no code implementations • 30 Dec 2023 • Anders Lansner, Naresh B Ravichandran, Pawel Herman
In this paper we characterize these different aspects of associative memory performance and benchmark six different learning rules on storage capacity and prototype extraction.
no code implementations • 28 Sep 2023 • Lele Cao, Gustaf Halvardsson, Andrew McCornack, Vilhelm von Ehrenheim, Pawel Herman
This paper addresses the growing application of data-driven approaches within the Private Equity (PE) industry, particularly in sourcing investment targets (i. e., companies) for Venture Capital (VC) and Growth Capital (GC).
no code implementations • 5 May 2023 • Naresh Ravichandran, Anders Lansner, Pawel Herman
We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure.
no code implementations • 13 Apr 2023 • Anders Lansner, Florian Fiebig, Pawel Herman
Theories and models of working memory (WM) were at least since the mid-1990s dominated by the persistent activity hypothesis.
no code implementations • 6 Mar 2023 • Qichen Xu, I. P. Miranda, Manuel Pereiro, Filipp N. Rybakov, Danny Thonig, Erik Sjöqvist, Pavel Bessarab, Anders Bergman, Olle Eriksson, Pawel Herman, Anna Delin
To demonstrate how this method works, we identify and analyze spin textures with topological charge $Q$ ranging from 1 to $-13$ (where antiskyrmions have $Q<0$) in the Pd/Fe/Ir(111) system, which we model using a classical atomistic spin Hamiltonian based on parameters computed from density functional theory.
1 code implementation • 31 Dec 2022 • Qichen Xu, Zhuanglin Shen, Manuel Pereiro, Pawel Herman, Olle Eriksson, Anna Delin
A long-standing and difficult problem in, e. g., condensed matter physics is how to find the ground state of a complex many-body system where the potential energy surface has a large number of local minima.
no code implementations • 30 Jun 2022 • Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule.
no code implementations • 28 Mar 2022 • Ruiyang Zhao, Zhao He, Tao Wang, Suhao Qiu, Pawel Herman, Yanle Hu, Chencheng Zhang, Dinggang Shen, Bomin Sun, Guang-Zhong Yang, Yuan Feng
Here we proposed a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling.
no code implementations • 29 Jun 2021 • Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
Learning internal representations from data using no or few labels is useful for machine learning research, as it allows using massive amounts of unlabeled data.
1 code implementation • 9 Jun 2021 • Artur Podobas, Martin Svedin, Steven W. D. Chien, Ivy B. Peng, Naresh Balaji Ravichandran, Pawel Herman, Anders Lansner, Stefano Markidis
The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas.
no code implementations • 1 Jan 2021 • Naresh Balaji, Anders Lansner, Pawel Herman
Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years.
no code implementations • 11 Oct 2020 • Stefano Markidis, Ivy Peng, Artur Podobas, Itthinat Jongsuebchoke, Gabriel Bengtsson, Pawel Herman
Our overall goal is to provide a general workflow for exploring particle trajectory space and automatically classifying particle trajectories from plasma simulations in an unsupervised manner.
no code implementations • 6 May 2020 • Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years.
no code implementations • 27 Mar 2020 • Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years.
no code implementations • 15 Aug 2019 • Vyacheslav Olshevsky, Yuri V. Khotyaintsev, Ahmad Lalti, Andrey Divin, Gian Luca Delzanno, Sven Anderzen, Pawel Herman, Steven W. D. Chien, Levon Avanov, Andrew P. Dimmock, Stefano Markidis
We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI).
no code implementations • 6 Oct 2018 • Steven W. D. Chien, Stefano Markidis, Chaitanya Prasad Sishtla, Luis Santos, Pawel Herman, Sai Narasimhamurthy, Erwin Laure
To measure TensorFlow I/O performance, we first design a micro-benchmark to measure TensorFlow reads, and then use a TensorFlow mini-application based on AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow.
Distributed, Parallel, and Cluster Computing