Understanding Impacts of High-Order Loss Approximations and Features in
Deep Learning Interpretation

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

1 February 2019

Papers citing "Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation"

12 / 12 papers shown

Title
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction Farhad Pourkamali-Anaraki 43 0 0 24 Apr 2025
Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapes Nikita Kiselev Andrey Grabovoy 54 1 0 18 Sep 2024
On Dissipativity of Cross-Entropy Loss in Training ResNets Jens Püttschneider T. Faulwasser 35 0 0 29 May 2024
Data-Centric Debugging: mitigating model failures via targeted data collection Sahil Singla Atoosa Malemir Chegini Mazda Moayeri Soheil Feiz 27 4 0 17 Nov 2022
A Survey of Neural Trees Haoling Li Mingli Song Mengqi Xue Haofei Zhang Jingwen Ye Lechao Cheng Mingli Song AI4CE 20 6 0 07 Sep 2022
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition Mengqi Xue Qihan Huang Haofei Zhang Lechao Cheng Mingli Song Ming-hui Wu Mingli Song ViT 33 52 0 22 Aug 2022
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo S. Feizi FAtt 20 80 0 29 Nov 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 24 6 0 19 Apr 2021
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks Apoorva Sharma Navid Azizan Marco Pavone UQCV 25 45 0 24 Feb 2021
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection Michael Tsang Dehua Cheng Hanpeng Liu Xuening Feng Eric Zhou Yan Liu FAtt 24 60 0 19 Jun 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020