Add to ORKGThe lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using in- tuitive toy examples as well as medical tasks for treating sep- sis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than sim- pler L1 or L2 penalties without sacrificing predictive power
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
This work is supported by the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) whic...
Data mining and machine learning (ML) are increasingly at the core of many aspects of modern life. W...
The lack of interpretability remains a key barrier to the adoption of deep models in many applicatio...
The lack of interpretability remains a barrier to adopting deep neural networks across many safety-c...
In classification and forecasting with tabular data, one often utilizes tree-based models. Those can...
One obstacle that so far prevents the introduction of machine learning models primarily in critical ...
Deep Learning based models are currently dominating most state-of-the-art solutions for disease pred...
Deep neural networks have achieved near-human accuracy levels in various types of classification and...
10 pages, 22 figures, submitted to ICLR 2023A wide variety of model explanation approaches have been...
There have been many recent advances in machine learning, resulting in models which have had major i...
We look at a specific aspect of model interpretability: models often need to be constrained in size ...
Growing amount of high dimensional data requires robust analysis techniques. Tree-based ensemble met...
Thesis (MCom)--Stellenbosch University, 2019.ENGLISH ABSTRACT: As deep learning methods are becoming...
In machine learning often a tradeoff must be made between accuracy and intelligibility. More accurat...
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
This work is supported by the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) whic...
Data mining and machine learning (ML) are increasingly at the core of many aspects of modern life. W...
The lack of interpretability remains a key barrier to the adoption of deep models in many applicatio...
The lack of interpretability remains a barrier to adopting deep neural networks across many safety-c...
In classification and forecasting with tabular data, one often utilizes tree-based models. Those can...
One obstacle that so far prevents the introduction of machine learning models primarily in critical ...
Deep Learning based models are currently dominating most state-of-the-art solutions for disease pred...
Deep neural networks have achieved near-human accuracy levels in various types of classification and...
10 pages, 22 figures, submitted to ICLR 2023A wide variety of model explanation approaches have been...
There have been many recent advances in machine learning, resulting in models which have had major i...
We look at a specific aspect of model interpretability: models often need to be constrained in size ...
Growing amount of high dimensional data requires robust analysis techniques. Tree-based ensemble met...
Thesis (MCom)--Stellenbosch University, 2019.ENGLISH ABSTRACT: As deep learning methods are becoming...
In machine learning often a tradeoff must be made between accuracy and intelligibility. More accurat...
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
This work is supported by the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) whic...
Data mining and machine learning (ML) are increasingly at the core of many aspects of modern life. W...