Bugra Akyildiz

DECEMBER 10, 2023

It uses a graph neural network (GNN) to model the relationships between different weather variables. degree resolution, 13 pressure levels) pre-trained on ERA5 data from 1979 to 2017 and fine-tuned on HRES data from 2016 to 2021. This allows it to capture complex relationships that traditional models cannot.

Neural Network 52

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Topbots

MARCH 5, 2024

A Brief Look Into the History of Creative AI Generative Adversarial Networks (GANs) for image generation were introduced in 2014. Then in 2016, DeepMind introduced WaveNet and audio generation. The research advancements quickly transformed into practical applications.

Large Language Models 52

Large Language Models AI AI Software Development 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

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How to Learn Machine Learning

DECEMBER 4, 2022

Numerous techniques, such as but not limited to rule-based systems, decision trees, genetic algorithms, artificial neural networks, and fuzzy logic systems, can be used to do this. In 2016, Google released an open-source software called AutoML. One recent example of the usage of Ai is in the field of code writing.

Artificial Intelligence 52

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AssemblyAI

NOVEMBER 8, 2023

  Each stage leverages a deep neural network that operates as a sequence labeling problem but at different granularities: the first network operates at the token level and the second at the character level. Training Data : We trained this neural network on a total of 3.7 billion words).   Fig.

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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.

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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].

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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!

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The Stanford AI Lab Blog

OCTOBER 13, 2021

Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. SelectiveNet: A deep neural network with an integrated reject option. In International Conference on Machine Learning (ICML), 2016. ↩ ↩ 2 Yonatan Geifman and Ran El-Yaniv.

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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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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.

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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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Heartbeat

SEPTEMBER 13, 2023

Alibi supports various models, including deep neural networks, and allows users to generate explanations for individual predictions. It provides a collection of techniques, including counterfactual explanations, contrastive explanations, and adversarial explanations. Singh, S. & & Geustrin, C.

Explainability 52

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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

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

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Snorkel AI

MAY 3, 2023

A fun story that I want to share—I remember back in 2016-2017ish when we started working on this problem and submitted one of our first papers on OOD detection called Odin to the conference. This out-of-distribution detection problem has become very important. Here, I’m showing you one of my favorite figures.

Machine Learning 52

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Snorkel AI

MAY 3, 2023

A fun story that I want to share—I remember back in 2016-2017ish when we started working on this problem and submitted one of our first papers on OOD detection called Odin to the conference. This out-of-distribution detection problem has become very important. Here, I’m showing you one of my favorite figures.

Machine Learning 52

Machine Learning Neural Network Explainability Data Scientist 52

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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Explosion

AUGUST 1, 2019

Deep neural networks have offered a solution, by building dense representations that transfer well between tasks. In the last few years, research has shown that linguistic knowledge can be acquired effectively from unlabelled text, so long as the network is large enough to represent the long-tail of rare usage phenomena.

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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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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

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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.

Deep Learning 52

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Marek Rei

JANUARY 17, 2018

The only filter that I applied was to exclude papers older than 2016, as the goal is to give an overview of the more recent work. They present a simple classifier that achieves unexpectedly good results, and a neural network based on attention that beats all previous results by quite a margin. NAACL 2016. Google, OpenAI.

Machine Learning 52

Machine Learning NLP Neural Network Convolutional Neural Networks 52

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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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

Mlearning.ai

MARCH 9, 2023

Neural Networks, 64, 59–63. Intriguing properties of neural networks. A simple guide for defending deep neural networks. 2018; Papernot et al., Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083. Papernot, N., McDaniel, P., Fredrikson, M., B., & Swami, A.

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 52

Mlearning.ai

JUNE 16, 2023

Practitioners first trained a Convolutional Neural Network (CNN) to perform image classification on ImageNet (i.e. December 10, 2016. The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. pre-training). fine-tuning). November 21, 2018.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision AI AI 52

TheSequence

DECEMBER 13, 2023

My path to working in AI is somewhat unconventional and began when I was wrapping up a postdoc in theoretical particle physics around 2016. I was surprised to learn that a few lines of code could outperform features that had been carefully designed by physicists over many years.

LLM 64

LLM Natural Language Processing Machine Learning Deep Learning 64

Viso.ai

MAY 1, 2024

Deep learning and Convolutional Neural Networks (CNNs) have enabled speech understanding and computer vision on our phones, cars, and homes. Stanford University and panel researchers P. Stone and R. Brooks et al. Brooks et al.

Robotics 52

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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.

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John Snow Labs

MAY 23, 2023

VQA frameworks combine two Deep Learning architectures to deliver the final answer: Convolutional Neural Networks (CNN) for image recognition and Recurrent Neural Network (RNN) (and its special variant Long Short Term Memory networks or LSTM) for NLP processing.

Natural Language Processing 40

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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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deepsense.ai

MAY 7, 2023

Next, we embed the images using an Inception-based [ 5 ] neural network. This solution is based on several Convolutional Neural Networks that work in a cascade fashion to locate the face with some landmarks in an image. 2014 Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , Zhang et al.

Neural Network 52

Neural Network Convolutional Neural Networks Explainability Deep Learning 52

Probably Approximately a Scientific Blog

JANUARY 12, 2021

The release of Google Translate’s neural models in 2016 reported large performance improvements: “60% reduction in translation errors on several popular language pairs”. With that said, the path to machine commonsense is unlikely to be brute force training larger neural networks with deeper layers.

Natural Language Processing 52

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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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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.

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