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Machine Learning Beginner Info/Resources
I also want to share some resources.
For Pytorch,
For TPU,
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Retentive Network: A Successor to Transformer for Large Language Models
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https://github.com/Jamie-Stirling/RetNet non-official implementation
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Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing
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I know we are moving away from Reddit. However, if I don't link, I feel like we may miss out good threads on r/machinelearning. Moreover, the authors don't only post arxiv links, they post other sutff such as Summary, Key points, ... (e.g this).
So can I at least put them in the posts instead of posting in a comment?
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Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models
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Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing
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I will follow then.
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PaLI-3 Vision Language Models: Smaller, Faster, Stronger
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indeed it would be great if the authors did so. I personally found some non-official implementations:
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PaLI-3 Vision Language Models: Smaller, Faster, Stronger
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Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training
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VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
The idea is similar to BLIP-2. Both papers use learnable tokens as queries for a transformer decoder. This decoder query from vision space base on the trainable queries and prompt.
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Think before you speak: Training Language Models With Pause Tokens
IIRC DeTr generate a sequence to predict boxes of objects. I think this paradigm can be applied to such models. "Think before you locate" could be a new path to explore.
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GitHub - mazzzystar/Queryable: Run CLIP on iPhone to Search Photos.
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Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing
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NeurIPS 2023 Machine Unlearning Challenge
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MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
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Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback
