PPT-Rationalizing Neural Predictions

Tao Lei Regina Barzilay and Tommi Jaakkola EMNLP 16 Feb 9 201 7 Abstract 1 Prediction without justification has limited applicability We learn to extract pieces of input text as justificaitonsrationales . 7.5 The student will read and demonstrate comprehension of a variety of fictional texts, narrative nonfiction, and poetry. .. e) Make. , confirm, and revise predictions. . What is a prediction? . A prediction is a forecast or an educated guess of what may happen next. Kong Da, Xueyu Lei & Paul McKay. Digit Recognition. Convolutional Neural Network. Inspired by the visual cortex. Our example: Handwritten digit recognition. Reference: . LeCun. et al. . Back propagation Applied to Handwritten Zip Code Recognition. Banafsheh. . Rekabdar. Biological Neuron:. The Elementary Processing Unit of the Brain. Biological Neuron:. A Generic Structure. Dendrite. Soma. Synapse. Axon. Axon Terminal. Biological Neuron – Computational Intelligence Approach:. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. What are Artificial Neural Networks (ANN)?. ". Colored. neural network" by Glosser.ca - Own work, Derivative of File:Artificial neural . network.svg. . Licensed under CC BY-SA 3.0 via Commons - https://commons.wikimedia.org/wiki/File:Colored_neural_network.svg#/media/File:Colored_neural_network.svg. Chris Ferro (University of Exeter). Tom . Fricker. , . Fredi. Otto, Emma Suckling. 12th International Meeting on Statistical Climatology (28 June 2013, . Jeju. , Korea). Credibility and performance. Table of Contents. Part 1: The Motivation and History of Neural Networks. Part 2: Components of Artificial Neural Networks. Part 3: Particular Types of Neural Network Architectures. Part 4: Fundamentals on Learning and Training Samples. of Poker AI. Christopher Kramer. Outline of Information. The Challenge. Application, problem to be solved, motivation. Why create a poker machine with ANNE?. The Flop. The hypothesis. Can a Poker AI run using only an ANNE?. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. 2015/10/02. 陳柏任. Outline. Neural Networks. Convolutional Neural Networks. Some famous CNN structure. Applications. Toolkit. Conclusion. Reference. 2. Outline. Neural Networks. Convolutional Neural Networks. Stimulus-Response. Stimulus-Response. Neural Processes. Use sensory systems to detect the stimulus. Visual, auditory, tactile…. Central computation or representation . Access memory, risk-reward, etc.. Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . Rohit. Ray. ESE 251. What are Artificial Neural Networks?. ANN are inspired by models of the biological nervous systems such as the brain. Novel structure by which to process information. Number of highly interconnected processing elements (neurons) working in unison to solve specific problems.. Scatter Plot Review. Using the Regression Line Model to Make Predictions. It’s the responsibility of the news medium to report on important decisions made by newsmakers. Examples include new traffic laws based on the number of accidents, immigration reform based on the number of people emigrating to the U.S., and gas prices based on the supply and demand of oil. These decisions make headlines because of the impact they have on our lives.