NVIDIA

OCTOBER 11, 2024

His neural network, AlexNet, trained on a million images, crushed the competition, beating handcrafted software written by vision experts. Researchers at Google, Stanford and New York University began using NVIDIA GPUs to accelerate AI development, achieving performance that previously required supercomputers.

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Marktechpost

SEPTEMBER 28, 2024

Although ML-based PDE solvers, such as physics-informed neural networks (PINNs), have shown potential, they often fail regarding speed, accuracy, and stability. The review thoroughly highlights the need to evaluate baselines in ML-for-PDE applications, noting the predominance of neural networks in the selected articles.

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Marktechpost

NOVEMBER 18, 2023

In Australia’s Great Barrier Reef in 2016, bleaching killed 29–50% of the coral. In most classification jobs, manual feature extractors are replaced by deep neural network (DNN) models. Coral reef bleaching is linked to several environmental and economic problems.

Machine Learning 111

Machine Learning Neural Network Computer Vision Deep Learning 111

Unite.AI

MARCH 5, 2024

Review of Previous YOLO Versions The YOLO (You Only Look Once) family of models has been at the forefront of fast object detection since the original version was published in 2016. The neural network needs enough depth and width to capture relevant features from the input images.

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Viso.ai

DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

Convolutional Neural Networks 98

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Towards AI

AUGUST 8, 2024

A subset of machine learning utilizing multilayered neural networks, otherwise known as deep neural networks. What is PyTorch PyTorch is an open-source deep learning framework developed by Facebook and released in 2016. Photo by Marius Masalar on Unsplash Deep learning. So, let’s take a look at PyTorch.

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Deep Learning Neural Network Machine Learning Data Ingestion 105

Viso.ai

DECEMBER 4, 2023

Artificial Neural Networks (ANNs) have been demonstrated to be state-of-the-art in many cases of supervised learning, but programming an ANN manually can be a challenging task. These frameworks provide neural network units, cost functions, and optimizers to assemble and train neural network models.

Neural Network 98

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PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. 2015 ; Redmon and Farhad, 2016 ), and others.

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Hal Daumé III

JULY 12, 2016

A Latent Variable Recurrent Neural Network for Discourse Relation Language Models by Ji, Haffari and Eisenstein. And they actually do a human evaluation, even for a short paper!

Neural Network 40

Neural Network NLP ML 40

Hal Daumé III

AUGUST 14, 2016

P16-1231 : Daniel Andor; Chris Alberti; David Weiss; Aliaksei Severyn; Alessandro Presta; Kuzman Ganchev; Slav Petrov; Michael Collins Globally Normalized Transition-Based Neural Networks [EDIT 14 Aug 2:40p: I misunderstood from the talk and therefore the following is basically inaccurate. Why do I like this?

Neural Network 40

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Marktechpost

JULY 17, 2023

It is especially appropriate for novices because it enables speedy neural network model construction while offering backend help. OpenVINO A comprehensive computer vision tool, OpenVINO (Open Visual Inference and Neural Network Optimization), helps create software that simulates human vision.

Computer Vision 98

Computer Vision Neural Network Machine Learning Deep Learning 98

Unite.AI

DECEMBER 11, 2024

Tools and Technologies Behind Gen AI in Art Generative Adversarial Networks (GANs) are a key technology behind AI art. GANs use two neural networks working together. One network, the “generator,” creates images, while the other, the “discriminator,” checks if the images look real.

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Marktechpost

SEPTEMBER 1, 2023

This can be accomplished in several ways, such as by employing neural networks to create entirely unique music or utilizing machine learning algorithms to assess existing music and produce new compositions in a similar style. AIVA, built in 2016, is another outstanding AI music creator consistently attracting notice.

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Viso.ai

OCTOBER 15, 2024

However, in recent years, human pose estimation accuracy achieved great breakthroughs with Convolutional Neural Networks (CNNs). The method won the COCO 2016 Keypoints Challenge and is popular for quality and robustness in multi-person settings. Pose Estimation is still a pretty new computer vision technology.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Algorithm 98

Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. Most neural network models begin by tokenising the text into words, and embedding the words into vectors. 2016) introduce an attention mechanism that takes a single matrix and outputs a single vector.

Deep Learning 52

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Unite.AI

OCTOBER 27, 2023

For example, multimodal generative models of neural networks can produce such images, literary and scientific texts that it is not always possible to distinguish whether they are created by a human or an artificial intelligence system.

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ML Review

JUNE 4, 2018

The first paper, to the best of our knowledge, to apply neural networks to the image captioning problem was Kiros et al. These new approaches generally; Feed the image into a Convolutional Neural Network (CNN) for encoding, and run this encoding into a decoder Recurrent Neural Network (RNN) to generate an output sentence.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Viso.ai

MARCH 12, 2024

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). proposed a deep convolutional neural network architecture codenamed Inception. Faster R-CNN as a single, unified network for object detection.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

This leads to the same size and architecture as the original neural network. He joined Amazon in 2016 as an Applied Scientist within SCOT organization and then later AWS AI Labs in 2018 working on Amazon Kendra. Typically, these adapters are then merged with the original model weights for serving.

Natural Language Processing 109

Natural Language Processing NLP Large Language Models LLM 109

Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Mlearning.ai

JANUARY 27, 2023

SVO-SLAM : SVO uses a semi-drect paradigm to estimate the 6-DOF motion of a camera system from both pixel intensities Neural Radiance Field (NeRF) A neural radiance field (NeRF) is a fully-connected neural network that can generate novel views of complex 3D scenes, based on a partial set of 2D images.

Computer Vision 98

Computer Vision Deep Learning Python Neural Network 98

Viso.ai

MAY 7, 2024

However, GoogLeNet demonstrated by using the inception module that depth and width in a neural network could be increased without exploding computations. GooLeNet – source Historical Context The concept of Convolutional Neural Networks ( CNNs ) isn’t new. We will investigate the inception module in depth.

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Viso.ai

FEBRUARY 27, 2024

Farhadi, signifying a step forward in the real-time object detection space, outperforming its predecessor – the Region-based Convolutional Neural Network (R-CNN). It is a single-pass algorithm having only one neural network to predict bounding boxes and class probabilities using a full image as input.

Neural Network 52

Neural Network Computer Vision Convolutional Neural Networks Python 52

Viso.ai

OCTOBER 7, 2024

billion tons of municipal solid waste was generated globally in 2016 with experts predicting a steep rise to 3.40 Computer vision mainly uses neural networks under the hood. Object Detection : Computer vision algorithms, such as convolutional neural networks (CNNs), analyze the images to identify and classify waste types (i.e.,

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Robotics 52

ODSC - Open Data Science

OCTOBER 4, 2024

His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, and deep reinforcement learning algorithms. His work has applications in autonomous robots and vehicles, among other decision-making domains.

Data Science 52

Data Science Machine Learning Neural Network Software Engineer 52

Explosion

JANUARY 12, 2018

We founded Explosion in October 2016, so this was our first full calendar year in operation. In August 2016, Ines wrote a post on how AI developers could benefit from better tooling and more careful attention to interaction design. The DarkNet code base is a great way to learn about implementing neural networks from scratch.

NLP 52

NLP Neural Network Machine Learning Python 52

Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. What are some examples of Foundation Models?

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Unite.AI

JULY 24, 2023

The YOLO concept was first introduced in 2016 by Joseph Redmon, and it was the talk of the town almost instantly because it was much quicker, and much more accurate than the existing object detection algorithms. What is YOLO? YOLO or “ You Only Look Once ” is a family of real time object detection models.

Algorithm 130

Algorithm Computer Vision Explainability Neural Network 130

deepsense.ai

APRIL 16, 2023

Pipeline Parallelism Since deep neural networks typically have multiple layers stacked on top of each other, the naive approach to model parallelism involves dividing a large model into smaller parts, with a few consecutive layers grouped together and assigned to a separate device, with the output of one stage serving as the input to the next stage.

Large Language Models 52

Large Language Models Neural Network Deep Learning Explainability 52

Viso.ai

JULY 17, 2024

An image can be represented by the relationships between the activations of features detected by a convolutional neural network (CNN). Fast Style Transfer (2016) While the previous model produced decent results, it was computationally expensive and slow. A Gram matrix captures the style information of an image in numerical form.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Pickl AI

JULY 24, 2024

Techniques such as neural networks, particularly deep learning, have enabled significant breakthroughs in image and speech recognition, natural language processing, and autonomous systems. ” 1986: A resurgence in neural networks occurs with the introduction of the backpropagation algorithm, revitalising AI research.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Natural Language Processing Neural Network 52

Probably Approximately a Scientific Blog

MAY 25, 2016

We started by improving path representation, using a recurrent neural network. I can't possibly explain the technical details without writing a long background post about neural networks first, so I'll skip most of the technical details. Since these two approaches are complementary, we combined them!

Neural Network 52

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AI Impacts

FEBRUARY 1, 2023

Preprint posted online June 21, 2016. More generally, large language models trained on internet text can extensively recount information about deep learning, neural networks, and the real-world contexts in which those networks are typically deployed; and can be fine-tuned to recount details about themselves specifically [OpenAI, 2022a].

AI 130

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Dlabs.ai

AUGUST 10, 2020

One of the first widely discussed chatbots was the one deployed by SkyScanner in 2016. The solution is based on a Transformer-type neural network, used in the BERT model as well, that has recently triumphed in the field of machine learning and natural language understanding.

Natural Language Processing 52

Natural Language Processing Chatbots Neural Network Machine Learning 52

Google Research AI blog

JANUARY 18, 2023

One trend that started with our work on Vision Transformers in 2020 is to use the Transformer architecture in computer vision models rather than convolutional neural networks. The neural network perceives an image, and generates a sequence of tokens for each object, which correspond to bounding boxes and class labels.

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Viso.ai

DECEMBER 17, 2024

After that, this framework has been officially opened to professional communities since 2016. Key Features of PaddlePaddle The following are its key features: Agile Framework for Neural Network Development PaddlePaddle helps make the process of creating deep neural networks easier. PyTorch is just like TensorFlow.

Deep Learning 52

Deep Learning Computer Vision Neural Network Artificial Intelligence 52

Pickl AI

AUGUST 22, 2024

First released in 2016, it quickly gained traction due to its intuitive design and robust capabilities. Overview of Keras Initially developed by François Chollet, Keras is an open-source neural network library written in Python. This flexibility allows users to efficiently design a wide range of neural network architectures.

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