SRI Blog

-By Makarand Tapaswi , Yukun Zhu , Rainer Stiefelhagen  Antonio Torralba , Raquel Urtasun , Sanja Fidler  arlsruhe Institute of Technology,  Massachusetts Institute of Technology,  University of Toronto Paper Link Abstract We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from the simpler “Who” did “What” to “Whom”, to “Why” and “How” certain events occurred. Each question comes with a set of five possible answers; a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of information – video clips, plots, subtitles, scripts, and DVS. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is hard. We make this data

-By Sara Sabour, Nicholas Frosst,  Geoffrey E. Hinton  Google Brain Toronto Paper Link Abstract   A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsule

-By Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim Google Brain  University of California Berkeley Paper Link This blog post is the fourth part of the Explainable Artificial Intelligence (XAI) series. Refer previous posts link . The post discusses on Salience Mapping techniques, performance, and metrics. Salience Mapping source: Analytics India Magazine The salience map approach is exemplified by occlusion procedure by Zeiler, where a network is repeatedly tested with portions of the input occluded to create a map showing which parts of the data actually have an influence on the network output. Abstract Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. This paper proposes an actionable methodology to evaluate what kinds of explanations a given met

-By Tameru Hailesilassie Department of Computer Science and Engineering National University of Science and Technology (MISiS) Moscow, Russia Today's blog is the continuation of XAI series. Rule Extraction from Neural Networks Abstract—Despite the highest classification accuracy in wide varieties of application areas, the artificial neural network has one disadvantage. The way this Network comes to a decision is not easily comprehensible. The lack of explanation ability reduces the acceptability of neural network in data mining and decision system. This drawback is the reason why researchers have proposed many rule extraction algorithms to solve the problem. Recently, Deep Neural Network (DNN) is achieving a profound result over the standard neural network for classification and recognition problems. It is a hot machine learning area proven both useful and innovative. This paper has thoroughly reviewed various rule extraction algorithms, considering the classifi