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Methods for interpreting and understanding deep neural networks

Abstract: This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017 Methods for Interpreting and Understanding Deep Neural Networks Gr egoire Montavon a, , Wojciech Samek b, , Klaus-Robert Muller a,c,d, a Department of Electrical Engineering & Computer Science, Technische Universit at Berlin, Marchstr. 23, Berlin 10587, German This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data Montavon, G, Samek, W & Müller, KR 2018, ' Methods for interpreting and understanding deep neural networks ', Digital Signal Processing: A Review Journal, vol. 73, pp. 1-15. https://doi.org/10.1016/j.dsp.2017.10.011. Montavon G, Samek W, Müller KR

This section focuses on the problem of interpreting a concept learned by a deep neural network (DNN). A DNN is a collection of neurons organized in a sequence of multiple layers, where neurons receive as input the neuron activations from the previous layer, and perform a simple computation (e.g. a weighted sum of the input followed by a nonlinear activation) The paper that was chosen is Methods for interpreting and understanding deep neural networks from Montavon et. al.[3]. Deep Neural networks work exceedingly well in many machine learning tasks and for example enable huge advancements in the eld of computer vision. The universal approximation theorem gives us mathematical proof that these networks can (in theory) learn any concept. Training these models via gradien

Layerwise relevance propagation (LRP) is a backward propagation technique which has gained considerable notoriety as a method to explain and interpret deep networks beyond many existing techniques.. Methods for Interpreting and Understanding Deep Neural Networks Gr´egoireMontavona, ∗,WojciechSamekbKlaus-RobertM¨ullera,c,d,∗ a Department of Electrical Engineering & Computer Science, Technische Universit¨at Berlin, Marchstr. 23, Berlin 10587, Germany b Department of Video Coding & Analytics, Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, Berlin 10587, German Tutorial on Methods for Interpreting and Understanding Deep Neural Networks Wojciech Samek (Fraunhofer HHI) Grégoire Montavon (TU Berlin) Klaus-Robert Müller (TU Berlin) 1:30 - 2:00 Part 1: Introduction 2:00 - 3:00 Part 2a: Making Deep Neural Networks Transparent 3:00 - 3:30 Break 3:30 - 4:00 Part 2b: Making Deep Neural Networks Transparen

Methods for interpreting and understanding deep neural networks Presented by Philipp Wimmer Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller 201 Methods for Interpreting and Understanding Deep Neural Networks. 24 Jun 2017 · Grégoire Montavon , Wojciech Samek , Klaus-Robert Müller ·. Edit social preview. This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. . Abstract This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications Methods for Interpreting and Understanding Deep Neural NetworksGrégoire Montavona, , Wojciech Samekb, , Klaus-Robert Müllera,c,d, aDepartment of Electrical Engineering & Computer Science, Technische Universität Berlin, Marchstr. 23, Berlin 10587, Germanyb Department of Video Coding & Analytics, Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, Berlin 10587, Germanyc Department of Brain &

(2018) Montavon et al. Digital Signal Processing: A Review Journal. This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is.. Methods for Interpreting and Understanding Deep Neural Networks #351. icoxfog417 opened this issue on Jul 5, 2017 · 2 comments. Labels. Interpretation. Comments. icoxfog417 added the DataRepresentation label on Jul 5, 2017. icoxfog417 added Interpretation and removed DataRepresentation labels on Mar 29, 2018

Video: Methods for Interpreting and Understanding Deep Neural

  1. Methods for interpreting and understanding deep neural networks This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative.
  2. Methods for Interpreting and Understanding Deep Neural Networks. Montavon, Grégoire. ; Samek, Wojciech. ; Müller, Klaus-Robert. Abstract. This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017
  3. [1] Montavon, G., Samek, W., Müller, K., jun 2017. Methods for Interpreting and Understanding Deep Neural Networks. arXiv preprint arXiv:1706.07979, 2017. Section 1.3 [2] Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J., 2016. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
  4. Methods for Interpreting and Understanding Deep Neural Networks. G. Montavon, W. Samek, and K. Müller. (2018-02) DOI 10.1016/j.dsp.2017.10.011 search on. Google Scholar Microsoft Bing WorldCat BASE. Tags interpreting neural_networks_literature. Users. Comments and Reviews

原文: Methods for interpreting and understanding deep neural networks. 博客内容: 关于该文章的学习摘要. 将论文的关键内容进行了翻译、配图说明,配合原文阅读,应该能较好的理解文章内 The Activation Maximization (AM) Method Let us interpret a concept predicted by a deep neural net (e.g. a class, or a real-valued quantity): Examples: I Creating a class prototype: maxx2X logp(!cjx): I Synthesizing an extreme case: maxx2X f(x): ICASSP 2017 Tutorial — G. Montavon, W. Samek, K.-R. Müller 5/4 understand the intelligent agents, which has become an important issue due to its importance in practical appli-cations. To address this issue, we develop a Distillation Guided Routing method, which is a flexible framework to interpret a deep neural network by identifying critical data routing paths and analyzing the functional processing be In deep learning, these data collection limitations do not exist because we can easily measure neural activations for very large and diverse datasets.Some works have manually found an analogue to tuning dimensions in deep neural networks (DNNs) Cammarata et al. (); Carter et al. (); however, there is currently no way to automatically identify them.In this paper, we define such a method as follows Interpretability of deep neural networks is a recently emerging area of machine learning research targeting a better understanding of how models perform feature selection and derive their classification decisions. In this paper, two neural network architectures are trained on spectrogram and raw waveform data for audio classification tasks on a newly created audio dataset and layer-wise.

The problem of explaining complex machine learning models, including Deep Neural Networks, has gained increasing attention over the last few years. While several methods have been proposed to explain network predictions, the definition itself of explanation is still debated deep neural networks has been the difficulty in interpreting and explaining the classification results. Recently, explain-ability methods have been devised for deep networks and specifically CNNs [32, 42, 31, 39, 40, 41]. These methods enable one to probe a CNN and identify the important sub-structures of the input data (as deemed by the. (2018) Montavon et al. Digital Signal Processing: A Review Journal. This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is..

Methods For Interpreting And Understanding Deep Neural

  1. Download and reference Methods For Interpreting And Understanding Deep Neural Networks by on Citationsy Online citations, reference lists, and bibliographies. Hom
  2. Abstract: This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data
  3. . Explainable Models for Time Series Data - 50
  4. Methods used in this study: Neural network: to decide whether there's a horse. Visualization technique (LRP): to analyze network's strategies. The following slides provide two things: An example of problematic strategies an ANN might use and why it might use those. A way to identify such strategies. 1
  5. Methods for Interpreting and Understanding Deep Neural Networks Digital Signal Processing, 73:1-15, 2018 [preprint | bibtex] W Samek, T Wiegand, KR Müller. Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models ITU Journal: ICT Discoveries - Special Issue 1 - The Impact of AI on Communication.

Challenge In Understanding Generalization of Deep Neural Networks Mismatch between Empirical Observation and Learning Theory The deep neural network models are in principle rich enough to memorize the training data (# parameters ≫ # of examples) However in practice, they do not overfit 7/4 Although deep learning techniques have been successfully applied to many tasks, interpreting deep neural network models is still a big challenge to us. Recently, many works have been done on visu-alizing and analyzing the mechanism of deep neural networks in the areas of image processing and natural language processing. I

Really for business context it can be anything you like. I think at the moment alot of demand will be in the area of supervised learning, where you use some input features selected and then predict a output of either a class (I.e types of products.. Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data. we will provide a comprehensive overview on methods to analyze deep neural networks and an insight how those interpretable and explainable methods help us understand time series data. Visualizing and understanding convolutional networks. In Proceedings. Methods for Interpreting and Understanding Deep Neural Networks (2018)-G. Montavon, W. Samek and K. Müller Explaining the visualization of what deep neural networks has learned (2017)-W. Samek, A. Binder, G. Montavon, S. Bach and K. Müller Explaining Explanations: An Overview of Interpretability of Machine Learning (2018 In this post, I will cover the intuition behind using these gradients, as well as two specific techniques that have come out of this: Integrated Gradients and DeepLift. Using gradients to interpret neural networks. Possibly the most intepretable model — and therefore the one we will use as inspiration — is a regression Understanding, Interpreting and Design Neural Network Models Through Tensor Representations Furong Huang University of Maryland. Modern deep neural networks have found tremendous empirical success in a wide variety of data science applications

Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals Interpretability of deep neural networks is a recently emerging area of machine learning research targeting a better understanding of how models perform feature selection and derive their classification decisions Methods for interpreting and understanding deep neural networks. Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller. Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73: 1-15, 2018 Download PDF: Sorry, we are unable to provide the full text but you may find it at the following location(s): https://doi.org/10.1016/j.dsp.... (external link

Deep Neural Networks (DNNs) have recently received sig- close this gap by applying attribution methods that aim for interpreting DNN decisions, in order to identify leaking operations in cryptographic Due to the black-box nature of DNNs, understanding the operation of Deep Learning (DL) models is an active area of research. It is. This work aims to deepen the understanding of a recurrent neural network for land use classification based on Sentinel-2 time series in the context of the European Common Agricultural Policy (CAP. Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Proceedings of the IEEE, 109(3):247-278, 2021 [preprint, bibtex] G Montavon, W Samek, KR Müller. Methods for Interpreting and Understanding Deep Neural Networks Digital Signal Processing, 73:1-15, 2018 ; W Samek, T Wiegand, KR Müller Understanding NN. This repository is intended to be a tutorial of various DNN interpretation and explanation techniques. Explanation of the theoretical background as well as step-by-step Tensorflow implementation for practical usage are both covered in the Jupyter Notebooks Although the recent succ esses of deep neural networks have fostered the interest of academia and industry, the predictions of these models are currently opaque to humans, which has important implications in terms of security, ethics, robustness, and scientific understanding. As deep neural networks will be increasingly deployed for automatic.

GitHub - 1202kbs/Understanding-NN: Tensorflow tutorial for

Tensorflow tutorial for various Deep Neural Network visualization techniques Understanding NN This repository is intended to be a tutorial of various DNN interpretation and explanation techniques. Explanation of the theoretical background as well as step-by-step Tensorflow implementation for practical us Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years Chapter 7. Neural Network Interpretation. This chapter is currently only available in this web version. ebook and print will follow. The following chapters focus on interpretation methods for neural networks. The methods visualize features and concepts learned by a neural network, explain individual predictions and simplify neural networks Methods for Interpreting and Understanding Deep Neural Networks. intro: Technische Universit¨at Berlin & Fraunhofer Heinrich Hertz Institute Interpreting Deep Neural Networks. blog: How convolutional neural network see the world - A survey of convolutional neural network visualization methods. intro: Mathematical Foundations of.

Dealing with Overconfidence in Neural Networks: Bayesian

学习摘要:Methods for interpreting and understanding deep

based techniques to interpret any deep neural network. By calculating the gradients at decision time we can create a causal relationship between input features and the network's output. With it, we can accurately attribute decisions to a specific cause or causes. This provides a generic method for Neural Network Intrepretability Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for inter-preting deep neural networks and extracting network-learned features from input data. W Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Despite the potential risk they bring, adversarial examples are also valuable for providing insights into the weakness and blind-spots of DNNs. Thus, the interpretability of a DNN in the adversarial setting aims to explain the rationale behind its.

These techniques utilize deep neural networks' layered structure. They scale better when used on complex deep neural networks than gradient-based methods, but can be used also on different machine learning models. The heatmap is obtained by backpropagating relevances from the output layer through the model to the input layer / Relative attributing propagation : Interpreting the comparative contributions of individual units in deep neural networks. AAAI 2020 - 34th AAAI Conference on Artificial Intelligence. AAAI press, 2020. pp. 2501-2508 (AAAI 2020 - 34th AAAI Conference on Artificial Intelligence)

As deep learning methods are becoming the front runner among machine learn- ing techniques, the importance of interpreting and understanding these meth- ods grows.Deep neural networks are known for their highly competitive pre- diction accuracies, but also infamously for their black box properties when it comes to their decision making process. Tree-based models on the other end of the. Ghanbari, M. & Ohler, U. Deep neural networks for interpreting RNA-binding protein target preferences. Genome Res. 30 , 214-226 (2020). CAS PubMed PubMed Central Article Google Schola Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and consume huge calculation resources. To alleviate this limitation, numerous deep-learning based target recognition methods are proposed, particularly.

years, deep learning models have shown great potential to generate graphs with complex properties preserved [54, 18, 35]. However, these methods mainly aim to generate graphs that reflect certain properties in the training graphs. 3 Background We first describe notations, and then provide some background on graph neural networks. Notations Computational Methods for Deep Learning: Theoretic, Practice and Applications [1st ed.] 9783030610807, 9783030610814. Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook Interpreting Deep Neural Networks Beyond Attribution Methods Integrated gradients were used to uncover motifs for RNA-protein interactions (Ghanbari & Ohler,2019). Recently, DeepLift was used to uncover known and novel TF binding sites, including their syntax with respect to other binding sites (Avsec et al.,2019). In silico mutagenesis - the gol Interpreting Deep Neural Networks for Medical Image Analysis A Project Report submitted by Understanding the organization and knowledge extraction process of deep learning This work aims to implement explainability techniques on deep learning models for medical image analysis, specifically, for brain tumor segmentation.. For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/vide

Identifying and interpreting tuning dimensions in deep

A review of analytical techniques for gait data. Part 2: Neural network and wavelet methods. G., Samek, W. & Müller, K.-R. Methods for interpreting and understanding deep neural networks.. Autor: Montavon, Grégoire et al.; Genre: Zeitschriftenartikel; Im Druck veröffentlicht: 2018; Open Access; Titel: Methods for Interpreting and Understanding Deep Neural Networks Deutsch English Deutsch 日本

Montavon et al., Methods for interpreting and understanding deep neural networks, Digital Signal Processing, 73:1-5, 2018 New Book: Samek, Montavon, Vedaldi, Hansen, Müller (eds.), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNAI 11700, Springer (2019) (coming up in 1 month) Keras Explanation Toolbo Interpreting Deep Neural Networks Beyond Attribution Methods ogy. For example, gradients (from predictions to the inputs) have been employed to reveal known transcription factor (TF) binding sites when trained to predict read profiles from high-throughput sequencing datasets (Kelley et al., 2018) Understanding NN. This repository is intended to be a tutorial of various DNN interpretation and explanation techniques. This section focuses on interpreting a concept learned by a deep neural network (DNN) through activation maximization. 1.1 Activation Maximization (AM) relevance decomposition with Simple Taylor Decomposition and then. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data. It also discusses a number of practical applications. Methods for Interpreting and Understanding Deep Neural Networks

Video: Interpreting and Explaining Deep Neural Networks for

Gradient-Based Attribution Methods SpringerLin

  1. These methods are evaluated on three classification tasks. Keywords - Artificial intelligence, black-box models, deep neural networks, interpretability, layer-wise relevance propagation, sensitivity analysis . poker [28]. These immense successes of AI systems, especially deep learning models, show th
  2. g the front runner among machine learning techniques, the importance of interpreting and understanding these methods grows. Deep neural networks are known for their highly competitive prediction accuracies, but also infamously for their black box properties when it comes to their.
  3. Understanding Deep Neural Networks 3 advice for caution when interpreting saliency maps. For example, [1] reports present TCAV, a method to understand a neural network on the level of the learned mapping. They use images that represent a certain feature. An SVM is trained on intermediate representations extracted from th
  4. training large, deep neural networks (DNNs), in-cluding notable successes in training convolu-tional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what compu-tations they perform at intermediate layers, has lagged behind. Progress in the field will b

[PDF] Methods For Interpreting And Understanding Deep

  1. He co-organized the first three editions of BlackboxNLP, the Workshop on Analyzing and Interpreting Neural Networks for NLP. Together with Afra Alishahi and students, he did some of the pioneering research on analyzing deep learning methods for visually grounded language (Kádár, Chrupała, & Alishahi, 2017) as well as for speech (Alishahi.
  2. He received a Masters degree in Communication Systems from École Polytechnique Fédérale de Lausanne in 2009 and received a Ph.D. degree from the Technische Universität Berlin in 2013. His research focuses on techniques for interpreting ML models such as deep neural networks and kernels
  3. Part II Methods for Interpreting AI Systems 4. Understanding Neural Networks via Feature Visualization: A Survey. 55 Anh Nguyen, Jason Yosinski, and Jeff Clune 5. Explanations for Attributing Deep Neural Network Predictions..... 149 Ruth Fong and Andrea Vedaldi 9. Gradient-Based Attribution Methods....
  4. Abstract: In recent years deep learning-based methods have achieved state-of-the-art performances in many domains. However, a thorough understanding of how deep learning models work remains a huge challenge. This lack of interpretability has significantly restricted a wide utilization of deep learning

Title: Methods for Interpreting and Understanding Deep

Deep neural networks are very complex and their decisions can be hard to interpret. The LIME technique approximates the classification behavior of a deep neural network using a simpler, more interpretable model, such as a regression tree. Interpreting the decisions of this simpler model provides insight into the decisions of the neural network [1] Recently deep neural networks have demonstrated competitive per-formance in classification and regression tasks for sequential data. However, it is still hard to understand which temporal patterns the internal channels of deep neural networks see in sequential data. To address this issue, we propose a new framework to visualiz

Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind The present workshop aims to review recent techniques and establish new theoretical foundations for interpreting and understanding deep learning models. However, interpretability of deep neural networks, (2) analysis and comparison of state-of-the-art models, (3) formalization of the interpretability problem, (4) interpretability for making.

[DeepLearning]关于Neural Network可解释性的可视化工具及Papers - 知

  1. Evaluating the visualization of what a deep neural network has learned W Samek, A Binder, G Montavon, S Lapuschkin, KR Müller IEEE transactions on neural networks and learning systems 28 (11), 2660-2673 , 201
  2. A benchmark for interpretability methods in deep neural networks(同arxiv:1806.10758) 3: 2019: arxiv: Interpretable CNNs: 3: 2019: NIPS: Full-gradient representation for neural network visualization: 2: 2019: NIPS: On the (In) fidelity and Sensitivity of Explanations: 2: 2019: ICCV: Understanding Deep Networks via Extremal Perturbations and.
  3. Fully Convolutional Networks for Semantic Segmentation; W6: Sep 24: Visualizing CNNs. PS/HW2 due night before (Wed. 9/23), PS/HW3 out Recorded Lecture, Slides (pdf). Methods for Interpreting and Understanding Deep Neural Networks ; Network Dissection: Quantifying Interpretability of Deep Visual Representations; W7: Sep 2
  4. As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs. In this paper, we propose Relative Attributing Propagation (RAP), which decomposes the output predictions of DNNs with a new perspective of separating the relevant (positive) and irrelevant (negative.
  5. Deep neural networks outperform comparison approaches. In predicting histology, deep neural networks outperformed other models by more than 0.10 AUC in 10-fold CV of the training dataset . Performance of the histology model remained consistent in the testing dataset, achieving test scores of 0.86 AUC, 0.91 AUC, and 0.71 AUC in predicting ADC.
  6. ology
  7. techniques of interpreting deep neural networks. They identified cross-cutting techniques that have been applied to explain the behavior of a wide range of models. A notable contribution of this tutorial is an approach for sensitivity analysis capa-ble of identifying important input features to a net-work. The technique observes the magnitude.

How to interpret Neural Network results - Quor

Abstract: Artificial deep neural networks are a powerful tool, able to extract information from large datasets and, using this acquired knowledge, make accurate predictions on previously unseen data. As a result, they are being applied in a wide variety of domains ranging from genomics to autonomous driving, from speech recognition to gaming. Many areas, where neural network-based solutions. Deep neural networks have been applied to hate speech detection with apparent success, but they have limited practical applicability without transparency into the predictions they make. In this paper, we perform several experiments to visualize and understand a state-of-the-art neural network classifier for hate speech (Zhang et al., 2018) All effective, modern, end-to-end document understanding systems present in the literature integrate multiple deep neural network architectures for both reading and comprehending a document's content. Since documents are made for humans, not machines, practitioners must combine CV as well as NLP architectures into a unified solution Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. pp. 3387-3395

Deep And Wide Neural Network - img-figtree

Interpreting and Explaining Deep Neural Networks

Integrating medical imaging and cancer biology with deep neural networks. by SPIE. Neural network framework. Credit: Smedley, Aberle, and Hsu. Despite our remarkable advances in medicine and healthcare, the cure to cancer continues to elude us. On the bright side, we have made considerable progress in detecting several cancers in earlier stages. concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie review

Peter KOO | Postdoctoral Research Associate | HarvardDemystifying the neural network black box - Speaker DeckWojciech Samek | Department of Artificial Intelligence