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Paper Replications

Paper Replications is a GitHub collection of code implementations and experiments that reproduce machine-learning and deep-learning research papers. Its folders cover individual papers, models or techniques and include code, scripts, sometimes notebooks, and in some cases pretrained weights or results.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Paper Replications — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to explore a research-paper implementation or experiment by topic and can follow the instructions in its individual folder.

Who it is for

  • Readers studying machine-learning architectures and techniques through code.
  • Practitioners looking for a replication related to a particular paper or model.
  • Researchers exploring existing training experiments and implementations.

Documented uses

  • Browse implementations by model, paper or technique.
  • Read folder-specific instructions and inspect code, scripts or notebooks.
  • Study PyTorch approaches to research ideas.
  • Locate selected pretrained weights or results where a folder includes them.
Before you choose

Requirements & limits

The repository identifies its code and notebooks as Apache License 2.0 and its documentation, diagrams, generated results and published artifacts as CC BY 4.0.

  • Instructions are maintained within individual subfolders rather than as one repository-wide run procedure.
  • Pretrained weights and results are present only in some folders.
  • The README points to Smolhub for viewing deployed models, separately from the repository's code.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Find instructions for a paper replication

Choose a replication folder and locate its project-specific usage guidance.

Before you start

  • Access to the Paper-Replications repository.

Steps

  1. Browse the repository’s subfolders and select one corresponding to a paper, model, or technique of interest.

  2. Open that folder’s README or notes and follow its specific instructions.

Expected outcome

You can identify the code and instructions associated with a selected replication.

Guide limitations

  • The repository’s top-level README does not provide universal setup or execution steps; instructions vary by folder.
  • Folders may include code, scripts, notebooks, and sometimes pretrained weights or results.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
GitHub - YuvrajSingh-mist/Paper-Replications: From-scratch PyTorch replications of classic and SOTA AI/ML papers: transformers, attention, GANs, diffusion, CLIP, LoRA, MoE and more, with reproducible training code. · GitHub · Official homepage title · retained in its source language
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