no code implementations • 4 Feb 2025 • Frederik Lizak Johansen, Ulrik Friis-Jensen, Erik Bjørnager Dam, Kirsten Marie Ørnsbjerg Jensen, Rocío Mercado, Raghavendra Selvan
In this work, we introduce an autoregressive language model that performs crystal structure prediction (CSP) from powder diffraction data.
no code implementations • 12 Dec 2024 • Bob Pepin, Christian Igel, Raghavendra Selvan
We give explicit expressions for the bias resulting from memorization in terms of the label and group membership distribution of the memorized dataset and the classifier bias on the unmemorized dataset.
1 code implementation • 3 Jun 2024 • Dustin Wright, Christian Igel, Raghavendra Selvan
BMRS is based on two recent methods: Bayesian structured pruning with multiplicative noise, and Bayesian model reduction (BMR), a method which allows efficient comparison of Bayesian models under a change in prior.
no code implementations • 19 Mar 2024 • Hongwei Bran Li, Fernando Navarro, Ivan Ezhov, Amirhossein Bayat, Dhritiman Das, Florian Kofler, Suprosanna Shit, Diana Waldmannstetter, Johannes C. Paetzold, Xiaobin Hu, Benedikt Wiestler, Lucas Zimmer, Tamaz Amiranashvili, Chinmay Prabhakar, Christoph Berger, Jonas Weidner, Michelle Alonso-Basant, Arif Rashid, Ujjwal Baid, Wesam Adel, Deniz Ali, Bhakti Baheti, Yingbin Bai, Ishaan Bhatt, Sabri Can Cetindag, WenTing Chen, Li Cheng, Prasad Dutand, Lara Dular, Mustafa A. Elattar, Ming Feng, Shengbo Gao, Henkjan Huisman, Weifeng Hu, Shubham Innani, Wei Jiat, Davood Karimi, Hugo J. Kuijf, Jin Tae Kwak, Hoang Long Le, Xiang Lia, Huiyan Lin, Tongliang Liu, Jun Ma, Kai Ma, Ting Ma, Ilkay Oksuz, Robbie Holland, Arlindo L. Oliveira, Jimut Bahan Pal, Xuan Pei, Maoying Qiao, Anindo Saha, Raghavendra Selvan, Linlin Shen, Joao Lourenco Silva, Ziga Spiclin, Sanjay Talbar, Dadong Wang, Wei Wang, Xiong Wang, Yin Wang, Ruiling Xia, Kele Xu, Yanwu Yan, Mert Yergin, Shuang Yu, Lingxi Zeng, Yinglin Zhang, Jiachen Zhao, Yefeng Zheng, Martin Zukovec, Richard Do, Anton Becker, Amber Simpson, Ender Konukoglu, Andras Jakab, Spyridon Bakas, Leo Joskowicz, Bjoern Menze
The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.
1 code implementation • 19 Mar 2024 • Raghavendra Selvan, Bob Pepin, Christian Igel, Gabrielle Samuel, Erik B Dam
In this work, we take a comprehensive look at the landscape of existing DL models for medical image analysis tasks and demonstrate their usefulness in settings where resources are limited.
2 code implementations • 14 Mar 2024 • Hallgrimur Thorsteinsson, Valdemar J Henriksen, Tong Chen, Raghavendra Selvan
We present experiments on two benchmark datasets showing that adversarial fine-tuning of compressed models can achieve robustness performance comparable to adversarially trained models, while also improving computational efficiency.
1 code implementation • 20 Feb 2024 • Ulrik Friis-Jensen, Frederik L. Johansen, Andy S. Anker, Erik B. Dam, Kirsten M. Ø. Jensen, Raghavendra Selvan
We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1. 2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K).
Ranked #1 on
X-ray PDF regression
on CHILI-100K
1 code implementation • 8 Feb 2024 • Tong Chen, Raghavendra Selvan
This synthetic dataset retains the essential information of the original dataset, enabling models trained on it to achieve performance levels comparable to those trained on the full dataset.
1 code implementation • 21 Sep 2023 • Sebastian Eliassen, Raghavendra Selvan
Further, we present a correction to the assumptions on the distribution of intermediate activation maps in EXACT (assumed to be uniform) and show improved variance estimations of the quantization and dequantization steps.
no code implementations • 5 Sep 2023 • Dustin Wright, Christian Igel, Gabrielle Samuel, Raghavendra Selvan
The solution lionized by both industry and the ML community to improve the environmental sustainability of ML is to increase the efficiency with which ML systems operate in terms of both compute and energy consumption.
1 code implementation • 17 Mar 2023 • Raghavendra Selvan, Julian Schön, Erik B Dam
The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive.
1 code implementation • 13 Jan 2023 • Julian Schön, Raghavendra Selvan, Lotte Nygård, Ivan Richter Vogelius, Jens Petersen
This, in contrast to previous methods, allows us to synthesize continuous, smooth, and high-quality longitudinal volumetric data with limited supervision.
2 code implementations • 12 Oct 2022 • Pedram Bakhtiarifard, Christian Igel, Raghavendra Selvan
We advocate for including energy efficiency as an additional performance criterion in NAS.
1 code implementation • 23 Aug 2022 • Nicklas Boserup, Raghavendra Selvan
Contrastive self-supervised learning provides a framework to learn meaningful representations using learned notions of similarity measures from simple pretext tasks.
1 code implementation • 20 Jul 2022 • Julian Schön, Raghavendra Selvan, Jens Petersen
The results show that unsupervised methods to discover interpretable directions in GANs generalize to VAEs and can be applied to medical images.
no code implementations • 4 Mar 2022 • Raghavendra Selvan, Nikhil Bhagwat, Lasse F. Wolff Anthony, Benjamin Kanding, Erik B. Dam
In this study, we present and compare the features of four tools from literature to quantify the carbon footprint of DL.
no code implementations • 15 Sep 2021 • Raghavendra Selvan, Erik B Dam, Søren Alexander Flensborg, Jens Petersen
The performance of the proposed tensor network segmentation model is compared with relevant baseline methods.
1 code implementation • 14 Sep 2021 • Justinas Antanavicius, Roberto Leiras, Raghavendra Selvan
The correspondence between the partial mouse brain image and reference atlas plate is determined based on the distance between low dimensional embeddings of brain slices and atlas plates that are obtained from Siamese networks using contrastive learning.
1 code implementation • 13 Feb 2021 • Raghavendra Selvan, Erik B Dam, Jens Petersen
We use the matrix product state (MPS) tensor network on non-overlapping patches of a given input image to predict the segmentation mask by learning a pixel-wise linear classification rule in a high dimensional space.
1 code implementation • 10 Feb 2021 • Klas Rydhmer, Raghavendra Selvan
While insects are the largest and most diverse group of terrestrial animals, constituting ca.
1 code implementation • 13 Nov 2020 • 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 patches.
1 code implementation • 25 Sep 2020 • Raghavendra Selvan, Silas Ørting, Erik B. Dam
The proposed locally orderless tensor network (LoTeNet) is compared with relevant methods on three datasets.
1 code implementation • 6 Jul 2020 • Lasse F. Wolff Anthony, Benjamin Kanding, Raghavendra Selvan
In this work, we present Carbontracker, a tool for tracking and predicting the energy and carbon footprint of training DL models.
1 code implementation • 4 Jun 2020 • Raghavendra Selvan, Frederik Faye, Jon Middleton, Akshay Pai
In this work, we propose a novel conditional generative model that is based on conditional Normalizing Flow (cFlow).
3 code implementations • 20 May 2020 • Raghavendra Selvan, Erik B. Dam, Nicki S. Detlefsen, Sofus Rischel, Kaining Sheng, Mads Nielsen, Akshay Pai
Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19).
2 code implementations • MIDL 2019 • Raghavendra Selvan, Erik B. Dam
With the increasing adoption of machine learning tools like neural networks across several domains, interesting connections and comparisons to concepts from other domains are coming to light.
no code implementations • 22 Aug 2019 • Antonio Garcia-Uceda Juarez, Raghavendra Selvan, Zaigham Saghir, Marleen de Bruijne
In this approach, the convolutional layers at the deepest level of the UNet are replaced by a GNN-based module with a series of graph convolutions.
1 code implementation • 28 Feb 2019 • Abraham George Smith, Jens Petersen, Raghavendra Selvan, Camilla Ruø Rasmussen
We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0. 9748 and an $r^2$ of 0. 9217.
1 code implementation • 21 Nov 2018 • Raghavendra Selvan, Thomas Kipf, Max Welling, Antonio Garcia-Uceda Juarez, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications.
no code implementations • 23 Jun 2018 • Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne
We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters.
no code implementations • 12 Apr 2018 • Raghavendra Selvan, Thomas Kipf, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne
We present extraction of tree structures, such as airways, from image data as a graph refinement task.
no code implementations • 10 Apr 2018 • Raghavendra Selvan, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne
Performance of the method is compared with two methods: the first uses probability images from a trained voxel classifier with region growing, which is similar to one of the best performing methods at EXACT'09 airway challenge, and the second method is based on Bayesian smoothing on these probability images.
no code implementations • 7 Aug 2017 • Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne
The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector.
no code implementations • 24 Nov 2016 • Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne
The results show improvements in performance when compared to the original method and region growing on intensity images.