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Reinforcement Learning for Physics: ODEs and Hyperparameter Tuning | by Robert Etter | Oct, 2024

Data ScienceOctober 17, 202456Views2Likes0Comments
Working with ODEs Physical systems can typically be modeled through differential equations, or equations including derivatives. Forces, hence Newton’s Laws, can be expressed as derivatives, as can Maxwell’s Equations, so differential equations can describe most physics problems. A differential equation describes how a system changes based on the system’s current state, in effect defining state…

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The Complete Guide to Workflows in NetSuite

UncategorisedOctober 14, 202442Views0Likes0Comments
Oracle NetSuite provides a robust solution for automating many business processes. One of the most powerful features is its built-in Workflow functionality, which allows users to automate a variety of tasks across different business functions. The most common use cases are for approvals, but workflows can also help with automation around invoicing, record-keeping, billing, and…

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Researchers at Stanford University Propose ExPLoRA: A Highly Effective AI Technique to Improve Transfer Learning of Pre-Trained Vision Transformers (ViTs) Under Domain Shifts

AI NewsOctober 12, 202464Views0Likes0Comments
Parameter-efficient fine-tuning (PEFT) methods, like low-rank adaptation (LoRA), allow large pre-trained foundation models to be adapted to downstream tasks using a small percentage (0.1%-10%) of the original trainable weights. A less explored area of PEFT is extending the pre-training phase without supervised labels—specifically, adapting foundation models to new domains using efficient self-supervised pre-training. While traditional…

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