activity
20122022
most citedOn the difficulty of training Recurrent Neural Networks

3.8k citations · 4k across the 9 of their papers we have counts for

collaborators

16 papers

cs.LG20221 cited

Benchmarking Learning Efficiency in Deep Reservoir Computing

Hugo Cisneros, Josef Sivic, Tomas Mikolov

It is common to evaluate the performance of a machine learning model by measuring its predictive power on a test dataset. This approach favors complicated models that can smoothly…

cs.AI2021

Classification of Discrete Dynamical Systems Based on Transients

Barbora Hudcová, Tomáš Mikolov

In order to develop systems capable of artificial evolution, we need to identify which systems can produce complex behavior. We present a novel classification method applicable to…

cs.NE20213 cited

Computational Hierarchy of Elementary Cellular Automata

Barbora Hudcová, Tomáš Mikolov

The complexity of cellular automata is traditionally measured by their computational capacity. However, it is difficult to choose a challenging set of computational tasks suitable…

nlin.CG20213 cited

Visualizing computation in large-scale cellular automata

Hugo Cisneros, Josef Sivic, Tomas Mikolov

Emergent processes in complex systems such as cellular automata can perform computations of increasing complexity, and could possibly lead to artificial evolution. Such a feat woul…

nlin.CG20203 cited

Classification of Complex Systems Based on Transients

Barbora Hudcova, Tomas Mikolov

In order to develop systems capable of modeling artificial life, we need to identify, which systems can produce complex behavior. We present a novel classification method applicabl…

cs.CL2020

Evaluating Online Continual Learning with CALM

Germán Kruszewski, Ionut-Teodor Sorodoc, Tomas Mikolov

Online Continual Learning (OCL) studies learning over a continuous data stream without observing any single example more than once, a setting that is closer to the experience of hu…