58 citations · 63 across the 6 of their papers we have counts for
9 papers
More than 83.9% of the zeros of the Riemann zeta function are distinct and more than 67.35% are simple and on the critical line
Kristian Muri Knausgård
Let N(T) count the nontrivial zeros of the Riemann zeta function up to height T with multiplicity, N_d(T) the distinct ones, and N_0^s(T) those that are simple and on the critical…
More than 83.69% of the zeros of the Riemann zeta function are distinct
Kristian Muri Knausgård
The lower asymptotic proportion of distinct nontrivial zeros of the Riemann zeta function, relative to the total number counted with multiplicity, is at least .…
Fast 3D Foundation Model Initialized Gaussian Splatting
Anurag Dalal, Daniel Hagen, Kjell G. Robbersmyr +1
This paper introduces a fast method for high-quality 3D Gaussian Splatting (3DGS) reconstruction without traditional Structure-from-Motion (SfM). The proposed approach leverages 3D…
Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review
Anurag Dalal, Daniel Hagen, Kjell G. Robbersmyr +1
Image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have gained attent…
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning
Martin Holen, Per-Arne Andersen, Kristian Muri Knausgård +1
This paper introduces two learning schemes for distributed agents in Reinforcement Learning (RL) environments, namely Reward-Weighted (R-Weighted) and Loss-Weighted (L-Weighted) gr…
A contrastive learning approach for individual re-identification in a wild fish population
Ørjan Langøy Olsen, Tonje Knutsen Sørdalen, Morten Goodwin +3
In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly be…
Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook
Morten Goodwin, Kim Tallaksen Halvorsen, Lei Jiao +7
The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These n…
Temperate Fish Detection and Classification: a Deep Learning based Approach
Kristian Muri Knausgård, Arne Wiklund, Tonje Knutsen Sørdalen +4
A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that c…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
Erlend Olsvik, Christian M. D. Trinh, Kristian Muri Knausgård +5
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environm…