Artistic portraits are everywhere in their everyday lives and in the creative sectors that inform the arts, social media avatars, movies, entertainment advertising, etc....
Differential privacy (DP) is used in machine learning to preserve the confidentiality of the information that forms the dataset. The most used algorithm to...
Several sequential decision-making challenges, like robotics, gaming, nuclear physics, balloon navigation, etc., have been successfully addressed using deep reinforcement learning. However, despite its potential,...
Microsoft's Natural Language Computing (NLC) team recently introduced their latest vision-language AI model, BEiT-3, a Bidirectional Encoder representation from Image Transformers with 1.9 billion...
Technological advancements strive to develop AI models that communicate more efficiently, accurately, and safely. Large language models (LLMs) have achieved outstanding success in recent...
The Generative Adversarial Network (GAN) is a deep neural network architecture that may learn from training data and produce new data that shares the...
Introduction
In computer vision and machine learning operations, data labeling is an essential part of the overall workflow. For reference, data labeling is the process...
Deep learning's success is due to its capacity to tackle some enormous non-convex optimization problems with relative simplicity. Even though non-convex optimization is NP-hard,...
Using only an off-the-shelf transformer trained on 680,000 hours of weakly-supervised, multi-lingual audio data, OpenAI’s Whisper can approach human-level robustness and accuracy in ASR,...
The constant improvement in display technologies and emerging new media representation types such as 360-degree videos enabled videos to have better visual characteristics such...
As Machine Learning becomes increasingly prevalent in software, a new subfield known as MLOps (short for ML Operations) has evolved to organize the "collection...
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