ML Research Hub – Telegram
ML Research Hub
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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📃 Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey

🗓 Publish year: 2024

📱 Authors: Qizhi Pei, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, ...
🏠 University: Renmin University of China, University of Science and Technology of China, Microsoft Research

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📦 Related sources and contents

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📑 Ten simple rules for designing graphical abstracts

📕 Journal: Plos Computational Biology (I.F.=4.3)
🗓 Publish year: 2024

📱Authors: Helena Klara Jambor ,Martin Bornhäuser
🏡 University: Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Germany

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👱 Arc2Face: A Foundation Model of Human Faces

TL; DR: a large dataset of high-resolution facial images, as well as a face generation model trained on its basis, which:

capable of creating photorealistic generations in a few seconds
provides complete similarity of generations to the target image compared to other existing models
built on the basis of Stable Diffusion and can be configured for any generation options, for example, different poses / facial expressions, etc.

Github: https://github.com/foivospar/Arc2Face

Project: https://arc2face.github.io

Demo: https://huggingface.co/spaces/FoivosPar/Arc2Face

Paper: https://arxiv.org/abs/2403.11641

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📁 Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review

📕 Journal: Mathematics (I.F.=2.4)
🗓 Publish year: 2023

🧑‍💻Authors: Minhyeok Lee
🏢University: Chung-Ang University, Republic of Korea

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⚡️ LLocalSearch: completely locally running meta search engine using LLM Agents

It is a completely local metasearch engine using LLM agents.

The user can ask a question, and the system will use a chain of AI agents to find the answer. The user can see the progress of the work and the final answer. No OpenAI or Google API keys are required.

Github

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🌉 Financial Datasets is an open source Python library that allows developers to create synthetic financial data sets using large language models (LLMs).

With this library, you can generate realistic financial data sets in 5 lines of code, based on SEC reports such as 10-Ks, 10-Qs and other financial reports.

Such datasets are useful for:
• LLM assessments
• LLM fine tuning
• testing of financial instruments
• and much more

The project is completely open source.

pip financial-datasets.

GitHub : https://github.com/virattt/financial-datasets

Example with code: https://colab.research.google.com/gist/virattt/f9b5a0ae82cc0caab57df5dedc2927c9/intro-financial-datasets.ipynb#scrollTo=K-b_1BPtJsS1

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🦖 DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video

👉 The Weizmann Institute has just released code for a new SOTA for object tracking.

Github: https://github.com/AssafSinger94/dino-tracker

Project: https://dino-tracker.github.io/

Paper: https://arxiv.org/abs/2403.14548

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🔥 ECoDepth: SOTA Diffusive Mono-Depth 🔥

🤨 New SIDE model using a diffusion backbone conditioned on ViT embeddings. It's the new SOTA in SIDE. Source Code released 💙

👉 Review: https://t.ly/s2pbB

👉 Paper: https://lnkd.in/eYt5yr_q

😏 Code: https://lnkd.in/eEcyPQcd

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