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Autor:
Kurach, Karol, Raichuk, Anton, Stańczyk, Piotr, Zając, Michał, Bachem, Olivier, Espeholt, Lasse, Riquelme, Carlos, Vincent, Damien, Michalski, Marcin, Bousquet, Olivier, Gelly, Sylvain
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner. We introduce the Google Resear
Externí odkaz:
http://arxiv.org/abs/1907.11180
Publikováno v:
Litʹë i Metallurgiâ, Vol 0, Iss 1, Pp 73-78 (2023)
Dispersed iron‑containing waste makes up the majority of solid technological waste of machine‑building and metallurgical enterprises. The problem of their disposal remains open, which leads to significant economic losses and creates a serious env
Externí odkaz:
https://doaj.org/article/5918d19de1b94a6384591e475f26d1b9
Autor:
Agnieszka Chłopaś-Konowałek, Marcin Zawadzki, Łukasz Kurach, Olga Wachełko, Rafał Ciaputa, Kaja Tusiewicz, Paweł Szpot
Publikováno v:
Archives of Forensic Medicine and Criminology, Vol 2022, Iss 2, Pp 67-80 (2022)
Aim: Bendiocarb is used against a wide range of insects but has already been withdrawn from the market in some countries. It poses a high risk to birds as they can accidentally ingest it while searching for food, followed by toxic effects. This paper
Externí odkaz:
https://doaj.org/article/5856787618344297a1e72c9c12cc6918
Autor:
Unterthiner, Thomas, van Steenkiste, Sjoerd, Kurach, Karol, Marinier, Raphael, Michalski, Marcin, Gelly, Sylvain
Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider videos. Lear
Externí odkaz:
http://arxiv.org/abs/1812.01717
Autor:
Zamorski, Maciej, Zięba, Maciej, Klukowski, Piotr, Nowak, Rafał, Kurach, Karol, Stokowiec, Wojciech, Trzciński, Tomasz
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtain meaningful representations of 3D shapes that can be used for challen
Externí odkaz:
http://arxiv.org/abs/1811.07605
Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several
Externí odkaz:
http://arxiv.org/abs/1810.10340
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task
Externí odkaz:
http://arxiv.org/abs/1807.04720
We propose a new learning paradigm called Deep Memory. It has the potential to completely revolutionize the Machine Learning field. Surprisingly, this paradigm has not been reinvented yet, unlike Deep Learning. At the core of this approach is the \te
Externí odkaz:
http://arxiv.org/abs/1803.11203
Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. W
Externí odkaz:
http://arxiv.org/abs/1711.10337