Observational Scaling Laws and the Predictability of Language Model Performance Published 2024-05-19 Download video MP4 360p Download video MP4 720p Recommendations 15:11 Large Language Models Must Be Taught to Know What They Don't Know 15:16 Beyond Model Collapse: Scaling Up with Synthesized Data Requires Reinforcement 29:29 Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Full Paper Review) 10:28 What does it mean for computers to understand language? | LM1 1:19:27 Stanford CS25: V3 I Retrieval Augmented Language Models 15:15 Attention as a Hypernetwork 13:13 Mixture-of-Agents Enhances Large Language Model Capabilities 14:35 Estimating the Hallucination Rate of Generative AI 19:14 Learn to Evaluate LLMs and RAG Approaches 14:10 All Learning Algorithms Explained in 14 Minutes 41:38 Sparse Training of Neural Networks Using AC/DC 18:20 How Far Can Transformers Reason? The Locality Barrier and Inductive Scratchpad 02:52 Large Language Models (LLMs) vs Natural Language Understanding (NLU) 1:15:31 Thomas Dietterich, "What’s Wrong with Large Language Models, and What We Should Be Building Instead" 09:39 [QA] Estimating the Hallucination Rate of Generative AI 25:20 Simple Introduction to Large Language Models (LLMs) 06:36 Machine Learning Fundamentals: Bias and Variance 27:14 But what is a GPT? Visual intro to transformers | Chapter 5, Deep Learning 22:22 Why Warmup the Learning Rate? Underlying Mechanisms and Improvements Similar videos 09:21 [Stanford, UofT] Observational Scaling Laws and the Predictability of Language Model Performance 10:23 [QA] Observational Scaling Laws and the Predictability of Language Model Performance 1:03:21 Bulk Reading New AI Paper Abstracts - May 25, 2024 57:11 Phil Brown — How IPUs are Advancing Machine Intelligence 1:47:12 Generative AI: Eric Xing 55:40 European leadership in HPC, big data and AI in weather and climate prediction 1:03:00 Tengyu Ma on Voyage AI - Weaviate Podcast #91! 57:08 Advancing Weather and Climate Prediction with Data Driven Methods: Will Chapman (NSF-NCAR) 1:35:10 Dominic Orchard: "Programming for the Planet" 1:03:47 Interpretable and Structure-Preserving Data-Driven Methods for Physical Simulations - Youngsoo Choi 59:12 Machine-learning-model-data-integration for a better understanding of the Earth System 1:30:53 SC20 Keynote + Q&A: Climate Science in the Age of Exascale with Professor Bjorn Stevens 1:08:18 TIES 2022: Auroop R. Ganguly - Plenary Speaker 55:53 Hyde Lecture - Karen M'Closkey 1:09:22 Big Data and Machine Learning for Climate Science 2:11:31 【IELTS LISTEN】雅思听力练习Train your ears-Noun8 56:02 Machine Learning in Extreme Weather Forecasting with William Drew Collins 1:04:58 Kostas Skenderis: Holographic Cosmology -- Part 2 3:21:26 NAEHS Council Meeting - Open Session - September 12, 2023 (Day 1 - Part 2) More results