DRSR | 20 “ A Deep Recurrent Survival Model | Machine Learning library

by Jinjiarui Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | DRSR Summary

DRSR is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. DRSR has no bugs, it has no vulnerabilities and it has low support. However DRSR build file is not available. You can download it from GitHub.

This is a tensorflow implementation of DRSA model. This is the experiment code in the following work:. A Deep Survival Model for Unbiased Ranking Jiarui Jin, Yuchen Fang, Weinan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai. SIGIR 2020 Oral.

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Quality

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Support

DRSR has a low active ecosystem.

It has 44 star(s) with 3 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

DRSR has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DRSR is current.

Quality

DRSR has no bugs reported.

Security

DRSR has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

DRSR does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

DRSR releases are not available. You will need to build from source code and install.

DRSR has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed DRSR and discovered the below as its top functions. This is intended to give you an instant insight into DRSR implemented functionality, and help decide if they suit your requirements.

Generate a batch replay buffer
Pads the data to the correct padding
Load the relevance data
Load feature data
Construct the network template
Embedding embedding
Creates the function network
Build the network
Build the loss network
Calculate the point loss
Generate the final list for each query
Write summary to train writer
Generate rank list based on rerank_score
Evaluate RNN
Test the RNN s RNN model
Test RNN algorithm
Train the RNN
Train the RNN model
Train the DNN
Train the DNN batch
Test the DNN
Runs the test
Tests RNN
Test the RNN batch
Build the train op
Calculate softmax loss loss

Get all kandi verified functions for this library.

DRSR Key Features

No Key Features are available at this moment for DRSR.

DRSR Examples and Code Snippets

No Code Snippets are available at this moment for DRSR.

Community Discussions

Trending Discussions on DRSR

Is there any way to retrieve password hashes from an Active Directory via LDAP?

QUESTION

Is there any way to retrieve password hashes from an Active Directory via LDAP?

Asked 2019-Dec-19 at 13:51

I know one can retrieve password hashes via RPC (ms-drsr), like it's done in the DSInternals PowerShell Module. Is there any way to get the MD4 hashes via LDAP?

So far I've tried using the AdDirSyncRequest control (1.2.840.113556.1.4.841), but with no luck. I can see the password attributes (ntPwdHistory, unicodePwd, etc) but they have no value.

...

ANSWER

Answered 2019-Dec-19 at 13:51

No, there isn't. The unicodePwd attribute is used to set the password, but the documentation says:

The unicodePwd attribute is never returned by an LDAP search.

Source https://stackoverflow.com/questions/59406408

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install DRSR

You can download it from GitHub.
You can use DRSR like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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