675 citations · 1.8k across the 55 of their papers we have counts for
8 papers · 1 filter
Mixed Models with Multiple Instance Learning
Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak +2
Predicting patient features from single-cell data can help identify cellular states implicated in health and disease. Linear models and average cell type expressions are typically…
De Novo Drug Design with Joint Transformers
Adam Izdebski, Ewelina Weglarz-Tomczak, Ewa Szczurek +1
De novo drug design requires simultaneously generating novel molecules outside of training data and predicting their target properties, making it a hard task for generative models.…
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…
Discouraging posterior collapse in hierarchical Variational Autoencoders using context
Anna Kuzina, Jakub M. Tomczak
Hierarchical Variational Autoencoders (VAEs) are among the most popular likelihood-based generative models. There is a consensus that the top-down hierarchical VAEs allow effective…
Learning Data Representations with Joint Diffusion Models
Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak
Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. In this work, we depart from…
Modelling Long Range Dependencies in D: From Task-Specific to a General Purpose CNN
David M. Knigge, David W. Romero, Albert Gu +5
Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data. In…