Test Sample Accuracy Scales with Training Sample Density in Neural Networks
暂无分享,去创建一个
[1] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[2] Dustin Tran,et al. Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness , 2020, NeurIPS.
[3] Yedid Hoshen,et al. Classification-Based Anomaly Detection for General Data , 2020, ICLR.
[4] Yoshua Bengio,et al. Inductive Biases for Deep Learning of Higher-Level Cognition , 2020, ArXiv.
[5] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.
[6] Surya Ganguli,et al. On the Expressive Power of Deep Neural Networks , 2016, ICML.
[7] Razvan Pascanu,et al. On the Number of Linear Regions of Deep Neural Networks , 2014, NIPS.
[8] C. K. Chow,et al. An optimum character recognition system using decision functions , 1957, IRE Trans. Electron. Comput..
[9] Prateek Mittal,et al. SSD: A Unified Framework for Self-Supervised Outlier Detection , 2021, ICLR.
[10] Kevin Gimpel,et al. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.
[11] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[12] Sebastian Nowozin,et al. Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift , 2019, NeurIPS.
[13] David Rolnick,et al. Deep ReLU Networks Have Surprisingly Few Activation Patterns , 2019, NeurIPS.
[14] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[15] Yoshua Bengio,et al. Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization , 2021, ArXiv.
[16] Ran El-Yaniv,et al. Selective Classification for Deep Neural Networks , 2017, NIPS.
[17] Jinwoo Shin,et al. CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances , 2020, NeurIPS.
[18] Kibok Lee,et al. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks , 2018, NeurIPS.
[19] David Duvenaud,et al. Residual Flows for Invertible Generative Modeling , 2019, NeurIPS.
[20] H. Akaike. A new look at the statistical model identification , 1974 .
[21] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[22] Maya R. Gupta,et al. To Trust Or Not To Trust A Classifier , 2018, NeurIPS.
[23] Naftali Tishby,et al. The information bottleneck method , 2000, ArXiv.
[24] Ang Li,et al. Hybrid Models for Open Set Recognition , 2020, ECCV.
[25] Mario Vento,et al. A method for improving classification reliability of multilayer perceptrons , 1995, IEEE Trans. Neural Networks.
[26] Stefan Wrobel,et al. Active Hidden Markov Models for Information Extraction , 2001, IDA.
[27] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[28] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[29] Xu Ji,et al. Invariant Information Clustering for Unsupervised Image Classification and Segmentation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).