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Machine Learning Models: Explaining their Decisions, and Validating the Explanations

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Gregoire Montavon (TU Berlin) at the International Workshop Machine Learning and Artificial intelligence at Télécom ParisTech

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Machine Learning Models: Explaining their Decisions, and Validating the Explanations

  1. 1. 1/45 Explaining Machine Learning Decisions Grégoire Montavon, TU Berlin Joint work with: Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin, Alexander Binder 18/09/2018 Intl. Workshop ML & AI, Telecom ParisTech
  2. 2. 2/45 From ML Successes to Applications Autonomous Driving Medical Diagnosis Networks (smart grids, etc.) Visual Reasoning AlphaGo beats Go human champ Deep Net outperforms humans in image classification
  3. 3. 3/45 Can we interpret what a ML model has learned?
  4. 4. 4/45 First, we need to define what we want from interpretable ML.
  5. 5. 5/45 Understanding Deep Nets: Two Views Understanding what mechanism the network uses to solve a problem or implement a function. Understanding how the network relates the input to the output variables.
  6. 6. 6/45 interpreting predicted classes explaining individual decisions
  7. 7. Interpreting Predicted Classes Image from Symonian’13 Example: “How does a goose typically look like according to the neural network?” goose non-goose
  8. 8. 8/45 Explaining Individual Decisions Images from Lapuschkin’16 Example: “Why is a given image classified as a sheep?” sheep non-sheep
  9. 9. 9/45 Example: Autonomous Driving [Bojarski’17] Input: Decision Bojarski et al. 2017 “Explaining How a Deep Neural Network Trained with End-to- End Learning Steers a Car” PilotNet Explanation:
  10. 10. 10/45 Example: Pascal VOC Classification [Lapuschkin’16] Comparing Performance on Pascal VOC 2007 (Fisher Vector Classifier vs. DeepNet pretrained on ImageNet) Fisher classifier (pretrained) deep net Lapuschkin et al. 2016. Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
  11. 11. 11/45 Example: Pascal VOC Classification [Lapuschkin’16] Lapuschkin et al. 2016. Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
  12. 12. 12/45 Example: Medical Diagnosis [Binder’18] A: Invasive breast cancer, H&E stain; B: Normal mammary glands and fibrous tissue, H&E stain; C: Diffuse carcinoma infiltrate in fibrous tissue, Hematoxylin stain Binder et al. 2018 “Towards computational fluorescence microscopy: Machine learning- based integrated prediction of morphological and molecular tumor profiles”
  13. 13. 13/45 Example: Quantum Chemistry [Schütt’17] DFT calculation of the stationary Schrödinger Equation DTNN, Schütt’17 molecular structure (e.g. atoms positions) molecular electronic properties (e.g. atomization energy) PBE0, Pedrew’86 interpretable insight Schütt et al. 2017: Quantum-Chemical Insights from Deep Tensor Neural Networks
  14. 14. 14/45 Examples of Explanation Methods
  15. 15. 15/45 Explaining by Decomposing Importance of a variable is the share of the function score that can be attributed to it. Decomposition property: input DNN
  16. 16. 16/45 Explaning Linear Models A simple method:
  17. 17. 17/45 Explaning Linear Models Taylor decomposition approach: Insight: explanation depends on the root point.
  18. 18. 18/45 Explaining Nonlinear Models second-order terms are hard to interpret and can be very large
  19. 19. 19/45 Explaining Nonlinear Models by Propagation Layer-Wise Relevance Propagation (LRP) [Bach’15]
  20. 20. 20/45 Explaining Nonlinear Models by Propagation Is there an underlying mathematical framework?
  21. 21. 21/45 Deep Taylor Decomposition (DTD) [Montavon’17] Question: Suppose that we have propagated LRP scores (“relevance”) until a given layer. How should it be propagated one layer further? Key idea: Let’s use Taylor expansions for this.
  22. 22. 22/45 DTD Step 1: The Structure of Relevance Observation: Relevance at each layer is a product of the activation and an approximately constant term.
  23. 23. 23/45 DTD Step 1: The Stucture of Relevance
  24. 24. 24/45 DTD Step 2: Taylor Expansion
  25. 25. 25/45 DTD Step 2: Taylor Expansion Taylor expansion at root point: Relevance can now be backward propagated
  26. 26. 26/45 DTD Step 3: Choosing the Root Point (same as LRP-α1 β0 ) (Deep Taylor generic) ✔ 1. nearest root 2. rescaled excitations Choice of root point
  27. 27. 27/45 DTD: Choosing the Root Point (Deep Taylor generic) Pixels domain Choice of root point
  28. 28. 28/45 DTD: Choosing the Root Point (Deep Taylor generic) Choice of root point Embedding: image source: Tensorflow tutorial
  29. 29. 29/45 DTD: Application to Pooling Layers A sum-pooling layer over positive activations is equivalent to a ReLU layer with weights 1. A p-norm pooling layer can be approximated as a sum-pooling layer multiplied by a ratio of norms that we treat as constant [Montavon’17]. → Treat pooling layers as ReLU detection layers
  30. 30. 30/45 Deep Taylor Decomposition on ConvNets LRP-α1 β0 DTDfor pixels LRP-α1 β0 LRP-α1 β0 LRP-α1 β0 LRP-α1 β0 LRP-α1 β0 * * For top-layers, other rules may improve selectivity forward pass backward pass
  31. 31. 31/45 Implementing Propagation Rules Sequence of element-wise computations Sequence of vector computations Example: LRP-α1 β0 :
  32. 32. 32/45 Implementing Propagation Rules Example: LRP-α1 β0 : Code that reuses forward and gradient computations:
  33. 33. 33/45 How Deep Taylor / LRP Scales
  34. 34. 34/45 Implementation on Large-Scale Models [Alber’18] https://github.com/albermax/innvestigate
  35. 35. 35/45 DTD for Kernel Models [Kauffmann’18] 1. Build a neural network equivalent of the One-Class SVM: Gaussian/Laplace Kernel Student Kernel 2. Computes its deep Taylor decomposition Outlier score
  36. 36. 36/45 DTD: Choosing the Root Point (Revisited) ✔ ✔ 1. nearest root 3. rescaled activation 2. rescaled excitations Choice of root point 2. LRP-α1 β0 (Deep Taylor generic) 3. Another rule
  37. 37. 37/45 Selecting the Explanation Technique How to select the best root points?
  38. 38. 38/45 Selecting the Explanation Technique Which rule leads to the best explanation?
  39. 39. 39/45 Selecting the Explanation Technique What axioms should an explanation satisfy?
  40. 40. 40/45 Selection based on Axioms division by zero → scores explode. LRP-α1 β0
  41. 41. 41/45 Selection based on Axioms discontinuous step function for bj = 0 LRP-α1 β0
  42. 42. 42/45 Explainable ML: Challenges Validating Explanations Underlying mathematical framework Axioms of an explanation Perturbation analysis [Samek’17] Similarity to ground truth Human perception
  43. 43. 43/45 Explainable ML: Opportunities Using Explanations Human interaction Designing better ML algorithms? Extracting new domain knowledge Detecting unexpected ML behavior Finding weaknesses of a dataset
  44. 44. 44/45 Check our webpage with interactive demos, software, tutorials, ... and our tutorial paper: Montavon, G., Samek, W., Müller, K.-R. Methods for Interpreting and Understanding Deep Neural Networks, Digital Signal Processing, 2018
  45. 45. 45/45 References ● [Alber’18] Alber, M. Lapuschkin, S., Seegerer, P., Hägele, M., Schütt, K., Montavon, G., Samek, W., Müller, K.-R., Dähne, S., Kindermans. P.-J. iNNvestigate neural networks. CoRR abs/1808.04260, 2018 ● [Bach’15] Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., Samek, W. On pixel-wise explanations for nonlinear classifier decisions by layer-wise relevance propagation. PLOS ONE 10 (7), 2015 ● [Binder’18] Binder, A., Bockmayr, M., …, Müller, K.-R., Klauschen, F. Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles CoRR abs/1805.11178, 2018 ● [Bojarski’17] Bojarski, M., Yeres, P., Choromanska, A., Choromanski, K., Firner, B., Jackel, L. D., Muller, U. Explaining how a deep neural network trained with end-to-end learning steers a car. CoRR abs/1704.07911, 2017 ● [Kauffmann’18] Kauffmann, J. Müller, K.-R., Montavon, G., Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models CoRR abs/1805.06230, 2018 ● [Lapuschkin’16] Lapuschkin, S., Binder, A., Montavon, G., Müller, K.-R., Samek, W. Analyzing classifiers: Fisher vectors and deep neural networks. CVPR 2016 ● [Montavon’17] Montavon, G., Lapuschkin, S., Binder, A., Samek, W., Müller, K.-R. Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognition 65, 211–222, 2017 ● [Samek’17] Samek, W., Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R. Evaluating the visualization of what a deep neural network has learned. IEEE Transactions on Neural Networks and Learning Systems, 2017 ● [Schütt’17] Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K.-R., Tkatchenko, A. Quantum-Chemical Insights from Deep Tensor Neural Networks, Nature Communications 8, 13890, 2017 ● [Symonian’13] Symonian, K. Vedaldi, A. Zisserman, A. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. ArXiv 2013

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