deep-learning
The ArtBench Dataset: Benchmarking Generative Models with Artworks
We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. It comprises 60,000 images of artwork from 10...
Minerva: Solving Quantitative Reasoning Problems with Language Models
Posted by Ethan Dyer and Guy Gur-Ari, Research Scientists, Google Research, Blueshift Team Language models have demonstrated remarkable performance...
How Imagen Actually Works
Learn how Imagen generates photorealistic images given only a text description.
Generating Children's Stories Using GPT-3 and DALL·E
We used GPT-3 and DALL·E to generate a children's storybook about Ash and Pikachu vs. Team Rocket. Read the story and marvel at the AI-generated visuals!
Recipe Cuisine Classification
BERT Transformer & Food-Drug Negative Interactions
How to Build an Image-Captioning Model in Pytorch
A detailed step-by-step explanation of how to build an image-captioning model in Pytorch
Google bans deepfake-generating AI from Colab
In a recent policy change, Google has banned deepfake-generating AI projects from Colab, its platform for hosting and running arbitrary Python code.
Face to Face With Dall-E, The AI Artist That Might Change The World
Dall-E can illustrate just about anything using a short text prompt. Should it?
A Guide To Asking Robots To Design Stained Glass Windows
...
A Face Search Engine Anyone Can Use Is Alarmingly Accurate
PimEyes is a paid service that finds photos of a person from across the internet, including some the person may not want exposed. “We’re just a tool provider,” its owner said.
Sparse Autoencoder Neural Networks — How to Utilise Sparsity for Robust Inf
A comparison between Undercomplete and Sparse AE with a detailed Python example
Another Firing Among Google’s A.I. Brain Trust, and More Discord (Published 2022)
The researchers are considered a key to the company’s future. But they have had a hard time shaking infighting and controversy over a variety of issues.
The Modern Mathematics of Deep Learning
We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning...
Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrou
Posted by Sharan Narang and Aakanksha Chowdhery, Software Engineers, Google Research In recent years, large neural networks trained for language un...
Autoencoders (AE) — A Smart Way to Process Your Data Using Unsupervised Neural Networks
What is an Autoencoder, and how to build one in Python?
NVIDIA NeRF AI Renders Amazingly Realistic 3D Scenes From 2D Photos In Just
It takes a human being around 0.1 to 0.4 seconds to blink. In even less time, an AI-based inverse rendering process developed by NVIDIA can generate a 3D scene from 2D photos.
AI Virtual Assistant Technology Guide 2022
They can help you get an appointment or order a pizza, find the best ticket deals and bring your...
Autoencoders: From Vanilla to Variational
Because GANs are not all you need
A Comprehensive Benchmark of Deep Learning Libraries on Mobile DevicesA Com
Machine Learning Algorithms Cheat Sheet — Accel.AI
Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way people learn, progressively improving its accuracy. This way, Machine Learning is one of the most interesting methods in Computer…
scikit-and-tensorflow-workbooks/ch14-Recurrent-NNs.ipynb at master · bjpcjp/scikit-and-tensorflow-workbooks
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks
scikit-and-tensorflow-workbooks/ch15-autoencoders.ipynb at master · bjpcjp/scikit-and-tensorflow-workbooks
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks
Deep Learning Interviews: Hundreds of fully solved job interview...
The second edition of Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific...
Curating a Dataset from Raw Images and Videos
Best-practices to follow when building datasets from large pools of image and video data and tools that make it straightforward.
Detecting Twenty-thousand Classes using Image-level Supervision
Current object detectors are limited in vocabulary size due to the small scale of detection datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as their datasets...
Ten Lessons From Three Generations Shaped Google’s TPUv4i - 2021-jouppi.pdf
[1909.10140] A new coefficient of correlation
Is it possible to define a coefficient of correlation which is (a) as simple as the classical coefficients like Pearson's correlation or Spearman's correlation, and yet (b) consistently estimates...
Low-Power AI Startup Eta Compute Delivers First Commercial Chips
The firm pivoted away from riskier spiking neural networks using a new power management scheme
PyTorch vs TensorFlow in 2023
Should you use PyTorch vs TensorFlow in 2023? This guide walks through the major pros and cons of PyTorch vs TensorFlow, and how you can pick the right framework.
Machine-Learning-Tokyo/Interactive_Tools: Interactive Tools for Machine Learning, Deep Learning and Math
Interactive Tools for Machine Learning, Deep Learning and Math - Machine-Learning-Tokyo/Interactive_Tools
Under The Hood Of Google’s TPU2 Machine Learning Clusters
As we previously reported, Google unveiled its second-generation TensorFlow Processing Unit (TPU2) at Google I/O last week. Google calls this new
3D Stacking Could Boost GPU Machine Learning
Nvidia has staked its growth in the datacenter on machine learning. Over the past few years, the company has rolled out features in its GPUs aimed neural
HPC Technique Propels Deep Learning at Scale
Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are […]
Analysis and Comparison of Performance and Power Consumption of Neural Netw
In this work, we analyze the performance of neural networks on a variety of heterogenous platforms. We strive to find the best platform in terms of raw benchmark performance, performance per watt a…
pylabel-project/pylabel: Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations…
Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo. - GitHub - pylabel-proj...
Tearing Apart Google’s TPU 3.0 AI Coprocessor
Google did its best to impress this week at its annual IO conference. While Google rolled out a bunch of benchmarks that were run on its current Cloud TPU
First In-Depth Look at Google’s TPU Architecture
Four years ago, Google started to see the real potential for deploying neural networks to support a large number of new services. During that time it was
Survey paper on Deep Learning on GPUs
The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. GPU continues to remain the most widely used accelerator for DL applications. We present a survey of architectur…
How to make your own deep learning accelerator chip!
Currently there are more than 100 companies all over the world building ASIC’s (Application specific integrated circuit) or SOC’s (System…
Vivienne Sze · Efficient Processing of Deep Neural Network: from Algorithms to Hardware Architectures
This tutorial describes methods to enable efficient processing for deep neural networks (DNNs), which are used in many AI applications including computer vision, speech recognition, robotics, etc....
louisfb01/best_AI_papers_2021: A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation…
A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code. - louisfb01/best_AI_papers_2021
https://blog.riseml.com/comparing-google-tpuv2-against-nvidia-v100-on-resnet-50-c2bbb6a51e5e
http://research.baidu.com/bringing-hpc-techniques-deep-learning/
Memory at the Core of New Deep Learning Research Chip
Over the last two years, there has been a push for novel architectures to feed the needs of machine learning and more specifically, deep neural networks.
Four Deep Learning Papers to Read in December 2021
From Sensory Substitution to Decision Transformers, Persistent Evolution Strategies and Sharpness-Aware Minimization
AI-Based Image Compression: The State of the Art
An overview of some of the leading libraries and frameworks out there
Transformers
AI-Based Image Compression: The State of the Art
An overview of some of the leading libraries and frameworks out there
MedMNIST v2 Dataset | Papers With Code
MedMNIST v2 is a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28 x 28 (2D) or 28 x 28 x 28 (3D) with the corresponding classification labels, so that no background…