Digital Autofocus Using Scene Content

Shazeer, Dov J.; Harris, Marjorie S.

doi:10.1117/12.946575

Cited by 3 publications

(3 citation statements)

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“…and Multi-Head Attention (Vaswani et al, 2017). This model use the swish activation function (Ramachandran et al, 2017) with GLU (Shazeer, 2020) for the MLP, also commonly referred to as swiglu. For normalization, we use RMSNorm (Zhang & Sennrich, 2019) since it's computationally more efficient than LayerNorm (Ba et al, 2016).…”

Section: Model Architecturementioning

confidence: 99%

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Endocrine Surgery

Mishra¹,

Agarwal²,

Parameswaran³

et al. 2021

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Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile "all around" code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use.

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Section: Model Architecturementioning

confidence: 99%

“…The 20B code model is trained with learned absolute position embeddings. We use Multi-Query Attention (Shazeer, 2019) during training for efficient downstream inference.…”

Section: Bmentioning

confidence: 99%

Endocrine Surgery

Mishra¹,

Agarwal²,

Parameswaran³

et al. 2021

View full text Add to dashboard Cite

show abstract

“…(Shazzer and Harris 1985) has developed monolithic image processing chips to implement their autofocus technique used in FLIR (Forward Looking Infrared) imaging.…”

Section: Image Focusing and Autofocusmentioning

confidence: 99%

<title>Optical range finding from image focus</title>

Weckler¹,

Kranzler²

1990

Sensing and Reconstruction of Three-Dimensional Objects and Scenes

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Comparison of phase‐contrast and fluorescence digital autofocus for scanning microscopy

Price

Gough

1994

Cytometry

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Reliable autofocus is required to obtain accurate measurements of fluorescent stained cellular components from a system capable of scanning multiple microscope fields. Autofocus could be performed directly with fluorescence images, but due to photobleaching and destructive fluorescence by-products, it is best to minimize fluorescence exposure for photosensitive specimens and live cells. This exposure problem could be completely avoided by using phase-contrast microscopy, implemented through the same optics as fluorescence microscopy. The purpose of this work was to evaluate functions for both phase-contrast and fluorescence autofocus and determine the suitability of phase-contrast autofocus for fluorescence microscopy. Eleven autofocus functions were independently evaluated for fluorescence and phase-contrast microscopy. The most suitable functions were then chosen from these and phase-contrast and fluorescence autofocus were compared on scans each comprising more than 1,000 microscope fields. Autofocus standard deviation (S.D.) of better than 100 nm was achieved for both phase contrast and fluorescence. There was a measurable difference between the best focus positions in the two modes, but the difference was constant enough to be measured and corrected, suggesting the possibility of using phase contrast to predict best focus in fluorescence microscopy. The scanning experiments also showed that autofocus can be performed at least as fast as 0.25 s/field without loss of precision.

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Digital Autofocus Using Scene Content

Cited by 3 publications

References 0 publications

Endocrine Surgery

Endocrine Surgery

<title>Optical range finding from image focus</title>

Comparison of phase‐contrast and fluorescence digital autofocus for scanning microscopy

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