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.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 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. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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. Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation.

Computer Vision 73

Computer Vision Machine Learning ML Convolutional Neural Networks 73

Marek Rei

NOVEMBER 12, 2019

ArXiv 2018. The generative part is then evaluated as a language model, while the inference network is evaluated as an unsupervised unlabeled constituency parser. EMNLP 2018. NAACL 2018. NAACL 2018. At the end, I also include the summaries for my own published papers since the last iteration (papers 61-74).

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings? using its Spectrogram ).

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

deepsense.ai

MAY 7, 2023

We also explained the building blocks of Stable Diffusion and highlighted why its release last year was such a groundbreaking achievement. Source: [ 2 ] In the previous post, we explained the importance of Stable Diffusion [ 3 ]. 2022 [link] Going deeper with convolutions , Szegedy et al. But don’t worry!

Neural Network 52

Neural Network Convolutional Neural Networks Explainability 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

ML Review

JUNE 4, 2018

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. In image captioning a network is not only required to classify objects, but instead to describe objects (including people and things) and their relations in a given image.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 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

ML Review

MAY 3, 2018

22] On a high level in the architecture, the frames extracted from a video sequence are processed in small sets within a Convolutional Neural Network (CNN), [23] while an LSTM-variant runs on the CNN output sequentially to generate output characters. A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52