1 code implementation • 4 Dec 2023 • Gabriel della Maggiora, Luis Alberto Croquevielle, Nikita Deshpande, Harry Horsley, Thomas Heinis, Artur Yakimovich
Despite their success, an important drawback of diffusion models is their sensitivity to the choice of variance schedule, which controls the dynamics of the diffusion process.
no code implementations • 28 May 2022 • Jamie J. Alnasir, Thomas Heinis, Louis Carteron
Advances in DNA technologies have made it possible to store the entirety of Wikipedia in a test tube and read that information using a handheld sequencing device, although imperfections in writing (synthesis) and reading (sequencing) need to be mitigated for it to be viable as a mainstream storage medium.
no code implementations • 11 May 2022 • Jasmine Quah, Omer Sella, Thomas Heinis
In this paper, we study accuracy trade-offs between deep model size and error correcting codes.
no code implementations • 29 Jun 2020 • Ali Hadian, Behzad Ghaffari, Taiyi Wang, Thomas Heinis
The initial work on learned indexes has shown that by learning the cumulative distribution function of the data, index structures such as the B-Tree can improve their performance by one order of magnitude while having a smaller memory footprint.
no code implementations • 29 Jun 2020 • Ali Hadian, Ankit Kumar, Thomas Heinis
Spatial indexes are crucial for the analysis of the increasing amounts of spatial data, for example generated through IoT applications.
no code implementations • 24 Jun 2019 • Thomas Heinis, Roman Sokolovskii, Jamie J. Alnasir
Whilst biological information is encoded in DNA via a specific mapping from triplet sequences of nucleotides to amino acids, DNA storage is not limited to a single encoding scheme, and there are many possible ways to map data to chemical sequences of nucleotides for synthesis, storage, retrieval and data manipulation.