Viso.ai

MARCH 26, 2024

described this model in the seminal paper titled “Attention is All You Need” in 2017. Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Vaswani et al.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. Additional resources: Adrian, R. Deep learning for computer vision with Python.

Computer Vision 97

Computer Vision Robotics Deep Learning Convolutional Neural Networks 97

AWS Machine Learning Blog

FEBRUARY 10, 2023

We describe how we designed an accurate, explainable ML model to make coverage classification from player tracking data, followed by our quantitative evaluation and model explanation results. This explains the uncertainty of the model’s classification: the TE is sticking with the SS by design, creating biases in the data. Jay Alammar.

Computer Vision 80

Computer Vision Machine Learning ML Convolutional Neural Networks 80

ML Review

JUNE 4, 2018

2017)[ 51 ] Introduction to Image Captioning Suppose that we asked you to caption an image; that is to describe the image using a sentence. These ideas also move in step with the explainability of results. If language grounding is achieved, then the network tells me how a decision was reached. 2017) [ 96 ].

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Snorkel AI

MARCH 1, 2023

This can make it challenging for businesses to explain or justify their decisions to customers or regulators. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. Interpretability Foundation models are often described as “black boxes.” What are foundation models?

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). link] Constructing a system for NLI that explains its decisions by pointing to the most relevant parts of the input. ArXiv 2017.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

Heartbeat

DECEMBER 14, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. > Finished chain. . >

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Scientist 52

Heartbeat

JULY 24, 2023

In this particular case, there are a total of eight layers in the convolutional neural network that contribute to the training process. This Meshnet model is inspired by multi-scale context aggregation by dilated convolutions, which is a technique that expands the input layer by introducing holes in it. 5098, 2023.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Machine Learning 52

ML Review

MAY 3, 2018

In Deep Learning: Practice and Trends (NIPS 2017) [2] , prominent researchers offered a simple abstraction — that virtually all deep learning approaches can be characterised as either augmenting architectures or loss functions, or applying the previous to new input/output combinations. 2017) present their WLAS Network.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

Heartbeat

DECEMBER 4, 2023

Mobilenets: Efficient convolutional neural networks for mobile vision applications.” arXiv preprint arXiv:1704.04861 (2017). Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

Artificial Intelligence 59

Artificial Intelligence Artificial Intelligence Deep Learning Convolutional Neural Networks 59