Evaluating large language models trained on code.

English based datasets are commonly available from Kaggle, GitHub, or recently published papers. Although benchmark tests with English datasets are sufficient to show off the performances of new models and methods, still a researcher need to train and validate the models on Korean based datasets to produce a technology or product, suitable for Korean processing. This paper introduces 15 popular Korean based NLP datasets with summarized details such as volume, license, repositories, and other research results inspired by the datasets. Also, I provide high-resolution instructions with sample or statistics of datasets. The main characteristics of datasets are presented on a single table to provide a rapid summarization of datasets for researchers.

"The Beatles: Get Back" is eight hours of carefully curated audio and footage from The Beatles in the studio and performing a rooftop concert in London in 1969. Jackson had to dig through 60 hours of vintage film footage and around 150 hours of audio recordings in order to put together his three-part documentary. Once he decided which footage and audio to include, then he had to take the next difficult step: cleaning up and restoring them both to give fans a look at The Beatles like they had never seen them before. In order to clean up the audio for "Get Back," Jackson employed algorithm technology to teach computers what different instruments and voices sounded like so they could isolate each track: Once each track was isolated, sound mixers could then adjust volume levels individually to help with sound quality and clarity. The isolated tracks also make it much easier to remove noise from the audio tracks, like background sounds or the electronic hum of older recording equipment.

AI/Machine Learning market Research Report covers a comprehensive market analysis by product types, end-user applications, sales channels, …

Mohanan told IANS. Integrating Artificial Intelligence (AL) and Machine Learning (ML) to detect heart ailments well in advance was one of the key …

Driven by AI and machine learning, the solution will assist the company in gaining an end-to-end view of each customer and understanding their …

He has a strong interest in Deep Learning and writing blogs on data science and machine learning.

Digital marketing is changing the entire marketing landscape altogether. It's, therefore, essential for businesses to keep abreast with the latest trends and abide by an approach that'll help them take the lead. As technology continues to evolve, it has become essential for marketers to improvise and leverage the full potential of current trends to stay ahead of the curve. For example, voice search optimization and artificial intelligence are now taking the lead when it comes to building a brand online. Artificial intelligence is no longer the cool tech from Hollywood movies. It has slowly started becoming a part of our daily lives.

We present a deep learning-based method for propagating spatially-varying visual material attributes (e.g. texture maps or image stylizations) to larger samples of the same or similar materials. For training, we leverage images of the material taken under multiple illuminations and a dedicated data augmentation policy, making the transfer robust to novel illumination conditions and affine deformations. Our model relies on a supervised image-to-image translation framework and is agnostic to the transferred domain; we showcase a semantic segmentation, a normal map, and a stylization. Following an image analogies approach, the method only requires the training data to contain the same visual structures as the input guidance. Our approach works at interactive rates, making it suitable for material edit applications. We thoroughly evaluate our learning methodology in a controlled setup providing quantitative measures of performance. Last, we demonstrate that training the model on a single material is enough to generalize to materials of the same type without the need for massive datasets.

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