Princiapal Component Analysis (PCA) clearly explained and implemented from scratch in Python:
RESEARCH
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LIGER: Novel Approach Combining Model Embeddings With Weak Supervision
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Mayee Chen from Stanford shared fascinating insights on LIGER, a novel approach that combines model embeddings with weak supervision. LIGER's relevance and impact continue to resonate https://
youtube.com/watch?v=SfN8oe
G-M6E
… #stanford #ligermodel #machinelearning #NLP #AI -
Statistical Thermodynamics of Generative Diffusion Models
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The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability Ambrogioni et al.: https://
arxiv.org/abs/2310.17467 #ArtificialIntelligence #DeepLearning #MachineLearning -
AI Content Flood Challenges Academic Peer Review System
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Peer review isn’t built to handle the flood of AI content, especially as not all of it will be obvious, and not all will be malicious (lots of scholars pay editors to help make their writing better, now they will use chat). The system, already straining, won’t be able to adjust.
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Humanities Courses Essential for AI Skill Development
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Also take some humanities courses, both for their own sake and if you want to get good at AI.
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Industrial researchers discuss repository collaboration
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Repo.
No, we are industrial researchers -
Georgia Tech Faculty Honored on CIOS 2023 Fall Roll
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I am thrilled to be among faculty members at Georgia Tech who made the CIOS 2023 Fall Honor Roll.
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MM1: Methods and Insights from Multimodal LLM Pre-training
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MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training McKinzie et al.: https://
arxiv.org/abs/2403.09611 #ArtificialIntelligence #DeepLearning #MachineLearning -
Learning Goals Enhance Team Performance and Knowledge Transfer
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To make teams better, "set learning-based goals for knowledge development, knowledge re-use (knowledge that could be used for future projects), and knowledge transfer (knowledge that could be useful to other teams)." as well as performance goals. Paper: http://
eprints.lse.ac.uk/101239/1/Getti
ng_to_know_you._Motivating_cross_understanding_for_team_and_individual_performance.pdf
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Moore’s Law for Language Models: Performance and Speed Trade-offs
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Trying to think about what a Moore's Law for Language Models would look like. the problem i'm running into is that there are two separate axes to improve upon: model ability for a given model size (perplexity) and inference speed (tokens per second) Anyway, we can all agree