Plotting

Inductive Learning


Self-Supervised Learning And Its Applications - AI Summary

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The focus was largely on supervised learning methods that require huge amounts of labeled data to train systems for specific use cases. Bidirectional Encoder Representations from Transformers (BERT) a paper published by researchers at the Google AI team has become a gold standard when it comes to several NLP tasks such as Natural Language Inference (MNLI), Question Answering (SQuAD), and more. To make BERT handle a variety of downstream tasks, input representation is able to unambiguously represent a pair of sentences that are packed together in a single sequence. While autoencoding models like BERT utilize self-supervised learning for tasks like sentence classification (next or not), another application of self-supervised approaches lies in the domain of text generation. The inputs are passed through our pre-trained model to obtain the final transformer block's activation hm l, which is then fed into an added linear output layer with parameters W y to predict y: Translation Language Modelling (TLM): a new addition and an extension of MLM, where instead of considering monolingual text streams, parallel sentences are concatenated as illustrated in the following image.


GitHub - jason718/awesome-self-supervised-learning: A curated list of awesome self-supervised methods

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Self-Supervised Learning has become an exciting direction in AI community. Predicting What You Already Know Helps: Provable Self-Supervised Learning. For self-supervised learning, Rationality implies generalization, provably. Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? FAIR Self-Supervision Benchmark [pdf] [repo]: various benchmark (and legacy) tasks for evaluating quality of visual representations learned by various self-supervision approaches.


Self-Supervised Learning and Its Applications - neptune.ai

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In the past decade, the research and development in AI have skyrocketed, especially after the results of the ImageNet competition in 2012. The focus was largely on supervised learning methods that require huge amounts of labeled data to train systems for specific use cases. In this article, we will explore Self Supervised Learning (SSL) – a hot research topic in a machine learning community. Self-supervised learning (SSL) is an evolving machine learning technique poised to solve the challenges posed by the over-dependence of labeled data. For many years, building intelligent systems using machine learning methods has been largely dependent on good quality labeled data. Consequently, the cost of high-quality annotated data is a major bottleneck in the overall training process.


A Topological Approach for Semi-Supervised Learning

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Nowadays, Machine Learning and Deep Learning methods have become the state-of-the-art approach to solve data classification tasks. In order to use those methods, it is necessary to acquire and label a considerable amount of data; however, this is not straightforward in some fields, since data annotation is time consuming and might require expert knowledge. This challenge can be tackled by means of semi-supervised learning methods that take advantage of both labelled and unlabelled data. In this work, we present new semi-supervised learning methods based on techniques from Topological Data Analysis (TDA), a field that is gaining importance for analysing large amounts of data with high variety and dimensionality. In particular, we have created two semi-supervised learning methods following two different topological approaches.


Los Angeles average gas price leads the nation at a record-breaking $6.08

Los Angeles Times

On Wednesday the average cost for a gallon of regular gas in Los Angeles reached $6.08, leaping 2.3 cents overnight and breaking a record set earlier this year, according to the latest data from AAA. Los Angeles is not alone in its pain as the cost of gas spikes across the nation. And according to analysts, the switch to a more expensive summer blend for other parts of the country promises the hurt will not stop anytime soon. The average cost for regular gas is more than $4 for nearly every state. According to AAA, the national average is $4.56, but California leads the nation with an average of $6.05.


No gas rebates in sight as average prices in L.A. barrel toward $6 a gallon -- again

Los Angeles Times

Experts say a perfect storm of supply-and-demand issues are sending gas prices in Los Angeles soaring again, with the price-per-gallon increasing more than 14 cents in the last 16 days, according to the latest fuel prices tracked by AAA. L.A. fuel prices are again inching toward a $6-a-gallon record set in March. The average price of a gallon of regular gasoline in the Los Angeles area is currently $5.91, with plenty of stations charging well over that. A year ago the price was $4.16. Overnight, the price jumped 2.2 cents, the highest level it has risen since February.


Self-Supervised Learning - The New AI Frontier

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AI has classically come in three forms, supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is where AI is given many example scenarios and the right answer for each one (such as images labeled as Cat or Dog). Unsupervised learning has been traditionally where AI learns to group items together by similarity (clustering), without explicit labels. Reinforcement learning is where AIs try out strategies (such as in a game) and attempt to optimize a reward function (such as points in the game). Many commercial AIs are based on supervised learning.


Director, Data Engineering

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Collectors Universe has multiple business lines that grade, authenticate, and sell millions of high-value, record-setting collectibles every quarter. We're the leader in third-party authentication and grading services for high-value collectibles including trading cards (Professional Sports Authenticator), coins (Professional Coin Grading Services), video games (Wata), event tickets, autographs, and memorabilia, and with your help we can continue to grow rapidly. Our goal is to make the joy of collecting accessible to everyone -- collectors looking to complete their set, inventors looking to maximize the value of their collection, and anyone who's looking to preserve a game, card or coin that reminds them of fond memories in their lives. We're looking for analytics engineers who can support us in creating the next generation of engaging products for collectors, scalable, intuitive software for our internal customers, and innovative, best in class solutions to bring delight to The Hobby. What will you help us build?


Graph Machine Learning with Python Part 4: Supervised & Semi-Supervised Learning

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This story will explore how we can reason from and model graphs using labels via Supervised and Semi-Supervised Learning. I'm going to be using a MET Art Collections dataset that will build on my previous parts on Metrics, Unsupervised Learning, and more. Be sure to check out the previous story before this one to keep up on some of the pieces as I won't cover all concepts again in this one: The easiest approach to conduct Supervised Learning is to use graph measures as features in a new dataset or in addition to an existing dataset. I have seen this method yield positive results for modeling tasks, but it can be really dependent on 1. how you model as a graph (what are the inputs, outputs, edges, etc.) and 2. which metrics to use. Depending on the prediction task, we could compute node-level, edge-level, and graph-level metrics.


Congratulations to the 2022 ICLR outstanding paper award winners!

AIHub

The winners of the 2022 International Conference on Learning Representations (ICLR) outstanding paper awards have been announced. There are seven outstanding paper winners and three honourable mentions. The award winners will be presenting their work at the conference, which is taking place virtually, this week. Analytic-DPM: an analytic estimate of the optimal reverse variance in diffusion probabilistic models Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang Abstract: Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate over thousands of timesteps.