PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

Autor: Nicolas Collignon, Benedek Rozemberczki, Rik Sarkar, Yixuan He, Paul Scherer, Alexander Riedel, Maria Astefanoaei, Guzmán López, Ferenc Béres, George Panagopoulos, Oliver Kiss

Publikováno v: Rozemberczki, B, Scherer, P, He, Y, Panagopoulos, G, Riedel, A, Astefanoaei, M S, Kiss, O, Béres, F, López, G, Collignon, N & Sarkar, R 2021, PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models . in G Demartini, G Zuccon, J S Culpepper, Z Huang & H Tong (eds), CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1-5, 2021 . Association for Computing Machinery, pp. 4564-4573 . https://doi.org/10.1145/3459637.3482014
CIKM
Rozemberczki, B, Scherer, P, He, Y, Panagopoulos, G, Riedel, A, Astefanoaei, M, Kiss, O, Beres, F, López, G, Collignon, N & Sarkar, R 2021, PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models . in CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management . Association for Computing Machinery (ACM), pp. 4564–4573, 30th ACM International Conference on Information and Knowledge Management, Gold Coast, Queensland, Australia, 1/11/21 . https://doi.org/10.1145/3459637.3482014

We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8003fd3766bbede9435be31164a1b04e
https://pure.itu.dk/portal/da/publications/da6e2236-eb6c-4468-918d-4907c16c869b

Multi-resolution sketches and locality sensitive hashing for fast trajectory processing

Autor: Paul Cesaretti, Mayank Goswami, Panagiota Katsikouli, Rik Sarkar, Maria Astefanoaei

Publikováno v: SIGSPATIAL 2018-International Conference on Advances in Geographic Information Systems
SIGSPATIAL 2018-International Conference on Advances in Geographic Information Systems, Nov 2018, Seattle, United States. ⟨10.1145/3274895.3274943⟩
Astefanoaei, M, Cesaretti, P, Katsikouli, P, Goswami, M & Sarkar, R 2018, Multi-resolution sketches and locality sensitive hashing for fast trajectory processing . in 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’18) : November 6–9, 2018, Seattle, WA, USA . Seattle, Washington, USA, pp. 279-288, 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2018), Seattle, United States, 6/11/18 . https://doi.org/10.1145/3274895.3274943
SIGSPATIAL/GIS

International audience; Searching for similar GPS trajectories is a fundamental problem that faces challenges of large data volume and intrinsic complexity of trajectory comparison. In this paper, we present a suite of sketches for trajectory data th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7515789d39ed47762c7f5728d03f473a
https://inria.hal.science/hal-01893304/file/sigspatial2018.pdf