December 15, 2024

Cutting-Edge Techniques That Speed Up AI Without Extra Costs | HackerNoon

Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional

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How Selection Mechanisms Transform State Space Models | HackerNoon

Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional

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Why Compressing Information Helps AI Work Better | HackerNoon

Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional

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How State Space Models Improve AI Sequence Modeling Efficiency | HackerNoon

Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional

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Princeton and CMU Push AI Boundaries with the Mamba Sequence Model | HackerNoon

Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional

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AI

Bayes’ Theorem: Understanding business outcomes with evidence

A practical introduction to Bayes’ Theorem: Probability for Data Science Series (2) Sunghyun Ahn · Follow Published in Towards Data Science · 10 min read · 3 days ago — Photo by Markus Spiske on Unsplash If you are not a paid member on Medium, I make my stories available for free: Friends link Bayes’ Theorem is one of the most widely used and celebrated concepts in statistics. It sets the basis of a probability

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Robotics

Vision-guided cobot automates paint process for DENSO – The Robot Report

Invent Automation has integrated CapSen vision technology as DENSO automates. Source: Invent Automation Automotive manufacturing has long benefited from the thoughtful deployment of robotics and automation, and for good reason. Among the many complex production processes, some can be too difficult or too tedious to be completed safely and efficiently by human workers. As a result, industrial automation technologies can add tremendous value to original equipment manufacturers and automotive parts suppliers alike in many ways.

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AI

Credit Card Fraud Detection with Different Sampling Techniques

How to deal with imbalanced data Mythili Krishnan · Follow Published in Towards Data Science · 10 min read · 11 hours ago — Photo by Bermix Studio on Unsplash Credit card fraud detection is a plague that all financial institutions are at risk with. In general fraud detection is very challenging because fraudsters are coming up with new and innovative ways of detecting fraud, so it is difficult to find a pattern that we

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AI

API Design of X (Twitter) Home Timeline

A closer look at X’s API: fetching data, linking entities, and solving under-fetching. Oleksii Trekhleb · Follow Published in Towards Data Science · 17 min read · 3 days ago — When designing a system’s API, software engineers often evaluate various approaches, such as REST vs RPC vs GraphQL, or hybrid models, to determine the best fit for a specific task or project. These approaches define how data flows between the backend and frontend, as

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AI

Data Valuation — A Concise Overview

Understanding the Value of your Data: Challenges, Methods, and Applications Tim Wibiral · Follow Published in Towards Data Science · 7 min read · 3 days ago — ChatGPT and similar LLMs were trained on insane amounts of data. OpenAI and Co. scraped the internet, collecting books, articles, and social media posts to train their models. It’s easy to imagine that some of the texts (like scientific or news articles) were more important than others

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