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A Summary of the Google Zürich Algorithms & Optimization Workshop
Recently, we hosted a workshop on Algorithms and Optimization in our office in Zürich, with the goal of fostering collab…
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**Title of the talk/workshop**
Accelerate your pandas workload using FireDucks at zero manual effort
**Abstract of the talk/workshop**
In general, a Data Scientist spends significant efforts in t…
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On first start of the client, we add widgets for each item in the conversation view when we click on the source for the first time. If there are thousands of messages/files/replies per source, there w…
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- Optimize AI algorithms and computational workflows to ensure scalability and efficiency in processing large volumes of user responses.
- Explore techniques such as distributed computing, parallel p…
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- [ ] [LMOps/README.md at main · microsoft/LMOps](https://github.com/microsoft/LMOps/blob/main/README.md?plain=1)
# LMOps/README.md at main · microsoft/LMOps
## LMOps
LMOps is a research initiati…
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To generate insights into when smart memory would be useful, we can consider some hypothetical scenarios:
**1. Applications with Complex Memory Usage Patterns:**
In applications with complex or un…
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#### Problem Statement:
Currently, deploying and managing secure cloud functions involves complex processes that can expose sensitive data. Creating a multi-layered deployment system using Zero-Knowl…
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@jjhforrest
I encountered a performance bottleneck while trying to solve a large MILP problem containing variables of tens of millions. Currently, the solver I am using is CBC, and I spent up to 6 h…
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**Is your feature request related to a problem? Please describe.**
An early experiment showed that there was large speed-up using _dense_ solvers https://github.com/colmap/colmap/pull/2161
The goa…
pwais updated
1 month ago
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Performance optimization for 500 concurrent jobs and large scale completed jobs. Related Issues: https://github.com/kubeflow/tf-operator/issues/965 https://github.com/kubeflow/tf-operator/issues/1079.…