RAINNET SYSTEMS
Rainnet is building next generation network automation technology that will enable companies to transform their networks from being inflexible, brittle, vulnerable, and expensive to being agile, reliable, secure, and inexpensive.
RAINNET SYSTEMS
Industry:
Computer Information Technology Marketing Automation Software
Founded:
2013-01-01
Address:
Seattle, Washington, United States
Country:
United States
Website Url:
http://www.rainnet.com
Total Employee:
1+
Status:
Active
Contact:
(216)455-3216
Total Funding:
3 M USD
Technology used in webpage:
Viewport Meta IPhone / Mobile Compatible SPF SSL By Default Apple Mobile Web Clips Icon Content Delivery Network Microsoft Exchange Online Office 365 Mail Microsoft Azure DNS GoDaddy DNS
Similar Organizations
Version 1
Version 1 provides a variety of IT solutions and services that impact customer's businesses.
Current Employees Featured
Shivaraj Tenginakai CEO & Founder @ Rainnet Systems
CEO & Founder
2014-01-01
Sudip Chakrabarti Investor @ Rainnet Systems
Investor
2016-08-01
Founder
Official Site Inspections
http://www.rainnet.com
- Host name: 147.62.236.23.bc.googleusercontent.com
- IP address: 23.236.62.147
- Location: Mountain View United States
- Latitude: 37.4043
- Longitude: -122.0748
- Metro Code: 807
- Timezone: America/Los_Angeles
- Postal: 94043
More informations about "Rainnet Systems"
RainNet v1.0: a convolutional neural network for radar …
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary …See details»
GitHub - hydrogo/rainnet: RainNet: a convolutional neural …
RainNet v1.0: a convolutional neural network for radar-based ...
In this study, we present RainNet, a deep convo-lutional neural network for radar-based precipitation now-casting. Its design was inspired by the U-Net and SegNet families of deep …See details»
RainNet v1.0: a convolutional neural network for radar …
Jun 11, 2020 · In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models,...See details»
RainNet v1.0: a convolutional neural network for radar …
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly …See details»
RainNet v1.0: a convolutional neural network for radar-based ...
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep …See details»
RainNet v1.0: a convolutional neural network for radar …
Mar 4, 2020 · In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models...See details»
RainNet | Proceedings of the 36th International Conference on …
To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named RainNet, which contains more than 62,400 pairs of high-quality low/high …See details»
GitHub - neuralchen/RainNet: [NeurIPS 2022]RainNet: …
Sep 8, 2024 · Data preparation. Run the 'dataset_prepare_hdf5.py' to process the dataset into patches. In 'dataset_prepare_hdf5.py', variable 'dataset_path' sets the hdf5 file path of RainNet; 'patch_hdf5_root' sets the target path to save …See details»
RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling
Dec 17, 2020 · In order to facilitate the research on precipitation downscaling for deep learning, we present the first REAL (non-simulated) Large-Scale Spatial Precipitation Downscaling …See details»
RainNet: A Large-Scale Imagery Dataset and Benchmark for
RainNet are organized in the form of image sequences (720 maps per month or 1 map/hour), showing complex physical properties, e.g., temporal misalignment, tem- poral sparse, and …See details»
Samples in RainNet - GitHub Pages
To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named RainNet, which contains more than 62,400 pairs of high-quality low/high …See details»
RainNet v1.0 - ResearchGate
Jul 8, 2020 · In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of …See details»
Rainymotion and RainNet: optical flow and deep learning models …
Rainymotion and RainNet: optical flow and deep learning models for radar-based precipitation nowcasting. Title: Rainymotion and RainNet: optical flow and deep learning models for radar …See details»
RAINNET: A LARGE-SCALE IMAGERY DATASET FOR SPATIAL
14 RainNet are organized in the form of image sequences (720 maps per month or 15 1 map/hour), showing complex physical properties, e.g., temporal misalignment, 16 temporal …See details»
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial ...
Dec 17, 2020 · The first large-scale spatial precipitation downscaling dataset named RainNet is presented, which contains more than $62,400$ pairs of high-quality low/high-resolution …See details»
RainNet v1.0: a convolutional neural network for radar-based ...
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep …See details»
RainNet v1.0: a convolutional neural network for radar-based ...
Sep 2, 2020 · In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation …See details»
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial ...
To illustrate the applications of RainNet, 14 state-of-the-art models, including deep models and traditional approaches, are evaluated. To fully explore potential downscaling solutions, we …See details»