NYU Center for Data Science

SEPTEMBER 11, 2024

Ho’s innovative approach has led to several groundbreaking achievements: Her team at Carnegie Mellon University was the first to apply 3D convolutional neural networks in astrophysics. She led the first effort to accelerate astrophysical simulations with deep learning.

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NVIDIA

OCTOBER 4, 2023

Harnessing the raw power of NVIDIA GPUs and aided by a network of thousands of cameras dotting the Californian landscape, DigitalPath has refined a convolutional neural network to spot signs of fire in real time. a short drive from the town of Paradise, where the state’s deadliest wildfire killed 85 people in 2018.

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Marktechpost

MAY 28, 2024

GPT-3: The Game Changer GPT-3, unveiled in June 2020, took the AI community by storm with its unprecedented scale and capabilities. This parallel processing capability allows Transformers to handle long-range dependencies more effectively than recurrent neural networks (RNNs) or convolutional neural networks (CNNs).

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Sebastian Ruder

JANUARY 19, 2021

2020 ), Turing-NLG , BST ( Roller et al., 2020 ), and GPT-3 ( Brown et al., 2020 ; Fan et al., 2020 ), quantization ( Fan et al., 2020 ), and compression ( Xu et al., 2020 ; Fan et al., 2020 ), quantization ( Fan et al., 2020 ), and compression ( Xu et al., 2020 ) and Big Bird ( Zaheer et al.,

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Marktechpost

MAY 31, 2024

This model leverages a vast dataset of multi-modal sleep recordings from over 14,000 participants, totaling more than 100,000 hours of sleep data collected between 1999 and 2020 at the Stanford Sleep Clinic. SleepFM utilizes a contrastive learning approach to integrate brain activity, ECG, and respiratory signals.

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Mlearning.ai

MAY 11, 2023

ChatGPT is an AI language model that has taken the world by storm since its release in 2020. What lies behind GPT is a type of artificial intelligence (AI) called a neural network. What the heck is neural network? A neural network is kind of like a big group of friends working together to solve a puzzle.

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Mlearning.ai

APRIL 8, 2023

Over the years, we evolved that to solving NLP use cases by adopting Neural Network-based algorithms loosely based on the structure and function of a human brain. The birth of Neural networks was initiated with an approach akin to structuring solving problems with algorithms modeled after the human brain.

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Marktechpost

OCTOBER 11, 2023

One challenge, for instance, is to boost the performance of a Convolution Neural Networks (CNN) model by more than 10% on the cifar10 dataset. They provide simple beginning programs for some of these activities to guarantee that the agent can make valid submissions. It achieves an average improvement of 48.18

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Viso.ai

NOVEMBER 4, 2024

Computer Vision Model for Solar Prediction The researchers based their solution on computer vision, specifically deep Convolutional neural networks (CNNs) for object localization and identification. Lastly, the model in recurrent neural network techniques (e.g.

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Heartbeat

JANUARY 11, 2023

Using the EfficientNet Architecture to Classify Car Brands — by Harpreet Sahota How do you measure how big a convolutional neural network is? Until 2020, the process of measuring a convolutional neural network was never well understood. You can’t weigh it or use a ruler to measure it.

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Artificial Corner

JUNE 23, 2023

Like U-Net, TransUnet includes two main sections: Encoder (Vision Transformer): Vision Transformer encodes patches of feature maps that are generated by a convolution neural network. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (2020) [3] Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, E.

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. No 2020 Jul iGPT The transformer model, originally developed for NLP, can also be used for image pre-training.

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Bugra Akyildiz

NOVEMBER 17, 2024

They have shown impressive performance in various computer vision tasks, often outperforming traditional convolutional neural networks (CNNs). 2020) EBM : Explainable Boosting Machine (Nori, et al. 2020) OpenCoder is an open and reproducible code LLM family which includes 1.5B 2019; Lou, et al. 2019; Lou, et al.

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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.

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Flipboard

MAY 17, 2023

CEOs of major auto companies were all saying by 2020 or 2021 or 2022, roughly. Convolutional neural networks being able to label regions of an image. So there’s a case where AI, convolutional neural networks—which, by the way, applied to vision is 10 years old—is going to make a difference.

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Unite.AI

OCTOBER 10, 2024

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks. In contrast, JPEG-DL (right) succeeds in distinguishing and delineating the subject of the photo.

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Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis. Richong, Z.,

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Viso.ai

NOVEMBER 5, 2024

Released in 2020, YOLOv4 enhances the performance of its predecessor, YOLOv3, by bridging the gap between accuracy and speed. Convolution Layer: The concatenated feature descriptor is then passed through a Convolution Neural Network. This step outputs both global and local information from the feature maps.

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Viso.ai

OCTOBER 15, 2024

In the field of real-time object identification, YOLOv11 architecture is an advancement over its predecessor, the Region-based Convolutional Neural Network (R-CNN). Using an entire image as input, this single-pass approach with a single neural network predicts bounding boxes and class probabilities.

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How to Learn Machine Learning

MAY 14, 2023

As a machine learning engineer, it’s essential to master deep learning techniques and neural networks, as they have become integral to solving complex problems in various domains. In addition to deep learning, it’s beneficial to specialize in a specific area or technique within machine learning.

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Viso.ai

MAY 6, 2024

The latest version is Kinetics 700-2020, which contains over 700 human action classes from up to 650,000 video clips. Source ) TensorFlow and PyTorch are two frameworks offering tools and libraries to implement and train these networks. An introduction to Graph Neural Networks (GNNs).

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Viso.ai

FEBRUARY 19, 2024

To overcome this IP concern – researchers have applied a Convolutional Neural Network (CNN) to detect plagiarized text and images as well as problematic deepfakes on the internet. 2020) applied an image copy detection scheme based on the deep learning Inception CNN model.

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Heartbeat

MAY 22, 2023

2020 ) can be integrated to add greater weight to the core features. Schematic diagram of the overall framework of Emotion Recognition System [ Source ] The models that are used for AI emotion recognition can be based on linear models like Support Vector Machines (SVMs) or non-linear models like Convolutional Neural Networks (CNNs).

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Viso.ai

DECEMBER 18, 2023

YOLO’s architecture was a significant revolution in the real-time object detection space, surpassing its predecessor – the Region-based Convolutional Neural Network (R-CNN). The backbone is a pre-trained Convolutional Neural Network (CNN) that extracts low, medium, and high-level feature maps from an input image.

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Marktechpost

JULY 24, 2023

Up-to-date machine learning-based data-driven techniques directly handle downstream forecasting or projection tasks by training a data-driven functional mapping in deep neural networks. These networks lack numerical model generality since they are trained on limited and consistent climate data for discrete spatiotemporal tasks.

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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.

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Viso.ai

JUNE 4, 2024

Many of these methods utilize deep learning and Convolutional Neural Networks (CNNs) to create point cloud processing. Deep Learning Convolutional-Based Method Processing irregular, unstructured point cloud data remains a formidable challenge, despite the deep learning’s success in processing structured 2D image data.

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Viso.ai

OCTOBER 7, 2024

Computer vision (CV) models rely on deep learning architectures consisting of artificial neural networks or convolutional neural networks (CNNs). 2020)): An improvement from 0.645 accuracy to 0.846 with the framework used. However, these methods can encounter limitations in handling some scenarios.

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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?

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AWS Machine Learning Blog

FEBRUARY 10, 2023

For a given frame, our features are inspired by the 2020 Big Data Bowl Kaggle Zoo solution ( Gordeev et al. ): we construct an image for each time step with the defensive players at the rows and offensive players at the columns. He started at the NFL in February 2020 as a Data Scientist and was promoted to his current role in December 2021.

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Viso.ai

MAY 20, 2023

It requires several times cheaper hardware than other neural networks and can be trained much faster on small datasets without any pre-trained weights. Most algorithms use a convolutional neural network (CNN) to extract features from the image to predict the probability of learned classes.

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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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ML Review

DECEMBER 16, 2019

NeurIPS’18 presented several papers with deep theoretical studies of building hyperbolic neural nets. Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN). Thank you for reading!

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Mlearning.ai

FEBRUARY 2, 2023

In this article, I show how a Convolutional Neural Network can be used to predict a person's age based on the person's ECG Attia et al 2019 [1], showed that a person's age could be predicted from an ECG using convolutional neural networks (CNN). Data Min Knowl Disc 34 , 1936–1962 (2020).

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Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.

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