DeepAI AI Chat
Log In Sign Up

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach

06/10/2019
by   Gyeong-In Yu, et al.
Microsoft
Seoul National University
0

Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained using backpropagation. We argue that the isolated training scheme of ML pipelines is sub-optimal, since it cannot jointly optimize multiple components. To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation. Our experiments show that fine-tuning of the translated pipelines is a promising technique able to increase the final accuracy.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/04/2022

DiffML: End-to-end Differentiable ML Pipelines

In this paper, we present our vision of differentiable ML pipelines call...
07/17/2021

Towards autonomic orchestration of machine learning pipelines in future networks

Machine learning (ML) techniques are being increasingly used in mobile n...
08/16/2022

Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices

The focus of this paper is a proof of concept, machine learning (ML) pip...
09/23/2019

Machine Learning Pipelines with Modern Big DataTools for High Energy Physics

The effective utilization at scale of complex machine learning (ML) tech...
07/27/2022

Learning with Combinatorial Optimization Layers: a Probabilistic Approach

Combinatorial optimization (CO) layers in machine learning (ML) pipeline...
02/28/2020

End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines

As machine learning (ML) being applied to many mission-critical scenario...
09/23/2019

Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics

The effective utilization at scale of complex machine learning (ML) tech...