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https://arxiv.org/abs/1706.02633
> Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propos…
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Dear Luyuan,
Your ICASSP paper, "TRLS: A TIME SERIES REPRESENTATION LEARNING FRAMEWORK VIA SPECTROGRAM FOR MEDICAL SIGNAL PROCESSING," was very interesting in addressing problems in medical time-se…
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#### **Healthcare Capabilities in AI**
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**1. AI Model Development**
- **Capabilities:**
- Crafting bespoke AI models tailored for healthcare applications.
- Leveraging dee…
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Hi Chongjie,
Thank you so much for your insightful work. May I kindly ask if there is any plan to support Colmap-free 3d Gaussian Splatting in your studio?
Sincerely,
Chenlin
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We need to develop (and maintain) a good base image for SDS 2.0 guest VMs, which will be used for GPU enabled VMs which AI research experts will be able access in private and be root on.
This card is…
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- PyTorch-Forecasting version: 0.8.5
- PyTorch version: 1.8.1
- Python version: 3.8
- Operating System: Linux
Hi there,
I had a similar question to #490 regarding how to code the group_ids fo…
ik362 updated
3 years ago
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### Problem Description
When using the PAR model for time series generation, it would be ideal to impose constraints on the resulting time series and/or on the context variables. For instance, so…
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I implemented RGAN and RCGAN from [1]. Would it make sense to add it to `torchgan.models`?
[1] [Esteban, Cristóbal, Stephanie L. Hyland, and Gunnar Rätsch. "Real-valued (medical) time series genera…
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The current framework is too much oriented towards Computer Vision problems where there is already lots of available resources. It would be great if you can also focus on multivariate time series wher…
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> The primary goal of a longitudinal study is to characterise the change in response over time and the factors that influence change. [fitzmaurice2012applied]
> Clustered data can arise from random…