Remove 2019 Remove Convolutional Neural Networks Remove Natural Language Processing
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AI News Weekly - Issue #343: Summer Fiction Reads about AI - Jul 27th 2023

AI Weekly

techcrunch.com The Essential Artificial Intelligence Glossary for Marketers (90+ Terms) BERT - Bidirectional Encoder Representations from Transformers (BERT) is Google’s deep learning model designed explicitly for natural language processing tasks like answering questions, analyzing sentiment, and translation. Get it today!]

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OMG-Seg: 10 Segmentation Tasks in 1 Framework (2024)

Viso.ai

Prompt-based Segmentation combines the power of natural language processing (NLP) and computer vision to create an image segmentation model. OMG-Seg explores co-training on various datasets, including COCO panoptic, COCO-SAM, and Youtube-VIS-2019. One such development is prompt-based segmentation.

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Graph Convolutional Networks for NLP Using Comet

Heartbeat

GCNs have been successfully applied to many domains, including computer vision and social network analysis. In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition. Richong, Z., & Nie, JY.

NLP 59
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Foundation models: a guide

Snorkel AI

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al.

BERT 83
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Dude, Where’s My Neural Net? An Informal and Slightly Personal History

Lexalytics

A paper that exemplifies the Classifier Cage Match era is LeCun et al [ 109 ], which pits support vector machines (SVMs), k-nearest neighbor (KNN) classifiers, and convolution neural networks (CNNs) against each other to recognize images from the NORB database. 90,575 trainable parameters, placing it in the small-feature regime.

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ML and NLP Research Highlights of 2020

Sebastian Ruder

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). 2019 ) and work that focuses on making them smaller has gained momentum: Recent approaches rely on pruning ( Sajjad et al.,

NLP 52
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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Advances in neural information processing systems 32 (2019). He is broadly interested in Deep Learning and Natural Language Processing. He completed his master’s degree in Data Science at Columbia University in the City of New York in December 2019. The Illustrated Transformer.” He obtained his Ph.D.