Towards AI

DECEMBER 28, 2023

Last Updated on December 30, 2023 by Editorial Team Author(s): Luhui Hu Originally published on Towards AI. AI Power for Foundation Models (source as marked) As we bid farewell to 2023, it’s evident that the domain of computer vision (CV) has undergone a year teeming with extraordinary innovation and technological leaps.

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BAIR

JULY 10, 2023

Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). and “how does that learning actually occur?” lack basic answers.

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Machine Learning Mastery

DECEMBER 31, 2022

Last Updated on January 1, 2023 While a logistic regression classifier is used for binary class classification, softmax classifier is a supervised learning algorithm which is mostly used when multiple classes are involved. Softmax classifier works by assigning a probability distribution to each class.

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NYU Center for Data Science

JANUARY 25, 2024

2023’s event, held in New Orleans in December, was no exception, showcasing groundbreaking research from around the globe. In the world of data science, few events garner as much attention and excitement as the annual Neural Information Processing Systems (NeurIPS) conference.

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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Snorkel AI

OCTOBER 31, 2023

The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models.

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Google Research AI blog

APRIL 30, 2023

Posted by Catherine Armato, Program Manager, Google The Eleventh International Conference on Learning Representations (ICLR 2023) is being held this week as a hybrid event in Kigali, Rwanda. We are proud to be a Diamond Sponsor of ICLR 2023, a premier conference on deep learning, where Google researchers contribute at all levels.

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Snorkel AI

OCTOBER 31, 2023

The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models.

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Towards AI

AUGUST 29, 2023

Last Updated on August 30, 2023 by Editorial Team Author(s): Tan Pengshi Alvin Originally published on Towards AI. Introducing the backbone of Reinforcement Learning — The Markov Decision Process This member-only story is on us. Let’s first start with a broad overview of Machine Learning. Upgrade to access all of Medium.

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Google Research AI blog

JUNE 18, 2023

Posted by Shaina Mehta, Program Manager, Google This week marks the beginning of the premier annual Computer Vision and Pattern Recognition conference (CVPR 2023), held in-person in Vancouver, BC (with additional virtual content).

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Google Research AI blog

JULY 23, 2023

Google is proud to be a Diamond Sponsor of the 40th International Conference on Machine Learning (ICML 2023), a premier annual conference, which is being held this week in Honolulu, Hawaii. Google is also proud to be a Platinum Sponsor for both the LatinX in AI and Women in Machine Learning workshops. Registered for ICML 2023?

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NYU Center for Data Science

NOVEMBER 15, 2023

We’re excited to announce that many CDS faculty, researchers, and students will present at the upcoming thirty-seventh 2023 NeurIPS (Neural Information Processing Systems) Conference , taking place Sunday, December 10 through Saturday, December 16. The conference will take place in-person at the New Orleans Ernest N.

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ODSC - Open Data Science

AUGUST 29, 2023

Now if you want to take your prompt engineering skills to the next level, or want to learn the basics, then you don’t want to miss ODSC West 2023. At ODSC West, you’ll experience multiple tracks with Large Language Models, having its own track.

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Snorkel AI

MARCH 30, 2023

Here’s an overview of the Data-centric Foundation Model Development capabilities: Warm Start: Auto-label training data using the power of FMs + state-of-the-art zero- or few-shot learning techniques during onboarding, helping get to a powerful baseline “first pass” with minimal human effort. Interested in learning more about Snorkel Flow?

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Towards AI

MARCH 4, 2023

Last Updated on March 4, 2023 by Editorial Team Author(s): Harshit Sharma Originally published on Towards AI. Fully-Supervised Learning (Non-Neural Network) — powered by — Feature Engineering Supervised learning required input-output examples to train the model. Let’s get started !!

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Google Research AI blog

MAY 25, 2023

Chirp is able to cover such a wide variety of languages by leveraging self-supervised learning on unlabeled multilingual dataset with fine-tuning on a smaller set of labeled data. Chirp is now available in the Google Cloud Speech-to-Text API , allowing users to perform inference on the model through a simple interface.

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AssemblyAI

SEPTEMBER 18, 2023

Below, we'll give you the basic know-how you need to understand LLMs, how they work, and the best models in 2023. A large language model (often abbreviated as LLM) is a machine-learning model designed to understand, generate, and interact with human language. Here are the top large language models and frameworks as of 2023.

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Towards AI

JULY 22, 2023

Last Updated on July 24, 2023 by Editorial Team Author(s): Cristian Originally published on Towards AI. In the context of Machine Learning, data can be anything from images, text, numbers, to anything else that the computer can process and learn from. Instead, it learns by finding patterns and structures in the input data.

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AWS Machine Learning Blog

SEPTEMBER 10, 2024

The final phase improved on the results of HEEC and PORPOISE—both of which have been trained in a supervised fashion—using a foundation model trained in a self-supervised manner, namely Hierarchical Image Pyramid Transformer (HIPT) ( Chen et al., 2023 ), has been investigated in the final stage of the PoC exercises.

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Snorkel AI

SEPTEMBER 29, 2023

Some machine learning algorithms, such as clustering and self-supervised learning , do not require data labels, but their direct business applications are limited. Use cases for supervised machine learning models, on the other hand, cover many business needs.

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Becoming Human

MAY 15, 2023

The two most common types of supervised learning are classification , where the algorithm predicts a categorical label, and regression , where the algorithm predicts a numerical value.

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Towards AI

APRIL 18, 2023

Last Updated on April 21, 2023 by Editorial Team Author(s): Sriram Parthasarathy Originally published on Towards AI. Building disruptive Computer Vision applications with No Fine-Tuning Imagine a world where computer vision models could learn from any set of images without relying on labels or fine-tuning. Sounds futuristic, right?

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Snorkel AI

AUGUST 31, 2023

The learning stage uses techniques like semi-supervised learning that use few or no labels. Data + GenAI: a transformative pair Garter's 2023 Hype Cycle for Artificial Intelligence positions generative AI as an enterprise game-changer. Get your copy of the Gartner® Hype Cycle for Artificial Intelligence 2023 report today.

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ODSC - Open Data Science

AUGUST 10, 2023

This perspective amalgamates language understanding and future prediction into a formidable self-supervised learning objective. You’ll get that at the ODSC West 2023 Deep Learning & Machine Learning Track. This strategy imparts the agent with a profound grasp of language semantics.

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Snorkel AI

AUGUST 31, 2023

The learning stage uses techniques like semi-supervised learning that use few or no labels. Data + GenAI: a transformative pair Garter's 2023 Hype Cycle for Artificial Intelligence positions generative AI as an enterprise game-changer. Get your copy of the Gartner® Hype Cycle for Artificial Intelligence 2023 report today.

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Towards AI

JULY 23, 2023

Last Updated on July 25, 2023 by Editorial Team Author(s): Abhijit Roy Originally published on Towards AI. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train. But, the question is, how did all these concepts come together?

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Towards AI

OCTOBER 5, 2023

Last Updated on October 6, 2023 by Editorial Team Author(s): Nieves Crasto Originally published on Towards AI. Learning transferable visual models from natural language supervision.” International conference on machine learning. Can self-supervised learning techniques be leveraged to address this problem?

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Snorkel AI

AUGUST 31, 2023

The learning stage uses techniques like semi-supervised learning that use few or no labels. Bach, “Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning”, 2023. Adlia et al, “Zero-Shot Robustification of Zero-Shot Models With Auxiliary Foundation Models”, 2023.

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DrivenData Labs

JUNE 14, 2023

Between December 2022 and April 2023, 404 participants from 59 countries signed up to solve the problems posed by the two tracks, and 82 went on to submit solutions. Self-supervised learning allows for effective use of unlabeled data for training models for representation learning tasks.

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ODSC - Open Data Science

MARCH 22, 2023

Learn more about the data-centric AI techniques that power Cleanlab at our upcoming talk at ODSC East 2023. About the author/ODSC East 2023 speaker: Jonas Mueller is Chief Scientist and Co-Founder at Cleanlab, a company providing data-centric AI software to improve ML datasets.

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Snorkel AI

AUGUST 4, 2023

However, this requires access to a labeled data set—and we’re back to the world of supervised learning! Each combines foundation model outputs with weak supervision in order to obtain improved performance while sidestepping label-hungry fine-tuning methods. What can be done in settings with little or no labeled data?

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Mlearning.ai

SEPTEMBER 24, 2023

Available at: [link] (Accessed: 8 February 2023). 2019) Applied Supervised Learning with Python. Available at: [link] (Accessed: 19 February 2023). Johnston, B. and Mathur, I. Packt Publishing. Submission Suggestions How to Make GridSearchCV Work Smarter, Not Harder was originally published in MLearning.ai

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Towards AI

SEPTEMBER 7, 2023

Last Updated on September 8, 2023 by Editorial Team Author(s): Louis Bouchard Originally published on Towards AI. An analogy to explain how deep learning works… This member-only story is on us. link] When we talk about artificial intelligence, or AI, we tend to mean deep learning. Upgrade to access all of Medium.

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Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. What is self-supervised learning? Self-supervised learning is a kind of machine learning that creates labels directly from the input data. Find out in the guide below.

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Data Science Blog

MARCH 14, 2023

Von Data Science spricht auf Konferenzen heute kaum noch jemand und wurde hype-technisch komplett durch Machine Learning bzw. AI wiederum scheint spätestens mit ChatGPT 2022/2023 eine neue Euphorie-Phase erreicht zu haben, mit noch ungewissem Ausgang. Neben Supervised Learning kam auch Reinforcement Learning zum Einsatz.

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ODSC - Open Data Science

OCTOBER 3, 2023

The best place to do this is at ODSC West 2023 this October 30th to November 2nd. To excel in this field, you need a diverse skill set that can include a profound understanding of AI models, linguistic expertise, creative problem-solving skills, data analysis capabilities, and strong communication and collaboration skills.

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Snorkel AI

APRIL 18, 2023

Typically, you let the experts read some articles, label them, and then use them as training data and train the supervised learning model. To address all these problems, we looked into weak supervised learning. Once we label a fraction of documents, we use that as training data to train the supervised learning model.

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