Learning What Makes a Difference from Counterfactual Examples and
Gradient Supervision

Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision

20 April 2020

Ehsan Abbasnejad

Papers citing "Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision"

19 / 19 papers shown

Title
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning F. Mumuni A. Mumuni AAML 27 5 0 11 Mar 2024
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition Chong Wang Yuanhong Chen Fengbei Liu Yuyuan Liu Davis J. McCarthy Helen Frazer Gustavo Carneiro 16 1 0 30 Nov 2023
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals Yanai Elazar Bhargavi Paranjape Hao Peng Sarah Wiegreffe Khyathi Raghavi Vivek Srikumar Sameer Singh Noah A. Smith AAML OOD 21 0 0 16 Nov 2023
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases Yanchen Liu Jing Yang Yan Chen Jing Liu Huaqin Wu MoE 32 2 0 28 Feb 2023
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers Wanqian Yang Polina Kirichenko Micah Goldblum A. Wilson DRL 4 10 0 28 Nov 2022
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals Maarten De Raedt Fréderic Godin Chris Develder Thomas Demeester 6 1 0 21 Oct 2022
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning Damien Teney Maxime Peyrard Ehsan Abbasnejad 19 29 0 06 Jul 2022
Fact Checking with Insufficient Evidence Pepa Atanasova J. Simonsen Christina Lioma Isabelle Augenstein 22 14 0 05 Apr 2022
Language bias in Visual Question Answering: A Survey and Taxonomy Desen Yuan 16 12 0 16 Nov 2021
Counterfactual Adversarial Learning with Representation Interpolation Wen Wang Boxin Wang Ning Shi Jinfeng Li Bingyu Zhu Xiangyu Liu Rongxin Zhang AAML OOD CML 14 2 0 10 Sep 2021
An Investigation of the (In)effectiveness of Counterfactually Augmented Data Nitish Joshi He He OODD 19 46 0 01 Jul 2021
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests Victor Veitch Alexander DÁmour Steve Yadlowsky Jacob Eisenstein OOD 9 91 0 31 May 2021
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization Damien Teney Ehsan Abbasnejad Simon Lucey A. Hengel 20 86 0 12 May 2021
Causal Learning for Socially Responsible AI Lu Cheng Ahmadreza Mosallanezhad Paras Sheth Huan Liu 63 13 0 25 Apr 2021
Counterfactual Generative Networks Axel Sauer Andreas Geiger OOD BDL CML 28 122 0 15 Jan 2021
CBNet: A Novel Composite Backbone Network Architecture for Object Detection Yudong Liu Yongtao Wang Siwei Wang Tingting Liang Qijie Zhao Zhi Tang Haibin Ling ObjD 207 244 0 09 Sep 2019
e-SNLI: Natural Language Inference with Natural Language Explanations Oana-Maria Camburu Tim Rocktaschel Thomas Lukasiewicz Phil Blunsom LRM 252 620 0 04 Dec 2018
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 185 711 0 17 Apr 2018
Learning Robust Representations of Text Yitong Li Trevor Cohn Timothy Baldwin OOD 147 15 0 20 Sep 2016