Zobrazeno 1 - 10
of 53
pro vyhledávání: '"CURTIN, RYAN R."'
As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and exploit parallelism to reduce memory and runtime, but suffer increasingly
Externí odkaz:
http://arxiv.org/abs/2407.06346
While distributed training is often viewed as a solution to optimizing linear models on increasingly large datasets, inter-machine communication costs of popular distributed approaches can dominate as data dimensionality increases. Recent work on non
Externí odkaz:
http://arxiv.org/abs/2406.01753
This report provides an introduction to the Bandicoot C++ library for linear algebra and scientific computing on GPUs, overviewing its user interface and performance characteristics, as well as the technical details of its internal design. Bandicoot
Externí odkaz:
http://arxiv.org/abs/2308.03120
Autor:
Curtin, Ryan R., Edel, Marcus, Shrit, Omar, Agrawal, Shubham, Basak, Suryoday, Balamuta, James J., Birmingham, Ryan, Dutt, Kartik, Eddelbuettel, Dirk, Garg, Rishabh, Jaiswal, Shikhar, Kaushik, Aakash, Kim, Sangyeon, Mukherjee, Anjishnu, Sai, Nanubala Gnana, Sharma, Nippun, Parihar, Yashwant Singh, Swain, Roshan, Sanderson, Conrad
Publikováno v:
Journal of Open Source Software, Vol. 8, No. 82, 2023
For over 15 years, the mlpack machine learning library has served as a "swiss army knife" for C++-based machine learning. Its efficient implementations of common and cutting-edge machine learning algorithms have been used in a wide variety of scienti
Externí odkaz:
http://arxiv.org/abs/2302.00820
Autor:
Curtin, Ryan R., Edel, Marcus, Prabhu, Rahul Ganesh, Basak, Suryoday, Lou, Zhihao, Sanderson, Conrad
Publikováno v:
Journal of Machine Learning Research, Vol. 22, No. 166, 2021
We overview the ensmallen numerical optimization library, which provides a flexible C++ framework for mathematical optimization of user-supplied objective functions. Many types of objective functions are supported, including general, differentiable,
Externí odkaz:
http://arxiv.org/abs/2108.12981
Autor:
Curtin, Ryan R., Edel, Marcus, Prabhu, Rahul Ganesh, Basak, Suryoday, Lou, Zhihao, Sanderson, Conrad
This report provides an introduction to the ensmallen numerical optimization library, as well as a deep dive into the technical details of how it works. The library provides a fast and flexible C++ framework for mathematical optimization of arbitrary
Externí odkaz:
http://arxiv.org/abs/2003.04103
We design and mathematically analyze sampling-based algorithms for regularized loss minimization problems that are implementable in popular computational models for large data, in which the access to the data is restricted in some way. Our main resul
Externí odkaz:
http://arxiv.org/abs/1905.10845
Autor:
Khamis, Mahmoud Abo, Curtin, Ryan R., Moseley, Benjamin, Ngo, Hung Q., Nguyen, XuanLong, Olteanu, Dan, Schleich, Maximilian
Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some of the input factors are defined by a collection of additive inequali
Externí odkaz:
http://arxiv.org/abs/1812.09526
We present ensmallen, a fast and flexible C++ library for mathematical optimization of arbitrary user-supplied functions, which can be applied to many machine learning problems. Several types of optimizations are supported, including differentiable,
Externí odkaz:
http://arxiv.org/abs/1810.09361
Autor:
Curtin, Ryan R., Gardner, Andrew B., Grzonkowski, Slawomir, Kleymenov, Alexey, Mosquera, Alejandro
Modern malware typically makes use of a domain generation algorithm (DGA) to avoid command and control domains or IPs being seized or sinkholed. This means that an infected system may attempt to access many domains in an attempt to contact the comman
Externí odkaz:
http://arxiv.org/abs/1810.02023