Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation) https://
bit.ly/46zvJNV
#AI #MachineLearning #DeepLearning #LLMs #DataScience
MACHINE LEARNING
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Practical Tips for Finetuning LLMs Using LoRA
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LoRAs Learn Compositional Patterns Beyond Aesthetic Visual Information
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A neat underexplored property of LoRAs is their ability to learn compositional patterns and other kinds of "non-aesthetic" visual information.
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2023 Key AI Papers, Videos, Articles, and Code Resources
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Stay updated in AI with my 2023 list of key industry papers, videos, articles, and codes—a resource for breakthroughs in the field. Showcasing diverse generative AI advancements, from VALL-E's lifelike voice mimicry to InstructPix2Pix's text-driven image editing and much more.
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Evaluating Multi-Modal Retrieval-Augmented Generation Systems
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Evaluating Multi-Modal Retrieval-Augmented Generation https://
bit.ly/49Xe6dB
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Wang et al. Generate Molecular Conformer Fields With AI
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Generating Molecular Conformer Fields Wang et al.: https://
arxiv.org/abs/2311.17932 #ArtificialIntelligence #DeepLearning #MachineLearning -
Initializing Deep Learning Models with Larger Models
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Initializing Models with Larger Ones Xu et al.: https://
arxiv.org/abs/2311.18823 #ArtificialIntelligence #DeepLearning #MachineLearning -
Training ControlNet Models with Upscaled Video Game Screenshots
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Could you use all these upscaled video game screenshots to train a controlnet?
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Gradient-Based Optimization Superior to Gradient-Free Methods
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No. That's just false.
Whenever you can use it, gradient-based optimization is incomparably more efficient than gradient-free, regardless of hardware.
There are situations where you have no choice but use gradient free, you should make that as rare as possible.
When you do, you -
Gradient-Based Learning Enables Distributed Representation Emergence
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Gradient-based learning causes distributed representations to emerge.
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Convex Loss Limitations in Machine Learning Theory
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The people mocking "no proof" didn't realize that it was their insistence to have a particular type of theory that was holding them back. They would only work on "trivial" models for which the loss was convex, so they could use fancy semi-definite programming methods and prove