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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Mlearning.ai

JULY 6, 2023

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., 2022’s paper. 2022 Deep learning notoriously needs a lot of data in training.

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AWS Machine Learning Blog

MARCH 6, 2024

As shown in the following table, many of the top-selling drugs in 2022 were either proteins (especially antibodies) or other molecules like mRNA translated into proteins in the body. Name Manufacturer 2022 Global Sales ($ billions USD) Indications Comirnaty Pfizer/BioNTech $40.8 Top companies and drugs by sales in 2022.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Given the availability of diverse data sources at this juncture, employing the CNN-QR algorithm facilitated the integration of various features, operating within a supervised learning framework. He joined Getir in 2022 as a Data Scientist and started working on time-series forecasting and mathematical optimization projects.

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Google Research AI blog

APRIL 26, 2023

These models are trained using data at scale, often by self-supervised learning. This process results in generalist models that can rapidly be adapted to new tasks and environments with less need for supervised data. The specific approach used for pre-training and learning representations is SimCLR.

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Google Research AI blog

FEBRUARY 10, 2023

In 2022, we continued this journey, and advanced the state-of-the-art in several related areas. We continued our efforts in developing new algorithms for handling large datasets in various areas, including unsupervised and semi-supervised learning , graph-based learning , clustering , and large-scale optimization.

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Snorkel AI

MARCH 24, 2023

Snorkel AI researchers continue to push the frontier of machine learning, as demonstrated by the 18 research papers recently added to our website. This batch of research papers, all published in 2022, present new developments in weak supervision and foundation models.

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Snorkel AI

MARCH 24, 2023

Snorkel AI researchers continue to push the frontier of machine learning, as demonstrated by the 18 research papers recently added to our website. This batch of research papers, all published in 2022, present new developments in weak supervision and foundation models.

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Chatbots Life

APRIL 19, 2024

This technology employs different learning methods during training. These include unsupervised or semi-supervised learning. It analyzes existing data to discover patterns and generate new content. Two Generative AI models are generative adversarial networks (GANs) and transformer-based models.

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DrivenData Labs

JUNE 14, 2023

In December 2022, DrivenData and Meta AI launched the Video Similarity Challenge. 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. student in ReLER, University of Technology Sydney, supervised by Yi Yang.

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Hacker News

MARCH 25, 2022

Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago.

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IBM Journey to AI blog

AUGUST 11, 2023

Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022. This advancement in AI means that data sets aren’t task specific—the model can apply information it’s learned about one situation to another.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning types Machine learning algorithms fall into five broad categories: supervised learning, unsupervised learning, semi-supervised learning, self-supervised and reinforcement learning. Manage a range of machine learning models with watstonx.ai temperature, salary).

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Google Research AI blog

JANUARY 18, 2023

Please keep your eye on this space and look for the title “Google Research, 2022 & Beyond” for more articles in the series. With this post, I am kicking off a series in which researchers across Google will highlight some exciting progress we've made in 2022 and present our vision for 2023 and beyond. natural images).

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NYU Center for Data Science

MAY 31, 2023

His paper “ CASS: Cross Architectural Self-Supervision for Medical Image Analysis ” was a joint effort with CDS MS student Pranav Singh and previous CDS Moore Sloan Faculty Fellow and Assistant Professor Elena Sizikova , now a Staff Fellow for the Center for Devices and Radiological Health (CDRH) in the Food and Drug Administration (FDA).

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Google Research AI blog

NOVEMBER 28, 2022

Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems ( NeurIPS 2022 ), the biggest machine learning conference of the year.

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BAIR

APRIL 29, 2022

At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. However the unique ability of RL systems to leverage temporal feedback in learning complicates the types of risks and safety concerns that can arise.

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Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. In March of 2022, DeepMind released Chinchilla AI.

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Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. In March of 2022, DeepMind released Chinchilla AI.

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Explosion

MAY 17, 2023

Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find. That’s not a path to improvement.

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Heartbeat

NOVEMBER 16, 2023

The process involves supervised learning (SL) and reinforcement learning (RL) phases. Here’s a summary of the key points: The paper introduces a method called “Constitutional AI” (CAI) which aims to train a harmless AI assistant using self-improvement without human labels for harmful outputs.

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Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. And in supervised learning, it has to be labeled data. AR : Yeah.

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Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. And in supervised learning, it has to be labeled data. AR : Yeah.

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Snorkel AI

MARCH 30, 2023

Snorkel introduced Data-centric Foundation Model Development capabilities in November 2022 for enterprises to overcome these challenges and leverage foundation models in production. With the Spring 2022 release, we are making these available to all customers in beta.

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Hacker News

MAY 3, 2023

In the past months, an exquisitely human-centric approach called Reinforcement Learning from Human Feedback (RLHF) has rapidly emerged as a tour de force in the realm of AI alignment. Fine-tuning may involve further training the pre-trained model on a smaller, task-specific labeled dataset, using supervised learning.

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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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Snorkel AI

APRIL 18, 2023

Prasanna Balaprakash, research and development lead from Argonne National Laboratory gave a presentation entitled “Extracting the Impact of Climate Change from Scientific Literature using Snorkel-Enabled NLP” at Snorkel AI’s Future of Data-Centric AI Workshop in August, 2022. One way that we did is using weak supervised learning.

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Snorkel AI

APRIL 18, 2023

Prasanna Balaprakash, research and development lead from Argonne National Laboratory gave a presentation entitled “Extracting the Impact of Climate Change from Scientific Literature using Snorkel-Enabled NLP” at Snorkel AI’s Future of Data-Centric AI Workshop in August, 2022. One way that we did is using weak supervised learning.

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Snorkel AI

FEBRUARY 6, 2023

  Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. He presented at Snorkel AI’s 2022 Future of Data Centric AI (FDCAI) Conference. And that’s the power of self-supervised learning. A transcript of Koul’s talk is provided below.

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Snorkel AI

FEBRUARY 6, 2023

Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. He presented at Snorkel AI’s 2022 Future of Data Centric AI (FDCAI) Conference. And that’s the power of self-supervised learning. A transcript of Koul’s talk is provided below.

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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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Snorkel AI

JUNE 27, 2023

Chip Huyen, co-founder and CEO of Claypot AI gave a presentation entitled “Platform for Real-Time Machine Learning” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. I’m a co-founder of Claypot AI, and I assist with machine learning AI systems designed at Stanford. Hello, my name is Chip.

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Snorkel AI

JUNE 27, 2023

Chip Huyen, co-founder and CEO of Claypot AI gave a presentation entitled “Platform for Real-Time Machine Learning” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. I’m a co-founder of Claypot AI, and I assist with machine learning AI systems designed at Stanford. Hello, my name is Chip.

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Heartbeat

FEBRUARY 28, 2023

U-Net , U-Net++ ], whereas unsupervised learning eliminates this requirement [see this r eview paper ]. Semi-supervised learning lies in between supervised and unsupervised learning, which we will learn in detail in the following sections. What is Semi-supervised Learning (SSL)?

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ML @ CMU

SEPTEMBER 15, 2023

(ii) We showcase the effectiveness of SSL-based TTA approaches for scene decomposition, while previous self-supervised test-time adaptation methods have primarily demonstrated results in classification tasks. 2021) with test time adaptation using BYOL self-supervised loss of MT3 (Bartler et al.

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AssemblyAI

JULY 18, 2023

We have begun to observe diminishing returns and are already exploring other promising research directions into multimodality and self-supervised learning. " arXiv preprint arXiv:2203.15556 (2022). [2] Citations [1] Hoffmann, Jordan, et al. "Training "Training compute-optimal large language models." Panayotov, G.

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Becoming Human

MARCH 16, 2023

Unlike supervised and semi-supervised learning algorithms that can identify patterns only in structured data, DL models are capable of processing vast volumes of unstructured data and make more advanced predictions with little supervision from humans.

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