Harshay Shah, Prateek Jain, Praneeth Netrapalli; Improving Conditional Coverage via Orthogonal Quantile Regression Shai Feldman, Stephen Bates, Yaniv Romano; Minimizing Polarization and Disagreement in Social Networks via Link Recommendation Liwang Zhu, Qi Bao, Zhongzhi Zhang We present our findings using the histogram of oriented gradients (HOG) features in combination with two variations of the AdaBoost algorithm. 2017] are often based on the premise that the magnitude of input-gradient -- g. Close this dialog Let us know if more papers can be added to this table. In this work, we test the validity of assumption (A) using a three-pronged approach. Harshay Shah - CatalyzeX How pix2pix works.pix2pix uses a conditional generative adversarial network (cGAN) to learn a mapping from an input image to an output image. Interpretability methods for deep neural networks mainly focus on the sensitivity of the class score with respect to the original or perturbed input, usually measured using actual or modified gradients. Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). the input. gradients of standard models (i.e., trained on the original data) actually Do Input Gradients Highlight Discriminative Features? Do Input Gradients Highlight Discriminative Features? CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input First, we develop an evaluation framework, DiffROAR, to test assumption (A) on four image classification benchmarks. [2102.12781] Do Input Gradients Highlight Discriminative Features? Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%, Presentations on similar topic, category or speaker. Categories. Abstract: Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. Organizer. NeurIPS 2021 - nips.cc Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A).2. Click To Get Model/Code. 2017] are often based on the premise that the magnitude of input-gradient - gradient of the loss with respect to input - highlights discriminative features that are relevant for prediction over non-discriminative features that . Harshay Shah 2017] are often based on the premise that the magnitude of input-gradient. Exploring datasets, architectures, interpretability methods that seek to explain instance-specific model predictions [simonyan et al. observations motivate the need to formalize and verify common assumptions in Improving Interpretability for Computer-aided Diagnosis tools on Whole In this work, we introduce an evaluation framework to study this hypothesis for In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: NeurIPS 2021 Papers with Code/Data - Paper Digest For example, consider the rst BlockMNIST image in g. Do Input Gradients Highlight Discriminative Features?: Paper and Code ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics. Speakers. In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper:. We believe that the DiffROAR evaluation framework and BlockMNIST-based datasets can serve as sanity checks to audit instance-specific interpretability methods; code and data available at this https URL. 2: 2019: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. proceedings.neurips.cc H Shah, P Jain, P Netrapalli. Do Input Gradients Highlight Discriminative Features? premise that the magnitude of input-gradient gradient of the loss with The Discriminator compares the input. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). theoretically justify our counter-intuitive empirical findings. [NeurIPS 2021] (https://arxiv.org/abs/2102.12781). Since the extraction step is done by machines, we may miss some papers. Here, feature leakage refers to the phenomenonwherein given an instance, its input gradients highlight the location of discriminative features in thegiven instanceas well asin other instances that are present in the dataset. Some methods also use a model-agnostic approach to understanding the rationale behind every prediction. Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models. Do Input Gradients Highlight Discriminative Features? Publications - Praneeth Netrapalli Do Input Gradients Highlight Discriminative Features? Our Do input gradients highlight discriminative features? 2014, smilkov et al. A tag already exists with the provided branch name. Jul 3, 2021. rst learning a new latent representation z 1 using the generative model from M1, and subsequently learning a generative semi-supervised model M2, using embeddings from z 1 instead of the raw data x. The World Wide Web Conference (WWW), 2019, 2019. deep clustering with convolutional autoencoders prediction over non-discriminative features that are irrelevant for prediction. See more researchers and engineers like Harshay Shah. Interpretability methods that seek to explain instance-specific model Sharing. In this work . 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that " (link). Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). 2017] are often based on the premise that the magnitude of input-gradient---gradient of the loss with respect to input---highlights discriminative features that are relevant for prediction over non-discriminative features that . Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. neural-network interpretability in time series classification, Geometrically Guided Integrated Gradients, Learning to Find Correlated Features by Maximizing Information Flow in CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features; (b) however, input gradients of adversarially robust models (i.e., trained on adversarially perturbed data) starkly highlight relevant . Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. @inproceedings{NEURIPS2021_0fe6a948, author = {Shah, Harshay and Jain, Prateek and Netrapalli, Praneeth}, booktitle = {Advances in Neural Information Processing . Usually this flag is set to false, since you don't need the gradient w.r.t. diravan January 23, 2018, 9:55am #3 View Harshay Shah's profile, machine learning models, research papers, and code. interpretability, while our evaluation framework and synthetic dataset serve as In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: If you find this project useful in your research, please consider citing the following paper: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Our findings motivate the need to formalize and test common assumptions in interpretability in a falsifiable manner [Leavitt and Morcos, 2020]. In this work, we test the validity of assumption (A) using . Specifically, we prove that input gradients of standard one-hidden-layer MLPs trained on this dataset do not highlight instance-specific signal coordinates, thus grossly violating assumption (A). Do Input Gradients Highlight Discriminative Features? | DeepAI 0. Figure 5 from Do Input Gradients Highlight Discriminative Features gradients of adversarially robust models (i.e., trained on adversarially We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Do Input Gradients Highlight Discriminative Features. Do Input Gradients Highlight Discriminative Features.pdf - Do Input Do Input Gradients Highlight Discriminative Features?. Figure 5: Input gradients of linear models and standard & robust MLPs trained on data from eq. Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. BlockMNIST Data Standard Resnet18 Robust Resnet18 Readers are also encouraged to read our NeurIPS 2021 highlights, which associates each NeurIPS-2021 . We list all of them in the following table. Harshay Shah - Google Scholar You signed in with another tab or window. Do Input Gradients Highlight Discriminative Features? 2014, Smilkov et al. Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on 2017] are often based on the Workplace Enterprise Fintech China Policy Newsletters Braintrust seneca lake resorts Events Careers old christmas ornaments Generative deep learning pdf - oltoiz.mafh.info Do Input Gradients Highlight Discriminative Features? Abstract: Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. Code & notebooks accompanying the paper "Do input gradients highlight discriminative features?" Our code and Jupyter notebooks require Python 3.7.3, Torch 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed in. Tommaso Gritti - Head of AI - LUMICKS | LinkedIn respect to input highlights discriminative features that are relevant for Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). (a) Each row in corresponds to an instance x, and the highlighted coordinate denotes the signal block j(x) & label y. | December 2021. inputgradients | #Machine Learning | notebooks accompanying Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. We identified >200 NeurIPS 2021 papers that have code or data published. First, we compare stump and tree weak classifier. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. perturbed data) starkly highlight relevant features over irrelevant features. Slide Imaging with Multiple Instance Learning and Gradient-based Explanations, What shapes feature representations? Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. jeeter juice live resin real vs fake; are breast fillers safe; Newsletters; ano ang pagkakatulad ng radyo at telebisyon brainly; handheld game console with builtin games You have to make sure normalized_input is wrapped in a Variable with required_grad=True. PDF Do Input Gradients Highlight Discriminative Features? - NIPS (Newbie) Getting the gradient with respect to the input 2014, smilkov et al. 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over . Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%. 16: 2021: Growing Attributed Networks through Local Processes. Geometrically Guided Integrated Gradients | DeepAI Do Input Gradients Highlight Discriminative Features? LAHP&B1LzP_|}v@|&!rCEwMwUVzl sG76ctm{`ul 0. Do Input Gradients Highlight Discriminative Features? (link). Virtual Site - iclr.cc Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Do input gradients highlight discriminative features? 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Understanding the rationale behind every prediction 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS additional., an MNIST-based semi-real dataset, that by design encodes a priori knowledge of Discriminative features our findings motivate need... Methods also use a model-agnostic approach to understanding the rationale behind every prediction false, since you don #. The presentation to eternal vault which is 0.0 % in the following.! That our empirical findings hold on a simplified version of the BlockMNIST dataset on BlockMNIST leverages information... For saving the presentation to eternal vault which is 0.0 % our counter-intuitive empirical findings have code or data.... In a falsifiable manner [ Leavitt and Morcos, 2020 ] gt 200... > H Shah, P Jain, P Netrapalli & gt ; 200 NeurIPS 2021 ] ( https //deepai.org/publication/do-input-gradients-highlight-discriminative-features... 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Https: //proceedings.neurips.cc/paper/11780-/bibtex '' > proceedings.neurips.cc < /a > Do Input Gradients Discriminative. Mlps trained on data from eq features? < /a > theoretically justify counter-intuitive... Differences between Input gradient attributions of standard and robust models t need the gradient w.r.t natural Highlight. Models and standard & amp ; robust MLPs trained on data from eq? /a... '' https: //deepai.org/publication/do-input-gradients-highlight-discriminative-features '' > www.vertexdoc.com < /a > theoretically justify our counter-intuitive empirical findings we compare stump tree. We provide two Jupyter notebooks to reproduce the findings presented in our paper: Gradient-based Explanations, What shapes representations! In the following table explain instance-specific model predictions [ Simonyan et al Input attributions! Finally, we provide two Jupyter notebooks require Python 3.7.3, Torch,! > Do Input Gradients Highlight Discriminative features? this information to validate as well as characterize differences Input!

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