Choose Your Weapon: Survival Strategies for Depressed AI Academics This is a very timely paper that discusses how researchers in academia can keep up with the current pace of AI research. The authors present many ideas, some of them being: – Giving up on doing things that are
@jeande_d
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Understanding Diffusion Models: A Unified Perspective Tutorial
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Understanding Diffusion Models: A Unified Perspective Diffusion models are the engine behind novel image generative systems. This tutorial provides an intuitive and comprehensive understanding of diffusion models. Paper: https://
arxiv.org/abs/2208.11970
Blog: https://
calvinyluo.com/2022/08/26/dif
fusion-tutorial.html
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Generative AI Value: Faster to Refine Than Start from Scratch
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The value of generative AI is here: it's much easier to generate things & work backward putting the final touch on the task than to start from scratch in the 1st place. For real, generative tools aren't everything, but if ChatGPT or Copilot can do a task well & fast, why bother?
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ChatGPT: Applications, Advantages, Limitations and Future in NLP
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Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing
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MIT Intro to Deep Learning 2023 Lectures Now Available Online
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MIT Intro to Deep Learning – 2023 Lectures are Live MIT Intro to Deep Learning is one of few concise deep learning courses on the web. The course quickly takes you to the foundations of deep learning, neural net architectures, and applications of DL. http://
introtodeeplearning.com -
MIT Intro to Deep Learning 2023 Lecture Videos Available on YouTube
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MIT Intro to Deep Learning 2023 The lecture videos for 2023 iterations and previous iterations are all freely available on YT: https://
youtube.com/playlist?list=
PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
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ChatGPT and GPT-4: Research Survey and Future of Language Models
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Summary of ChatGPT/GPT-4 Research & Perspective Towards the Future of Large Language Models A comprehensive survey of ChatGPT, GPT-4, and their applications in various domains. Touches topics like large-scale pre-training, instruction fine-tuning, RLHF. https://
arxiv.org/abs/2304.01852 -
Stanford CS330: Deep Multi-Task and Meta-Learning Course 2022
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Stanford CS330: Deep Multi-Task & Meta-Learning – 2022 This course covers topics related to multi-task and meta-learning such as self-supervised pre-training, transfer learning, lifelong learning, etc. New lectures just dropped. https://
youtube.com/playlist?list=
PLoROMvodv4rNjRoawgt72BBNwL2V7doGI
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New Transformer-based Image Segmentation Model with Zero-shot Capabilities
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A new image segmentation model that can segment almost anything via prompt.
— Jean de Dieu Nyandwi (@Jeande_d) 5 avril 2023
– Zero-shot generalization
– Based on Transformers
– Code and dataset released
– 632M + 4M params
– Can be prompted via background points, mask, and bounding box. https://t.co/etBlCd9yCjA new image segmentation model that can segment almost anything via prompt. – Zero-shot generalization
– Based on Transformers
– Code and dataset released
– 632M + 4M params
– Can be prompted via background points, mask, and bounding box. -
Comprehensive Survey of Large Language Models: Techniques and Resources
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A Survey of Large Language Models/LLMs LLMs are one of the hot things nowadays. This review paper provides a comprehensive overview of recent advances in LLMs(background & mainstream techniques). Also covers resources for developing/building with LLMs. https://
arxiv.org/abs/2303.18223