Výsledky vyhledávání - "Kinnison, Jeffery"

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LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report

Autor: Zhao, Justin, Wang, Timothy, Abid, Wael, Angus, Geoffrey, Garg, Arnav, Kinnison, Jeffery, Sherstinsky, Alex, Molino, Piero, Addair, Travis, Rishi, Devvret

Low Rank Adaptation (LoRA) has emerged as one of the most widely adopted methods for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs). LoRA reduces the number of trainable parameters and memory usage while achieving comparable p

Externí odkaz: http://arxiv.org/abs/2405.00732

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HomOpt: A Homotopy-Based Hyperparameter Optimization Method

Autor: Abraham, Sophia J., Maduranga, Kehelwala D. G., Kinnison, Jeffery, Carmichael, Zachariah, Hauenstein, Jonathan D., Scheirer, Walter J.

Machine learning has achieved remarkable success over the past couple of decades, often attributed to a combination of algorithmic innovations and the availability of high-quality data available at scale. However, a third critical component is the fi

Externí odkaz: http://arxiv.org/abs/2308.03317

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Toward an Automated HPC Pipeline for Processing Large Scale Electron Microscopy Data

Autor: Vescovi, Rafael, Li, Hanyu, Kinnison, Jeffery, Keceli, Murat, Salim, Misha, Kasthuri, Narayanan, Uram, Thomas D., Ferrier, Nicola

We present a fully modular and scalable software pipeline for processing electron microscope (EM) images of brain slices into 3D visualization of individual neurons and demonstrate an end-to-end segmentation of a large EM volume using a supercomputer

Externí odkaz: http://arxiv.org/abs/2011.03204

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Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation

Autor: Murray, Kenton, Kinnison, Jeffery, Nguyen, Toan Q., Scheirer, Walter, Chiang, David

Neural sequence-to-sequence models, particularly the Transformer, are the state of the art in machine translation. Yet these neural networks are very sensitive to architecture and hyperparameter settings. Optimizing these settings by grid or random s

Externí odkaz: http://arxiv.org/abs/1910.06717

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Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples

Autor: Kinnison, Jeffery, Trokielewicz, Mateusz, Carballo, Camila, Czajka, Adam, Scheirer, Walter

Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation. In many cases, iris biometrics acquisition equipment takes a number of images in sequence and combines the segmentation and matching results for

Externí odkaz: http://arxiv.org/abs/1901.01575

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A Neurobiological Evaluation Metric for Neural Network Model Search

Autor: Blanchard, Nathaniel, Kinnison, Jeffery, RichardWebster, Brandon, Bashivan, Pouya, Scheirer, Walter J.

Neuroscience theory posits that the brain's visual system coarsely identifies broad object categories via neural activation patterns, with similar objects producing similar neural responses. Artificial neural networks also have internal activation be

Externí odkaz: http://arxiv.org/abs/1805.10726

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