116 citations · 174 across the 6 of their papers we have counts for
16 papers
Segmenting two-dimensional structures with strided tensor networks
Raghavendra Selvan, Erik B Dam, Jens Petersen
Tensor networks provide an efficient approximation of operations involving high dimensional tensors and have been extensively used in modelling quantum many-body systems. More rece…
Dynamic -VAEs for quantifying biodiversity by clustering optically recorded insect signals
Klas Rydhmer, Raghavendra Selvan
While insects are the largest and most diverse group of terrestrial animals, constituting ca. 80% of all known species, they are difficult to study due to their small size and simi…
Detection of foraging behavior from accelerometer data using U-Net type convolutional networks
Manh Cuong Ngô, Raghavendra Selvan, Outi Tervo +2
Narwhal is one of the most mysterious marine mammals, due to its isolated habitat in the Arctic region. Tagging is a technology that has the potential to explore the activities of…
Multi-layered tensor networks for image classification
Raghavendra Selvan, Silas Ørting, Erik B Dam
The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patc…
Locally orderless tensor networks for classifying two- and three-dimensional medical images
Raghavendra Selvan, Silas Ørting, Erik B Dam
Tensor networks are factorisations of high rank tensors into networks of lower rank tensors and have primarily been used to analyse quantum many-body problems. Tensor networks have…
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
Lasse F. Wolff Anthony, Benjamin Kanding, Raghavendra Selvan
Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware…