RESEARCH

COMPUTER VISION

Neural Basis Models for Interpretability

November 06, 2022

Abstract

Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations. Generalized Additive Models (GAMs) are an inherently interpretable class of models that address this limitation by learning a non-linear shape function for each feature separately, followed by a linear model on top. However, these models are typically difficult to train, require numerous parameters, and are difficult to scale. We propose an entirely new subfamily of GAMs that utilizes basis decomposition of shape functions. A small number of basis functions are shared among all features, and are learned jointly for a given task, thus making our model scale much better to large-scale data with high-dimensional features, especially when features are sparse. We propose an architecture denoted as the Neural Basis Model (NBM) which uses a single neural network to learn these bases. On a variety of tabular and image datasets, we demonstrate that for interpretable machine learning, NBMs are the state-of-the-art in accuracy, model size, and, throughput and can easily model all higher-order feature interactions. Source code is available at \href{https://github.com/facebookresearch/nbm-spam}{\ttfamily github.com/facebookresearch/nbm-spam}.

Download the Paper

AUTHORS

Written by

Filip Radenovic

Abhimanyu Dubey

Dhruv Mahajan

Publisher

NeurIPS

Research Topics

Computer Vision

Core Machine Learning

Related Publications

March 09, 2023

COMPUTER VISION

The Casual Conversations v2 Dataset

Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas

March 09, 2023

February 21, 2023

COMPUTER VISION

CORE MACHINE LEARNING

ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks

Felix Xu, Fuyuan Zhang, Hua Qi, Jianjun Zhao, Jianlang Chen, Lei Ma, Qing Guo, Zhijie Wang

February 21, 2023

January 10, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Online Backfilling with No Regret for Large-Scale Image Retrieval

Gokhan Uzunbas, Joena Zhang, Sara Cao, Ser-Nam Lim, Taipeng Tian, Bohyung Han, Seonguk Seo

January 10, 2023

January 04, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

Xi Liu, Panganamala Kumar, Ruida Zhou, Tao Liu

January 04, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.