暂无分享,去创建一个
Rongrong Ji | Ling Shao | Feiyue Huang | Tong Tong | Jie Hu | Ke Li | Qixiang Ye | ShengChuan Zhang
[1] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[2] Yoshua Bengio,et al. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation , 2013, ArXiv.
[3] Bolei Zhou,et al. Revisiting the Importance of Individual Units in CNNs via Ablation , 2018, ArXiv.
[4] Ling Shao,et al. Interpretable Neural Network Decoupling , 2020, ECCV.
[5] Martin Wattenberg,et al. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) , 2017, ICML.
[6] Joelle Pineau,et al. Conditional Computation in Neural Networks for faster models , 2015, ArXiv.
[7] Jason Yosinski,et al. Understanding Neural Networks via Feature Visualization: A survey , 2019, Explainable AI.
[8] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[9] Quanshi Zhang,et al. Interpretable Convolutional Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[10] Qi Tian,et al. Information Competing Process for Learning Diversified Representations , 2019, NeurIPS.
[11] Christiane Fellbaum,et al. Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.
[12] Bolei Zhou,et al. Network Dissection: Quantifying Interpretability of Deep Visual Representations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Bolei Zhou,et al. GAN Dissection: Visualizing and Understanding Generative Adversarial Networks , 2018, ICLR.
[14] Jaime S. Cardoso,et al. Machine Learning Interpretability: A Survey on Methods and Metrics , 2019, Electronics.
[15] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[16] Ling Shao,et al. Dynamic Neural Network Decoupling , 2019, ArXiv.
[17] Pascal Vincent,et al. Visualizing Higher-Layer Features of a Deep Network , 2009 .
[18] Xiaolin Hu,et al. Interpret Neural Networks by Identifying Critical Data Routing Paths , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[19] Venkatesh Saligrama,et al. Adaptive Neural Networks for Efficient Inference , 2017, ICML.
[20] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[22] Matthew Botvinick,et al. On the importance of single directions for generalization , 2018, ICLR.
[23] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[25] Bolei Zhou,et al. Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Naftali Tishby,et al. The information bottleneck method , 2000, ArXiv.
[27] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.