Human-Centered Artificial Intelligence and Machine Learning

Visual interpretability for deep learning: a survey

Original

Quanshi Zhang and Song-Chun Zhu · 1901

Earlier work this paper cites.

Cyc: A large-scale investment in knowledge infrastructure

Doug Lenat · 1995

Earlier work this paper cites.

Conceptnet–a practical commonsense reasoning tool-kit

Hugo Liu and Push Singh · 2004

Earlier work this paper cites.

VisKE: Visual knowledge extraction and question answering by visual verification of relation phrases

Fereshteh Sadeghi, Santosh Kumar Divvala, and Ali Farhadi · 2015

Earlier work this paper cites.

Learning common sense through visual abstraction

Ramakrishna Vedantam, Xiao Lin, Tanmay Batra, C. Lawrence Zitnick, and Devi Parikh · 2015

Cited alongside, same era.

Towards learning from stories: An approach for interactive machine learning

Brent Harrison and Mark Riedl · 2016

Cited alongside, same era.

Model-agnostic interpretability of machine learning

Original

Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016

Cited alongside, same era.

Translating neuralese

Jacob Andreas, Anca D. Dragan, and Dan Klein · 2017

Cited alongside, same era.

Reinforcement learning with a corrupted reward channel

Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, and Shane Legg · 2017

Later among the works it cites.

Grad-cam: Visual explanations from deep networks via gradient-based localization

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017

Later among the works it cites.

A simple method for commonsense reasoning

Original

Trieu H. Trinh and Quoc V. Le · 2018

Later among the works it cites.

Automated rationale generation: a technique for explainable AI and its effects on human perceptions

Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, and Mark O. Riedl · 2019

Closest in time.