82 citations · 95 across the 7 of their papers we have counts for
7 papers
Generative AI Systems: A Systems-based Perspective on Generative AI
Jakub M. Tomczak
Large Language Models (LLMs) have revolutionized AI systems by enabling communication with machines using natural language. Recent developments in Generative AI (GenAI) like Vision…
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies
Jie Luo, Jakub Tomczak, Karine Miras +1
The main question this paper addresses is: What combination of a robot controller and a learning method should be used, if the morphology of the learning robot is not known in adva…
Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better
Jie Luo, Karine Miras, Jakub Tomczak +1
Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if…
Exploring Continual Learning of Diffusion Models
Michał Zając, Kamil Deja, Anna Kuzina +4
Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data. However, tra…
Towards a General Purpose CNN for Long Range Dependencies in D
David W. Romero, David M. Knigge, Albert Gu +4
The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learn…
Improving Variational Auto-Encoders using Householder Flow
Jakub M. Tomczak, Max Welling
Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Co…