Code to reproduce the results for Compositional Attention

Sarthak Mittal

Last update: Nov 30, 2022

Related tags

Deep Learning Compositional-Attention

Overview

Compositional-Attention

This repository contains the official implementation for the paper Compositional Attention: Disentangling Search and Retrieval.

Our code is built on top of open-sourced implementations of multiple different tasks. We thank and reference these helpful implementations below.

We refer the readers to the respective sub-directories for details regarding each of the experiments. Do cite our work if you build up on it or find it useful.

@misc{mittal2021compositional,
      title={Compositional Attention: Disentangling Search and Retrieval}, 
      author={Sarthak Mittal and Sharath Chandra Raparthy and Irina Rish and Yoshua Bengio and Guillaume Lajoie},
      year={2021},
      eprint={2110.09419},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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Code to reproduce the experiments from our NeurIPS 2021 paper " The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective"

Code To run: python runner.py new --save SAVE_NAME --data PATH_TO_DATA_DIR --dataset DATASET --model model_name [options] --n 1000 - train - t

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Source code and notebooks to reproduce experiments and benchmarks on Bias Faces in the Wild (BFW).

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Comments

question

Very good job. Congratulations! And thank you for sharing codes. I have some questions, Which variables represent search and retrieval? Are qk_dim and nrules?

opened by banxianerr 1
CVE-2007-4559 Patch

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

opened by TrellixVulnTeam 0

Owner

Sarthak Mittal

Graduate Student Department of Mathematics Universite de Montreal / MILA

GitHub

Code to reproduce the results for Statistically Robust Neural Network Classification, published in UAI 2021

1 Jun 2, 2022

In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021

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Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020).

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This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

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Code to reproduce the results for Compositional Attention

Related tags

Overview

Compositional-Attention

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PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

Comments

question

CVE-2007-4559 Patch

Patching CVE-2007-4559

Owner

Sarthak Mittal

Code to reproduce the results for Statistically Robust Neural Network Classification, published in UAI 2021

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