman, Deborah Hart, Ove Hoegh-Guldberg, Daniel Ierodiaconou, Julia Mackin-McLaughlin, Kathryn Markey, Pedro S. Menandro, Jacquomo Monk, Shreya Nemani, John O'Brien, Elizabeth Oh, Luba Y. Reshitnyk, Katleen Robert, Chris M. Roelfsema, Jessica A. Sameoto, Alexandre C. G. Schimel, Jordan A. Thomson, Brittany R. Wilson, Melisa C. Wong, Craig J. Brown, Thomas Trappenberg
Advances in underwater imaging enable the collection of extensive seafloor image datasets that are necessary for monitoring important benthic ecosystems.
no code implementations • 2 Nov 2021 • Scott C. Lowe, Thomas Trappenberg, Sageev Oore
We seek to improve the pooling operation in neural networks, by applying a more theoretically justified operator.
no code implementations • 22 Oct 2021 • Scott C. Lowe, Robert Earle, Jason d'Eon, Thomas Trappenberg, Sageev Oore
Consequently, we construct efficient approximations named $\text{AND}_\text{AIL}$ (the AND operator Approximate for Independent Logits), $\text{OR}_\text{AIL}$, and $\text{XNOR}_\text{AIL}$, which utilize only comparison and addition operations, have well-behaved gradients, and can be deployed as activation functions in neural networks.
1 code implementation • 10 Dec 2019 • Abraham Nunes, Martin Alda, Timothy Bardouille, Thomas Trappenberg
Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require {a priori} (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems with ill-defined categories or where semantically relevant features must be learned as abstractions from some data.
no code implementations • 10 Sep 2019 • Andre G. C. Pacheco, Abder-Rahman Ali, Thomas Trappenberg
We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019.
no code implementations • ICLR 2018 • Michael Traynor, Thomas Trappenberg
This work introduces a simple network for producing character aware word embeddings.
no code implementations • 20 Dec 2014 • Thomas Trappenberg, Paul Hollensen, Pitoyo Hartono
In this study we want to connect our previously proposed context-relevant topographical maps with the deep learning community.