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As a trainer and facilitator who works with nonprofit organizations and staffers, you have to be obsessed with learningtheory to design and deliver effective instruction, have productive meetings, or embark on your own self-directed learning path.
This increase in accuracy is important to make AI applications good enough for production , but there has been an explosion in the size of these models. It is safe to say that the accuracy hasn’t been linearly increasing with the size of the model. They define it as “buying” stronger results by just throwing more compute at the model.
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.
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.
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 scaled key SLT-derived techniques to models with billions of parameters, eliminating our main concerns around tractability.
Some relevant criteria for evaluating a specification language include: How expressive is the language? What is the right way to quantify the differences and similarities between different goal specifications in a given specification language? This is called a behavioural model. Are there things it cannot express?
Smith, Scott W. Linderman, David Sussillo Contact : jsmith14@stanford.edu Links: Paper | Website Keywords : recurrent neural networks, switching linear dynamical systems, interpretability, fixed points Compositional Transformers for Scene Generation Authors : Drew A.
Manning, Jure Leskovec Contact : xikunz2@cs.stanford.edu Award nominations: Spotlight Links: Paper | Website Keywords : knowledge graph, question answering, languagemodel, commonsense reasoning, graph neural networks, biomedical qa Fast Model Editing at Scale Authors : Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D.
This guide provides an opinionated overview of recent work and open problems across areas like adversarial testing, model transparency, and theoretical approaches to AI alignment. Robust unlearning: One idea for reducing AI risks is to remove models' knowledge of potentially dangerous topics, such as cybersecurity exploits or virology.
Goodhart's Law in Reinforcement Learning As you probably know, "Goodhart's Law" is an informal principle which says that "if a proxy is used as a target, it will cease to be a good proxy". However, does this guarantee that we get a low regret relative to the underlying true reward function when that reward model is optimised?
Platt , Fernando Pereira , Dale Schuurmans Keynote Speakers The Data-Centric Era: How ML is Becoming an Experimental Science Isabelle Guyon The Forward-Forward Algorithm for Training Deep Neural Networks Geoffrey Hinton Outstanding Paper Award Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Chitwan Saharia , William Chan (..)
One way is to see if it helps us prove things about models that we care about knowing. In this episode, I speak with Jason Gross about his agenda to benchmark interpretability in this way, and his exploration of the intersection of proofs and modern machine learning. Whats the theme? Jason Gross (00:01:02): Okay.
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