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A Comprehensive Guide to Social LearningTheory GyrusAim LMS GyrusAim LMS - Social learningtheory’s fundamental tenet is that people learn by watching, copying, and behaving like others in social situations. What Is Social LearningTheory? They observe the behavior and its specific results.
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This book is filled with great tips on designing engaging learning experiences that help your participants connect, inspire, and engage. The model balances content, learning design, and participants. The ideas, tips, and tricks are grounded in adult learningtheory, but the book is very practical.
Designing and delivering a training to a nonprofit audience is not about extreme content delivery or putting together a PowerPoint and answering questions. If you want to get results, you need to think about instructional design and learningtheory. And, there is no shortage of learningtheories and research.
If you’re registered for ICLR 2023, we hope you’ll visit the Google booth to learn more about the exciting work we’re doing across topics spanning representation and reinforcement learning, theory and optimization, social impact, safety and privacy, and applications from generative AI to speech and robotics. Mukund Varma T.
Kochenderfer Contact : philhc@stanford.edu Links: Paper Keywords : deep learning or neural networks, sparsity and feature selection, variational inference, (application) natural language and text processing Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss Authors : Jeff Z.
Published on February 20, 2025 11:54 PM GMT TLDR: We made substantial progress in 2024: We published a series of papers that verify key predictions of Singular LearningTheory (SLT) [ 1 , 2 , 3 , 4 , 5 , 6 ]. We introduced refined variants of the LLC to study how individual attention heads develop during training.
If this research agenda gets a bit more attention, then I think it would be entirely realistic to develop something like a mathematical theory of outer alignment on a timescale of maybe 1-3 years (and this is not accounting for the possibility of getting strong AI proof assistants within that time span).
We attentively respond to requests and purposefully use different modes of feedback to inform program design from our comment board, social media outlets, conversations and observations both inside and outside the museum, creative feedback at events such as our Show and Tell Booth and online visitor surveys specific to our programs.
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Reverse engineering tiny language models with modified architecture that makes them more interpretable , such as attention-only language models ( Olsson et al. Developing a theory of the inductive biases of neural network training on a specific model of computation like boolean circuits (building on Malach and Shalev-Shwartz , Barak et al.
How can we use the takeaways from this frame to say where we should focus our attention? So if we want to drill down into what parts need the most attention and the most understanding, to understand them we should be looking at how do the non-linearities perform their function. You can get shorter proofs that have better accuracy.
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