18 citations · 41 across the 13 of their papers we have counts for
17 papers
Using Set Learning to Improve Set-Valued Classifiers on Long-tailed Data
Kevin Kasa, Scott C. Lowe, Graham W. Taylor
Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets per input rather than fixed-si…
Masked Swingers: Harnessing Data Augmentation to Advance Autoencoders for Self-Supervised Learning
Anthony Fuller, Scott C. Lowe, Daniel G. Kyrollos +3
Self-supervised learning (SSL) removes the need for annotations and makes models that are capable across more domains than supervised learning. The autoencoder SSL framework learns…
BarcodeMAE+: Rethinking Masked Pretraining and Global Representations for DNA Barcode Foundation Models
Monireh Safari, Pablo Millan Arias, Scott C. Lowe +3
Many DNA foundation models are pretrained by masking parts of a sequence and asking the model to reconstruct them. Standard masked pretraining exposes the encoder to special [MASK]…
Self-Distillation of Hidden Layers for Self-Supervised Representation Learning
Scott C. Lowe, Anthony Fuller, Sageev Oore +2
The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.g. MAE) that reconstruct raw low-level data, and predictive approaches (e.g. I-JE…
A continental-scale dataset of ground beetles with high-resolution images and validated morphological trait measurements
S M Rayeed, Mridul Khurana, Alyson East +18
Despite the ecological significance of invertebrates, global trait databases remain heavily biased toward vertebrates and plants, limiting comprehensive ecological analyses of high…
BarcodeMamba+: Advancing State-Space Models for Fungal Biodiversity Research
Tiancheng Gao, Scott C. Lowe, Brendan Furneaux +2
Accurate taxonomic classification from DNA barcodes is a cornerstone of global biodiversity monitoring, yet fungi present extreme challenges due to sparse labelling and long-tailed…
Hyperbolic Multimodal Representation Learning for Biological Taxonomies
ZeMing Gong, Chuanqi Tang, Xiaoliang Huo +6
Taxonomic classification in biodiversity research involves organizing biological specimens into structured hierarchies based on evidence, which can come from multiple modalities su…
A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level
Johanna Orsholm, John Quinto, Hannu Autto +26
Insects comprise millions of species, many experiencing severe population declines under environmental and habitat changes. High-throughput approaches are crucial for accelerating…
Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens
Alyson East, Elizabeth G. Campolongo, Luke Meyers +25
1) Biological collections house millions of specimens with digital images increasingly available through open-access platforms. However, most imaging protocols were developed for h…
Enhancing DNA Foundation Models to Address Masking Inefficiencies
Monireh Safari, Pablo Millan Arias, Scott C. Lowe +3
Masked language modelling (MLM) as a pretraining objective has been widely adopted in genomic sequence modelling. While pretrained models can successfully serve as encoders for var…
System 2 Reasoning Capabilities Are Nigh
Scott C. Lowe
In recent years, machine learning models have made strides towards human-like reasoning capabilities from several directions. In this work, we review the current state of the liter…
Hierarchical Multi-Label Classification with Missing Information for Benthic Habitat Imagery
Isaac Xu, Benjamin Misiuk, Scott C. Lowe +3
In this work, we apply state-of-the-art self-supervised learning techniques on a large dataset of seafloor imagery, \textit{BenthicNet}, and study their performance for a complex h…
BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity
Zahra Gharaee, Scott C. Lowe, ZeMing Gong +10
As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, this paper presents the BIOSCAN-5M Insect dataset to the machine learning community and establ…
An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders
Scott C. Lowe, Joakim Bruslund Haurum, Sageev Oore +2
Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embe…
CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale
ZeMing Gong, Austin T. Wang, Xiaoliang Huo +4
Measuring biodiversity is crucial for understanding ecosystem health. While prior works have developed machine learning models for taxonomic classification of photographic images a…
BenthicNet: A global compilation of seafloor images for deep learning applications
Scott C. Lowe, Benjamin Misiuk, Isaac Xu +26
Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery…
BarcodeBERT: Transformers for Biodiversity Analysis
Pablo Millan Arias, Niousha Sadjadi, Monireh Safari +9
In the global challenge of understanding and characterizing biodiversity, short species-specific genomic sequences known as DNA barcodes play a critical role, enabling fine-grained…