Conditional probability, Learning, Probability theory, Scientific method, Neural network, Bayesian probability

Creating an encyclopedia from GPT-3 using B̶a̶y̶e̶s̶’̶ ̶R̶u̶l̶e̶ Gibbs sampling

On Sep 22, 2020
@kchonyc shared
is meta-learning just noise in gibbs sampling? a fun thought on GPT-3 as a meta-learner vs. gibbs sampling using GPT-3 and GPT-E in a hypothetical parallel universe https://t.co/Ka3oDbVh7H
Open

Many aspects of OpenAI’s GPT-3 have fascinated and continue to fascinate people, including myself. this makes GPT-3 subsume a so-called sequence-to-sequence or encoder-decoder model, allowing one to use GPT-3 to find an answer given a question (often referred to as “prompt” which comes ...

kyunghyuncho.me
On Sep 22, 2020
@kchonyc shared
is meta-learning just noise in gibbs sampling? a fun thought on GPT-3 as a meta-learner vs. gibbs sampling using GPT-3 and GPT-E in a hypothetical parallel universe https://t.co/Ka3oDbVh7H
Open

Creating an encyclopedia from GPT-3 using B̶a̶y̶e̶s̶’̶ ̶R̶u̶l̶e̶ Gibbs sampling

Creating an encyclopedia from GPT-3 using B̶a̶y̶e̶s̶’̶ ̶R̶u̶l̶e̶ Gibbs sampling

Many aspects of OpenAI’s GPT-3 have fascinated and continue to fascinate people, including myself. this makes GPT-3 subsume a so-called sequence-to-sequence or encoder-decoder model, ...

Data Scientist Interviews Demystified

Data Scientist Interviews Demystified

Tech companies and production teams may focus more on software skills, while larger businesses outside of tech may focus more on the statistical testing and data mining skills. Especially ...

What is GPT-3? Everything your business needs to know about OpenAI’s breakthrough AI language program

What is GPT-3? Everything your business needs to know about OpenAI’s breakthrough AI language program

A software program that ingests gigabytes of text can automatically generate whole paragraphs so natural they sound like a person wrote them. OpenAI’s GPT-3 is all the rage. What is it, ...

dbm.dvi

dbm.dvi

How to Pretrain Deep Boltzmann Machines in Two Stages Kyunghyun Cho, Tapani Raiko, Alexander Ilin, and Juha Karhunen Abstract A deep Boltzmann machine (DBM) is a recently introduced Markov ...

Curriculum for Reinforcement Learning

Curriculum for Reinforcement Learning

A curriculum is an efficient tool for humans to progressively learn from simple concepts to hard problems. It breaks down complex knowledge by providing a sequence of learning steps of ...

Sparse Gaussian Process Approximations and Applications

Sparse Gaussian Process Approximations and Applications

EP Expectation Propagation FIF Frequency inducing feature FITC Fully Independent Training Conditional xxii Nomenclature GP Gaussian process GPLVM Gaussian Process Latent Variable Model ...

Where did the least-square come from?

Where did the least-square come from?

What would you say in a machine learning interview, if asked about the mathematical basis of the least-square loss function?

Apple at NeurIPS 2019

Apple at NeurIPS 2019

Apple Machine Learning Journal publishes posts written by Apple engineers about their work using machine learning technologies to help build innovative products for millions of people ...

GPT-2 and the Nature of Intelligence

GPT-2 and the Nature of Intelligence

Anything that looks like genuine understanding is just an illusion.

Click here to read the article

Click here to read the article

The approach trains a “setter” model to generate goals for a “solver” agent by optimizing three setter ob- jectives: discovering the subset of expressible goals that are valid, or ...