Dataconomy

JUNE 20, 2023

Voicebox, according to Meta, can do speech-generating jobs it was not particularly trained on, in contrast to earlier voice-generator platforms. Meta Voicebox is capable of editing, sampling, and stylizing speech even though they weren’t particularly trained to do so through in-context learning.

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ODSC - Open Data Science

JULY 27, 2023

A result of a collaborative research effort between ETH Zurich and Microsoft, LightGlue leverages a deep network that combines image matching and outlier rejection. To solve this issue the team developed LightGlue as a more accurate, efficient, and easier-to-train alternative. LightGlue also presents a Pareto-optimal solution.

Data Science 52

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ODSC - Open Data Science

JULY 3, 2023

If you don’t know, LLMs are a type of artificial intelligence that is trained on massive amounts of text data. It is a Transformer-based model trained using a mixture of objectives similar to UL2. Pythia Pythia is a suite of 16 LLMs trained on the same public data that can be used to study the development and evolution of LLMs.

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Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps.

Machine Learning 97

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Defined.ai blog

FEBRUARY 20, 2023

These algorithms consist of two parts: a generator network that produces synthetic data, and a discriminator network that attempts to distinguish the synthetic from real data. During training, the model learns to generate output images that are similar to the corresponding target images in the training set.

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Flipboard

APRIL 4, 2023

It pitted established male EDA experts against two young female Google computer scientists, and the underlying argument had already led to the firing of one Google researcher. In most cases, Circuit Training was not the winner, but it was competitive. Circuit Training then starts placing the macros on the chip “canvas” one at a time.

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O'Reilly Media

MARCH 26, 2024

TL;DR LLMs and other GenAI models can reproduce significant chunks of training data. Specific prompts seem to “unlock” training data. Generative AI Has a Plagiarism Problem ChatGPT, for example, doesn’t memorize its training data, per se. This is the basis of The New York Times lawsuit against OpenAI.

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Google Research AI blog

MARCH 2, 2023

This allows the training of models on locally available signals without exposing raw data to servers, increasing user privacy. This allows the training of models on locally available signals without exposing raw data to servers, increasing user privacy.

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ODSC - Open Data Science

MARCH 27, 2023

Our expert speakers will cover a wide range of topics, tools, and techniques that data scientists of all levels can apply in their work. ODSC Europe is still a few months away, coming this June 14th-15th, but we couldn’t be more excited to announce our first group of sessions. Check a few of them out below.

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Towards AI

MAY 11, 2023

My goal is to provide a clear understanding of the differences and similarities among these models to guide researchers and practitioners in choosing the most appropriate deep generative model for their specific applications. GANs [1, 2] learn to generate new data similar to a training dataset. Training by adversarial loss.

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Chatbots Life

APRIL 16, 2024

Machine learning has revolutionized various fields by enabling computers to learn from data and make accurate predictions or classifications. Their aim is to learn patterns from the data and generate new samples with similar characteristics. They can identify the distribution of continuous data and mimic it.

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Mlearning.ai

APRIL 17, 2023

In this research paper , ensemble learning is presented as a solution to the challenge of false positives in fraud detection. For a brief overview of these models, please see below: Data The researcher of the paper used the same credit card fraud transaction dataset from Kaggle, which I had used in the previous code reconstruction article.

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ODSC - Open Data Science

JANUARY 5, 2024

It improves upon existing diffusion-based image generation techniques through several key features, including streamlined data processing, enhanced guidance mechanisms, advanced GPU filtering, and efficient input/output management. Mixtral Offloading Exciting news for Open Souce AI enthusiasts!

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IBM Journey to AI blog

APRIL 18, 2024

A world where computer minds pilot self-driving cars, delve into complex scientific research, provide personalized customer service and even explore the unknown. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

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ODSC - Open Data Science

JANUARY 11, 2024

Open Data Science Blog Recap: Samsung Poised to Show Off New Smartphone With AI Taking The Lead Role: The electronics maker is poised to introduce a new smartphone that will have AI taking the lead role for its new Galaxy S series phone. Artificial intelligence has been moving at the speed of light with all of the news that has come out.

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Dataconomy

FEBRUARY 28, 2023

Deep learning models have emerged as a powerful tool in the field of ML, enabling computers to learn from vast amounts of data and make decisions based on that learning. With this growth comes the need for more advanced algorithms and models to process and analyze this data. What are deep learning models?

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AWS Machine Learning Blog

NOVEMBER 16, 2023

With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production.

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Pickl AI

SEPTEMBER 18, 2023

These AI systems can generate new data or content rather than simply analyzing or processing existing data. These algorithms learn from massive datasets and then generate fresh material that resembles the training data. GANs are widely used for generating images, videos, and other types of data.

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Google Research AI blog

APRIL 21, 2023

We demonstrate how this platform enables a better model evaluation experience through interactive characterization and visualization of ML model performance and interactive data augmentation and comparison. Study participants included seven computer vision researchers, audio ML researchers, and engineers across three ML teams.

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PyImageSearch

OCTOBER 2, 2023

A special emphasis will be placed on the Gaussian distribution’s pivotal role in VAEs and the balance between reconstruction loss and KL divergence. The highlight will be the VAE training on the Fashion-MNIST data, followed by a detailed post-training analysis. Let’s get started!

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AssemblyAI

SEPTEMBER 29, 2023

At the highest level, DALL-E 2's works very simply: First, a text prompt is input into a text encoder that is trained to map the prompt to a representation space. The link between textual semantics and their visual representations in DALL-E 2 is learned by another OpenAI model called CLIP ( C ontrastive L anguage- I mage P re-training).

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Google Research AI blog

APRIL 30, 2023

We are proud to be a Diamond Sponsor of ICLR 2023, a premier conference on deep learning, where Google researchers contribute at all levels. Continue below to find the many ways in which Google researchers are engaged at ICLR 2023, including workshops, papers, posters and talks (Google affiliations in bold ).

Supervised Learning 105

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PyImageSearch

OCTOBER 23, 2023

Our journey begins with training a VAE on this dataset, setting the stage for a series of captivating experiments. By the time we conclude, you’ll have a comprehensive understanding of how to implement, train, and experiment with VAEs using PyTorch.

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Deep Learning Deep Learning Computer Science Computer Science 67

Mlearning.ai

JUNE 22, 2023

While factors like the number of parameters, activation functions, architectural nuances, context sizes, pretraining data corpus, and languages used in training differentiate these models, one often overlooked aspect that can significantly impact their performance is the training process.

AI 52

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Flipboard

JULY 10, 2023

How Autoencoders Achieve High-Quality Reconstructions? Architecture Training Process Objectives Summary Citation Information Introduction to Autoencoders Uncover the intriguing world of Autoencoders with this tutorial. no class labels or labeled data) such as dimensionality reduction, feature extraction, and data compression.

Deep Learning 110

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PyImageSearch

JULY 17, 2023

visualizing the latent space, uniform sampling of data points from this latent space, and recreating images using these sampled points). Pause momentarily, tap into your inner data scientist, and register for your no-strings-attached Roboflow account. ➤ We will then explore different testing situations (e.g.,

Deep Learning 98

Deep Learning Deep Learning Python Machine Learning 98

Heartbeat

AUGUST 1, 2023

Surely research in this field yielded results that could be used without requiring neural networks. It learns the features of different images similarly and does not have the limitation of working like a deep neural net, i.e., learning features specifically from the training set. It has a relatively straightforward method of operation.

Deep Learning 52

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AWS Machine Learning Blog

MARCH 15, 2024

With increased access to data, ML has the potential to provide unparalleled business insights and opportunities. To address this issue, federated learning (FL) is a decentralized and collaborative ML training technique that offers data privacy while maintaining accuracy and fidelity.

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Explosion

AUGUST 1, 2019

Support is provided for fine-tuning the transformer models via spaCy’s standard nlp.update training API. Transformer-based pipelines won’t be perfect for every use-case, but they’re not just for research either: even if you’re processing text at scale, there are lots of ways your team could make use of these huge but highly accurate models.

Natural Language Processing 52

Natural Language Processing AWS Machine Learning Machine Learning 52

Google Research AI blog

MARCH 23, 2023

While there have been many recent advances in training joint visual-language models on Internet-scale data, figuring out how to best connect them to a spatial representation of the physical world that can be used by robots remains an open research question.

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Google Research AI blog

JULY 6, 2023

Posted by Kuniaki Saito, Student Researcher, Google Research, Cloud AI Team, and Kihyuk Sohn, Research Scientist, Google Research Image retrieval plays a crucial role in search engines. However, CIR methods require large amounts of labeled data, i.e., triplets of a 1) query image, 2) description, and 3) target image.

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ODSC - Open Data Science

OCTOBER 13, 2023

Researchers from Google and Cornell Introduce DynIBaR: Revolutionizing Dynamic Scene Reconstruction with AI A new paper from researchers from Google and Cornell has introduced DynlBaR, a new method for generating photorealistic free-viewpoint rendering.

Data Science 52

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Google Research AI blog

NOVEMBER 9, 2022

Li, Research Scientist, and Sven Dorkenwald, Student Researcher, Connectomics at Google Mapping the wiring and firing activity of the human brain is fundamental to deciphering how we think — how we sense the world, learn, decide, remember, and create — as well as what issues can arise in brain disease or dysfunction. Posted by Peter H.

Clustering 52

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Google Research AI blog

JUNE 22, 2023

Posted by Zalán Borsos, Research Software Engineer, and Marco Tagliasacchi, Senior Staff Research Scientist, Google Research The recent progress in generative AI unlocked the possibility of creating new content in several different domains, including text, vision and audio. In the case of audio, neural audio codecs (e.g.,

AI 98

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ODSC - Open Data Science

MARCH 1, 2024

Open Data Science Blog Recap Paris-based Mistral AI is emerging as a formidable challenger to industry giants like OpenAI and Anthropic. Open Data Science Blog Recap Paris-based Mistral AI is emerging as a formidable challenger to industry giants like OpenAI and Anthropic. 7B parameter model: Trained on 6 trillion tokens of text data.

Artificial Intelligence 98

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AssemblyAI

SEPTEMBER 22, 2023

This technique entails training the model to learn joint embeddings of text and images, capturing the relationships between words (e.g., How, then, to effectively train a text-music joint embedding? However, the text descriptions used to train MuLan are often only loosely connected with the corresponding audio.

AI 98

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Heartbeat

MAY 9, 2023

Photo by Nik on Unsplash If you are a natural language processing researcher or have an interest in this field, you have surely come across SpaCy or you are very close to it! Data fields are: text: a string feature. small_train_dataset = ds["train"].shuffle(seed=34).take(5000) small_train_dataset = ds["train"].shuffle(seed=34).take(5000)

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