Data processing

by Synced 2022-02-28 0

Princeton U’s DataMUX Enables DNNs to Simultaneously and Accurately Process up to 40 Input Instances With Limited Computational Overhead

In the new paper DataMUX: Data Multiplexing for Neural Networks, a Princeton University research team proposes Data Multiplexing (DataMUX). The novel technique enables neural networks to process multiple inputs simultaneously and generate accurate predictions, increasing model throughput with minimal additional memory requirements.

by Synced 2021-06-25 3

AI Machine Learning & Data Science Research

Google Research’s Prediction Depth: Understanding the Laws that Govern DL Data Processing

A team from Google Research proposes prediction depth, a new measure of example difficulty determined from hidden embeddings. Their study reveals the surprising fact that the prediction depth of a given input has strong connections to a model’s uncertainty, confidence, accuracy and speed of learning for that data point.