Row 8032

Row ID: 8032 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 8032 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Hi everyone!

We are organizing the **Differentiable almost everything** workshop at ICML this year.

Many discrete operations e.g. sorting, topk, shortest paths, clustering (and many more) have null-gradients almost everywhere, and are hence not suitable for modern gradient based learning frameworks (such as deep learning). This workshop will cover research topics that aim to remedy such problems!

[https://differentiable.xyz/](https://differentiable.xyz/)

We encourage anyone who is working on relevant topics to submit their work. Even if you are not submitting, please do come by the workshop at ICML to see some of the exciting talks that will take place!

I have attached a full summary of the workshop below! All the best with your current work, L :)

*Gradients and derivatives are integral to machine learning, as they enable gradient-based optimization. In many real applications, however, models rest on algorithmic components that implement discrete decisions, or rely on discrete intermediate representations and structures. These discrete steps are intrinsically non-differentiable and accordingly break the flow of gradients. To use gradient-based approaches to learn the parameters of such models requires turning these non-differentiable components differentiable. This can be done with careful considerations, notably, using smoothing or relaxations to propose differentiable proxies for these components. With the advent of modular deep learning frameworks, these ideas have become more popular than ever in many fields of machine learning, generating in a short time-span a multitude of “differentiable everything”, impacting topics as varied as rendering, sorting and ranking, convex optimizers, shortest-paths, dynamic programming, physics simulations, NN architecture search, top-k, graph algorithms, weakly- and self-supervised learning, and many more.*

*This workshop will provide a forum for anything differentiable, bringing together academic and industry researchers to highlight challenges and developments, provide unifying ideas, discuss practical implementation choices and explore future directions.*

Field	Value
text	Hi everyone! We are organizing the Differentiable almost everything workshop at ICML this year. Many discrete operations e.g. sorting, topk, shortest paths, clustering (and many more) have null-gradients almost everywhere, and are hence not suitable for modern gradient based learning frameworks (such as deep learning). This workshop will cover research topics that aim to remedy such problems! [https://differentiable.xyz/](https://differentiable.xyz/) We encourage anyone who is working on…
label	r/machinelearning
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communityName	r/MachineLearning
datetime	2024-05-18
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Raw Record

{
  "text": "Hi everyone!\n\nWe are organizing the **Differentiable almost everything** workshop at ICML this year.\n\nMany discrete operations e.g. sorting, topk, shortest paths, clustering (and many more) have null-gradients almost everywhere, and are hence not suitable for modern gradient based learning frameworks (such as deep learning). This workshop will cover research topics that aim to remedy such problems!\n\n[https://differentiable.xyz/](https://differentiable.xyz/)\n\nWe encourage anyone who is working on relevant topics to submit their work. Even if you are not submitting, please do come by the workshop at ICML to see some of the exciting talks that will take place!\n\nI have attached a full summary of the workshop below! All the best with your current work, L :)\n\n*Gradients and derivatives are integral to machine learning, as they enable gradient-based optimization. In many real applications, however, models rest on algorithmic components that implement discrete decisions, or rely on discrete intermediate representations and structures. These discrete steps are intrinsically non-differentiable and accordingly break the flow of gradients. To use gradient-based approaches to learn the parameters of such models requires turning these non-differentiable components differentiable. This can be done with careful considerations, notably, using smoothing or relaxations to propose differentiable proxies for these components. With the advent of modular deep learning frameworks, these ideas have become more popular than ever in many fields of machine learning, generating in a short time-span a multitude of “differentiable everything”, impacting topics as varied as rendering, sorting and ranking, convex optimizers, shortest-paths, dynamic programming, physics simulations, NN architecture search, top-k, graph algorithms, weakly- and self-supervised learning, and many more.*\n\n*This workshop will provide a forum for anything differentiable, bringing together academic and industry researchers to highlight challenges and developments, provide unifying ideas, discuss practical implementation choices and explore future directions.*",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-18",
  "username_encoded": "Z0FBQUFBQm5LakwzdV9kZV9teXkxc1JMZURKbEFsTUZjdVljVjJvVnVBRThxUWJtZUVMcnQ0Y00xendkc19heGl6SDVLU0cwSjZRZlJUcUZHZ0dZNXFvVnhtRWVvRU9zR0UyMDdkcGRwaUZSek1uOC1SMFE2Znc9",
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}

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Entry Information

Entry ID: 8032
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000