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NeRFs Explained: Goodbye Photogrammetry?

PyImageSearch

Home Table of Contents NeRFs Explained: Goodbye Photogrammetry? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Steps Next Steps Citation Information NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work?

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Eight Graphs that Explain Software Engineering Salaries in 2023

Flipboard

Hired’s analysis included data from 68,500 job candidates and 494,000 interview requests collected from the site between January 2021 through December 2022, supplemented by a survey of 1,300 software engineers. Tech Salaries Jump, But Don’t Keep Up With Inflation According to Dice’s numbers , tech salaries grew 2.3

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2021 Data/AI Salary Survey

O'Reilly Media

In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. It’s easy to hypothesize about this difference, but we’re at a loss to explain it. How do we explain this? In 2021, and without being anywhere near as repulsive, we’d say, “There’s a great future in the cloud.

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ML and NLP Research Highlights of 2021

Sebastian Ruder

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP).   2021 saw the continuation of the development of ever larger pre-trained models. 2021 saw the development of alternative model architectures that are viable alternatives to the transformer. style loss.

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ACL 2021 Highlights

Sebastian Ruder

ACL 2021 took place virtually from 1–6 August 2021. Understanding models Gaining a better understanding of the behaviour of current models was another theme of the conference, with three out of the six outstanding papers falling in this area: Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

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Explainability in AI and Machine Learning Systems: An Overview

Heartbeat

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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MQM shows the power of a gold-standard evaluation

Ehud Reiter

So it is exciting that I do see the emergence of something which feels like a true gold standard evaluation in the above sense, which is the MQM evaluation technique in Machine Translation ( Freitag et al 2021 ). Errors are categorised and assigned a severity level.

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