Shreyansh Singh

MARCH 26, 2023

There are many approximate attention methods out there like Reformer, Smyrf, Reformer, Performer and others ( you can find more details on a few of these in my previous blog ) which aim to reduce the compute requirements to linear or near-linear in sequence length, but many of them do not display wall-clock speedup against standard attention.

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The Stanford AI Lab Blog

DECEMBER 6, 2021

We’re excited to share all the work from SAIL that’s being presented at the main conference , at the Datasets and Benchmarks track and the various workshops , and you’ll find links to papers, videos and blogs below.

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AWS Machine Learning Blog

AUGUST 21, 2023

Background One specific application of ML algorithms in the medical domain, which uses large volumes of text, is clinical decision support systems (CDSSs) for triage. Predictions of these are now highly achievable from admission notes alone, through the use of natural language processing (NLP) algorithms [1].

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The MLOps Blog

FEBRUARY 22, 2024

Every input example needs a task label that helps identify your expected output. For instance, outputs in classification and text summarization problems are different, so based on the task label, you can decide if the current example trains classification or extraction.

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ML Review

MARCH 31, 2019

Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing. The reason it works is that unlike the first model, the second model is trained on the primary model’s “soft” probability outputs, rather than the “hard” (0/1) true labels from the real training data.

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Google Research AI blog

JULY 23, 2023

Posted by Cat Armato, Program Manager, Google Groups across Google actively pursue research in the field of machine learning (ML), ranging from theory and application. We build ML systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more.

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Heartbeat

FEBRUARY 28, 2023

Finally, we will look at some of the recent semi-supervised medical image segmentation algorithms. SSL is a machine learning paradigm that combines a very small amount of labeled data along with a large amount of unlabelled data for training. Let’s dive in! What is Semi-supervised Learning (SSL)?

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The Stanford AI Lab Blog

MARCH 28, 2022

[Summary] tl;dr: A tremendous amount of effort has been poured into training AI algorithms to competitively play games that computers have traditionally had trouble with, such as the retro games published by Atari, Go, DotA, and StarCraft II. Can the same algorithms that master Atari games help us grade these game assignments?

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Snorkel AI

FEBRUARY 17, 2023

First I’ll chat a bit about millions of label errors and the 10 most common machine learning benchmark data sets. This is built on a theory that we developed at MIT called Confident Learning , which is a subfield of machine learning for learning with noisy labels, finding errors in data, and estimating uncertainty in data.

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Snorkel AI

FEBRUARY 17, 2023

First I’ll chat a bit about millions of label errors and the 10 most common machine learning benchmark data sets. This is built on a theory that we developed at MIT called Confident Learning , which is a subfield of machine learning for learning with noisy labels, finding errors in data, and estimating uncertainty in data.

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Explosion

AUGUST 21, 2016

While researchers rightly focus on better algorithms, there are a lot more things to be done. Data Collection and Training Contrary to popular belief, the bottleneck in AI is data , not algorithms. This suggests a tempting theory: annotation time should be dirt cheap, right? What’s holding back Artificial Intelligence?

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Mlearning.ai

FEBRUARY 25, 2023

How does the Artificial Neural Network algorithm work? ANN approach is a machine learning algorithm inspired by biological neural networks. While classical machine learning algorithms fell short of analyzing big data, artificial neural networks performed well on big data. This is where the backpropagation algorithm comes in.

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PyImageSearch

OCTOBER 2, 2023

Figure 2: Variational Autoencoder Architecture Diagram (source: image created by the author for the LearnOpenCV Blog ). Figure 3: The reparameterization trick transforms a stochastic node into a deterministic one, facilitating gradient flow (source: image designed by the author for the LearnOpenCV Blog ). The config.py

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Explosion

APRIL 1, 2017

Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. Most AI systems today rely on supervised learning : you provide labelled input and output pairs, and get a program that can perform analogous computation for new data. Try Prodigy!

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ML @ CMU

MAY 12, 2023

In short, this says that the (k)-th data silo may set its own ((varepsilon_k, delta_k)) example-level DP target for any learning algorithm with respect to its local dataset. Of course, the problem lies in what that feature vector (and the corresponding label) is—we’ll get to this in the following section.

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Google Research AI blog

NOVEMBER 28, 2022

Organizing Committee General Chairs includes: Sanmi Koyejo Program Chairs include: Alekh Agarwal Workshop Chairs include: Hanie Sedghi Tutorial Chairs include: Adji Bousso Dieng , Jessica Schrouff Affinity Workshop Chair: Adji Bousso Dieng , Jessica Schrouff Program Committee, Senior Area Chairs include: Corinna Cortes , Claudio Gentile , Mohammad (..)

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Explosion

NOVEMBER 21, 2019

al, 2015) is a twist on the word2vec family of algorithms that lets you learn more interesting word vectors. sense2vec reloaded: the updated library sense2vec is a Python package to load and query vectors of words and multi-word phrases based on part-of-speech tags and entity labels. It was a nice idea in theory.

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Mlearning.ai

JUNE 5, 2023

A Guide to Computer Vision Tools Hello and welcome to my blog on computer vision tools! In this blog, I will introduce you to some of the most popular and powerful computer vision tools that you can use to unleash your creativity and have fun. Learn the basics of computer vision theory. Let’s get started!

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ML Review

MARCH 5, 2019

Word embeddings Visualisation of word embeddings in AI Distillery Word2vec is a popular algorithm used to generate word representations (aka embeddings) for words in a vector space. Then, the algorithm proceeds with the following word as the new centre word, i.e. “learning”, sets up the new context, and repeats the same procedure.

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Explosion

DECEMBER 17, 2013

I wrote this blog post in 2013, describing an exciting advance in natural language understanding technology. Today, almost all high-performance parsers are using a variant of the algorithm described below (including spaCy). We could, in theory, have written our guidelines so that the “correct” parses were reversed.

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The MLOps Blog

DECEMBER 7, 2022

Given that the whole theory of machine learning assumes today will behave at least somewhat like yesterday, what can algorithms and models do for you in such a chaotic context ? And that includes data. Next comes a stage of hyperparameter tuning for the models. Then comes a very exhaustive evaluation stage.

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AWS Machine Learning Blog

JULY 13, 2023

Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers. Train a binary classification model using the SageMaker built-in XGBoost algorithm.

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