Tracking the World State with Recurrent Entity Networks

Size: px
Start display at page:

Download "Tracking the World State with Recurrent Entity Networks"

Transcription

1 Tracking the World State with Recurrent Entity Networks Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun Task At each timestep, get information (in the form of a sentence) about the state of the world. Then answer a question. When we get new information, we should update our representation of the world state. The world state can be decomposed into the state of each entity in the world, so we only need to update one entity. Architecture The memory model: input: a sequence of vectors s 1, s T output: a set of entity representations h, h 1 k The world is a collection of entities. Information about each entity is stored in a single cell. Each cell comes with a key and a memory slot. h ~ and g depends on h, w, s standard gating mechanism: 1/10

2 multiple-cells at multiple timesteps: 2/10

3 Input Encoder: input: a sequence of sentences. output: an encoding of each sentence as a fixed sized vector e i are pretrained embeddings Output Module: input: a query vector q and the outputs of the memory model output: arbitrary vector (log probabilities over words) 3/10

4 Look at only one entity and drop the query: y = Rϕ(Hh j) = Rϕ j y i = Riϕ j Key vectors the model should identify entities by keys, which are trainable Key tying: Use parser to identify entities. One memory cell for each entity. Freeze key vector to be word embedding of an entity. Related work LSTM/GRU RENN 4/10

5 scalar memory cell with full interaction separate memory cells just sigmoid layer of input and hidden state content-based term between input and hidden state LSTM: Forget gate layer: Input gate layer & tanh(hyperbolic tangent) layer: Memory Network store the entire input sequence in dynamic long-term memory sequentially update a controller's hidden state via a softmax gating over the memories RENN a fixed number of blocks a window of words of hidden states as memories update each block with an independent gated RNN Gated graph network RENN inter-network communication with edges parallel/independent recurrent models Compared to RENN, CommNN, Interaction Network, Neural Physics Engine use parallel recurrent models without gating mechanism. Experiments Synthetic world model task Task details: Two agents randomly placed in a 10x10 grid Answer the locations of the agents based on up to T-2 supporting facts Details: 5 memory slots 20D per cell 5/10

6 babi Details: 20 memory cells 100D embedding U = V = 0, W = I, ϕ = id entity matrix 6/10

7 7/10

8 Interpreting representations Recall that y i = Riϕ j Find closest R for each entity ϕ : i j 8/10

9 CBT Input:. 20 sentences. 21st sentence with missing word. list of candidate words Details: Tied keys to candidate words Dropout U = V = 0, W = I, ϕ = id No normalization 9/10

10 10/10

Sequence Modeling with Neural Networks

Sequence Modeling with Neural Networks Sequence Modeling with Neural Networks Harini Suresh y 0 y 1 y 2 s 0 s 1 s 2... x 0 x 1 x 2 hat is a sequence? This morning I took the dog for a walk. sentence medical signals speech waveform Successes

More information

Long-Short Term Memory and Other Gated RNNs

Long-Short Term Memory and Other Gated RNNs Long-Short Term Memory and Other Gated RNNs Sargur Srihari srihari@buffalo.edu This is part of lecture slides on Deep Learning: http://www.cedar.buffalo.edu/~srihari/cse676 1 Topics in Sequence Modeling

More information

Natural Language Processing and Recurrent Neural Networks

Natural Language Processing and Recurrent Neural Networks Natural Language Processing and Recurrent Neural Networks Pranay Tarafdar October 19 th, 2018 Outline Introduction to NLP Word2vec RNN GRU LSTM Demo What is NLP? Natural Language? : Huge amount of information

More information

Recurrent Autoregressive Networks for Online Multi-Object Tracking. Presented By: Ishan Gupta

Recurrent Autoregressive Networks for Online Multi-Object Tracking. Presented By: Ishan Gupta Recurrent Autoregressive Networks for Online Multi-Object Tracking Presented By: Ishan Gupta Outline Multi Object Tracking Recurrent Autoregressive Networks (RANs) RANs for Online Tracking Other State

More information

Contents. (75pts) COS495 Midterm. (15pts) Short answers

Contents. (75pts) COS495 Midterm. (15pts) Short answers Contents (75pts) COS495 Midterm 1 (15pts) Short answers........................... 1 (5pts) Unequal loss............................. 2 (15pts) About LSTMs........................... 3 (25pts) Modular

More information

Recurrent and Recursive Networks

Recurrent and Recursive Networks Neural Networks with Applications to Vision and Language Recurrent and Recursive Networks Marco Kuhlmann Introduction Applications of sequence modelling Map unsegmented connected handwriting to strings.

More information

Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17

Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17 Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17 UMass Medical School ICLR 2017 Presenter: Jack Lanchantin ICLR 2017 Presenter: Jack Lanchantin 1 Outline 1 Intro 2 Hypothesis

More information

Artificial Neural Networks D B M G. Data Base and Data Mining Group of Politecnico di Torino. Elena Baralis. Politecnico di Torino

Artificial Neural Networks D B M G. Data Base and Data Mining Group of Politecnico di Torino. Elena Baralis. Politecnico di Torino Artificial Neural Networks Data Base and Data Mining Group of Politecnico di Torino Elena Baralis Politecnico di Torino Artificial Neural Networks Inspired to the structure of the human brain Neurons as

More information

RECURRENT NETWORKS I. Philipp Krähenbühl

RECURRENT NETWORKS I. Philipp Krähenbühl RECURRENT NETWORKS I Philipp Krähenbühl RECAP: CLASSIFICATION conv 1 conv 2 conv 3 conv 4 1 2 tu RECAP: SEGMENTATION conv 1 conv 2 conv 3 conv 4 RECAP: DETECTION conv 1 conv 2 conv 3 conv 4 RECAP: GENERATION

More information

Natural Language Understanding. Recap: probability, language models, and feedforward networks. Lecture 12: Recurrent Neural Networks and LSTMs

Natural Language Understanding. Recap: probability, language models, and feedforward networks. Lecture 12: Recurrent Neural Networks and LSTMs Natural Language Understanding Lecture 12: Recurrent Neural Networks and LSTMs Recap: probability, language models, and feedforward networks Simple Recurrent Networks Adam Lopez Credits: Mirella Lapata

More information

Long-Short Term Memory

Long-Short Term Memory Long-Short Term Memory Sepp Hochreiter, Jürgen Schmidhuber Presented by Derek Jones Table of Contents 1. Introduction 2. Previous Work 3. Issues in Learning Long-Term Dependencies 4. Constant Error Flow

More information

Deep Learning. Recurrent Neural Network (RNNs) Ali Ghodsi. October 23, Slides are partially based on Book in preparation, Deep Learning

Deep Learning. Recurrent Neural Network (RNNs) Ali Ghodsi. October 23, Slides are partially based on Book in preparation, Deep Learning Recurrent Neural Network (RNNs) University of Waterloo October 23, 2015 Slides are partially based on Book in preparation, by Bengio, Goodfellow, and Aaron Courville, 2015 Sequential data Recurrent neural

More information

Recurrent Neural Networks Deep Learning Lecture 5. Efstratios Gavves

Recurrent Neural Networks Deep Learning Lecture 5. Efstratios Gavves Recurrent Neural Networks Deep Learning Lecture 5 Efstratios Gavves Sequential Data So far, all tasks assumed stationary data Neither all data, nor all tasks are stationary though Sequential Data: Text

More information

CSCI 315: Artificial Intelligence through Deep Learning

CSCI 315: Artificial Intelligence through Deep Learning CSCI 315: Artificial Intelligence through Deep Learning W&L Winter Term 2017 Prof. Levy Recurrent Neural Networks (Chapter 7) Recall our first-week discussion... How do we know stuff? (MIT Press 1996)

More information

Recurrent Neural Networks. Jian Tang

Recurrent Neural Networks. Jian Tang Recurrent Neural Networks Jian Tang tangjianpku@gmail.com 1 RNN: Recurrent neural networks Neural networks for sequence modeling Summarize a sequence with fix-sized vector through recursively updating

More information

CSC321 Lecture 15: Exploding and Vanishing Gradients

CSC321 Lecture 15: Exploding and Vanishing Gradients CSC321 Lecture 15: Exploding and Vanishing Gradients Roger Grosse Roger Grosse CSC321 Lecture 15: Exploding and Vanishing Gradients 1 / 23 Overview Yesterday, we saw how to compute the gradient descent

More information

arxiv: v3 [cs.lg] 14 Jan 2018

arxiv: v3 [cs.lg] 14 Jan 2018 A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation Gang Chen Department of Computer Science and Engineering, SUNY at Buffalo arxiv:1610.02583v3 [cs.lg] 14 Jan 2018 1 abstract We describe

More information

Modelling Time Series with Neural Networks. Volker Tresp Summer 2017

Modelling Time Series with Neural Networks. Volker Tresp Summer 2017 Modelling Time Series with Neural Networks Volker Tresp Summer 2017 1 Modelling of Time Series The next figure shows a time series (DAX) Other interesting time-series: energy prize, energy consumption,

More information

(2pts) What is the object being embedded (i.e. a vector representing this object is computed) when one uses

(2pts) What is the object being embedded (i.e. a vector representing this object is computed) when one uses Contents (75pts) COS495 Midterm 1 (15pts) Short answers........................... 1 (5pts) Unequal loss............................. 2 (15pts) About LSTMs........................... 3 (25pts) Modular

More information

Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions

Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions 2018 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 18) Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions Authors: S. Scardapane, S. Van Vaerenbergh,

More information

Analysis of the Learning Process of a Recurrent Neural Network on the Last k-bit Parity Function

Analysis of the Learning Process of a Recurrent Neural Network on the Last k-bit Parity Function Analysis of the Learning Process of a Recurrent Neural Network on the Last k-bit Parity Function Austin Wang Adviser: Xiuyuan Cheng May 4, 2017 1 Abstract This study analyzes how simple recurrent neural

More information

Introduction to Deep Neural Networks

Introduction to Deep Neural Networks Introduction to Deep Neural Networks Presenter: Chunyuan Li Pattern Classification and Recognition (ECE 681.01) Duke University April, 2016 Outline 1 Background and Preliminaries Why DNNs? Model: Logistic

More information

Structured Neural Networks (I)

Structured Neural Networks (I) Structured Neural Networks (I) CS 690N, Spring 208 Advanced Natural Language Processing http://peoplecsumassedu/~brenocon/anlp208/ Brendan O Connor College of Information and Computer Sciences University

More information

EE-559 Deep learning Recurrent Neural Networks

EE-559 Deep learning Recurrent Neural Networks EE-559 Deep learning 11.1. Recurrent Neural Networks François Fleuret https://fleuret.org/ee559/ Sun Feb 24 20:33:31 UTC 2019 Inference from sequences François Fleuret EE-559 Deep learning / 11.1. Recurrent

More information

GraphRNN: A Deep Generative Model for Graphs (24 Feb 2018)

GraphRNN: A Deep Generative Model for Graphs (24 Feb 2018) GraphRNN: A Deep Generative Model for Graphs (24 Feb 2018) Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec Presented by: Jesse Bettencourt and Harris Chan March 9, 2018 University

More information

Recurrent Neural Networks (Part - 2) Sumit Chopra Facebook

Recurrent Neural Networks (Part - 2) Sumit Chopra Facebook Recurrent Neural Networks (Part - 2) Sumit Chopra Facebook Recap Standard RNNs Training: Backpropagation Through Time (BPTT) Application to sequence modeling Language modeling Applications: Automatic speech

More information

High Order LSTM/GRU. Wenjie Luo. January 19, 2016

High Order LSTM/GRU. Wenjie Luo. January 19, 2016 High Order LSTM/GRU Wenjie Luo January 19, 2016 1 Introduction RNN is a powerful model for sequence data but suffers from gradient vanishing and explosion, thus difficult to be trained to capture long

More information

Google s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Google s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Google s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Y. Wu, M. Schuster, Z. Chen, Q.V. Le, M. Norouzi, et al. Google arxiv:1609.08144v2 Reviewed by : Bill

More information

Recurrent Neural Network

Recurrent Neural Network Recurrent Neural Network Xiaogang Wang xgwang@ee..edu.hk March 2, 2017 Xiaogang Wang (linux) Recurrent Neural Network March 2, 2017 1 / 48 Outline 1 Recurrent neural networks Recurrent neural networks

More information

Neural Networks 2. 2 Receptive fields and dealing with image inputs

Neural Networks 2. 2 Receptive fields and dealing with image inputs CS 446 Machine Learning Fall 2016 Oct 04, 2016 Neural Networks 2 Professor: Dan Roth Scribe: C. Cheng, C. Cervantes Overview Convolutional Neural Networks Recurrent Neural Networks 1 Introduction There

More information

Today s Lecture. Dropout

Today s Lecture. Dropout Today s Lecture so far we discussed RNNs as encoder and decoder we discussed some architecture variants: RNN vs. GRU vs. LSTM attention mechanisms Machine Translation 1: Advanced Neural Machine Translation

More information

Presented By: Omer Shmueli and Sivan Niv

Presented By: Omer Shmueli and Sivan Niv Deep Speaker: an End-to-End Neural Speaker Embedding System Chao Li, Xiaokong Ma, Bing Jiang, Xiangang Li, Xuewei Zhang, Xiao Liu, Ying Cao, Ajay Kannan, Zhenyao Zhu Presented By: Omer Shmueli and Sivan

More information

Neural Turing Machine. Author: Alex Graves, Greg Wayne, Ivo Danihelka Presented By: Tinghui Wang (Steve)

Neural Turing Machine. Author: Alex Graves, Greg Wayne, Ivo Danihelka Presented By: Tinghui Wang (Steve) Neural Turing Machine Author: Alex Graves, Greg Wayne, Ivo Danihelka Presented By: Tinghui Wang (Steve) Introduction Neural Turning Machine: Couple a Neural Network with external memory resources The combined

More information

Lecture 17: Neural Networks and Deep Learning

Lecture 17: Neural Networks and Deep Learning UVA CS 6316 / CS 4501-004 Machine Learning Fall 2016 Lecture 17: Neural Networks and Deep Learning Jack Lanchantin Dr. Yanjun Qi 1 Neurons 1-Layer Neural Network Multi-layer Neural Network Loss Functions

More information

Structured deep models: Deep learning on graphs and beyond

Structured deep models: Deep learning on graphs and beyond Structured deep models: Deep learning on graphs and beyond Hidden layer Hidden layer Input Output ReLU ReLU, 25 May 2018 CompBio Seminar, University of Cambridge In collaboration with Ethan Fetaya, Rianne

More information

Machine Translation. 10: Advanced Neural Machine Translation Architectures. Rico Sennrich. University of Edinburgh. R. Sennrich MT / 26

Machine Translation. 10: Advanced Neural Machine Translation Architectures. Rico Sennrich. University of Edinburgh. R. Sennrich MT / 26 Machine Translation 10: Advanced Neural Machine Translation Architectures Rico Sennrich University of Edinburgh R. Sennrich MT 2018 10 1 / 26 Today s Lecture so far today we discussed RNNs as encoder and

More information

IMPROVING STOCHASTIC GRADIENT DESCENT

IMPROVING STOCHASTIC GRADIENT DESCENT IMPROVING STOCHASTIC GRADIENT DESCENT WITH FEEDBACK Jayanth Koushik & Hiroaki Hayashi Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA {jkoushik,hiroakih}@cs.cmu.edu

More information

Lecture 11 Recurrent Neural Networks I

Lecture 11 Recurrent Neural Networks I Lecture 11 Recurrent Neural Networks I CMSC 35246: Deep Learning Shubhendu Trivedi & Risi Kondor University of Chicago May 01, 2017 Introduction Sequence Learning with Neural Networks Some Sequence Tasks

More information

Faster Training of Very Deep Networks Via p-norm Gates

Faster Training of Very Deep Networks Via p-norm Gates Faster Training of Very Deep Networks Via p-norm Gates Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh Center for Pattern Recognition and Data Analytics Deakin University, Geelong Australia Email:

More information

Lecture 15: Exploding and Vanishing Gradients

Lecture 15: Exploding and Vanishing Gradients Lecture 15: Exploding and Vanishing Gradients Roger Grosse 1 Introduction Last lecture, we introduced RNNs and saw how to derive the gradients using backprop through time. In principle, this lets us train

More information

Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville. - Cambridge, MA ; London, Spis treści

Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville. - Cambridge, MA ; London, Spis treści Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville. - Cambridge, MA ; London, 2017 Spis treści Website Acknowledgments Notation xiii xv xix 1 Introduction 1 1.1 Who Should Read This Book?

More information

Neural Networks Language Models

Neural Networks Language Models Neural Networks Language Models Philipp Koehn 10 October 2017 N-Gram Backoff Language Model 1 Previously, we approximated... by applying the chain rule p(w ) = p(w 1, w 2,..., w n ) p(w ) = i p(w i w 1,...,

More information

Learning Recurrent Neural Networks with Hessian-Free Optimization: Supplementary Materials

Learning Recurrent Neural Networks with Hessian-Free Optimization: Supplementary Materials Learning Recurrent Neural Networks with Hessian-Free Optimization: Supplementary Materials Contents 1 Pseudo-code for the damped Gauss-Newton vector product 2 2 Details of the pathological synthetic problems

More information

NEURAL LANGUAGE MODELS

NEURAL LANGUAGE MODELS COMP90042 LECTURE 14 NEURAL LANGUAGE MODELS LANGUAGE MODELS Assign a probability to a sequence of words Framed as sliding a window over the sentence, predicting each word from finite context to left E.g.,

More information

Better Conditional Language Modeling. Chris Dyer

Better Conditional Language Modeling. Chris Dyer Better Conditional Language Modeling Chris Dyer Conditional LMs A conditional language model assigns probabilities to sequences of words, w =(w 1,w 2,...,w`), given some conditioning context, x. As with

More information

Lecture 5 Neural models for NLP

Lecture 5 Neural models for NLP CS546: Machine Learning in NLP (Spring 2018) http://courses.engr.illinois.edu/cs546/ Lecture 5 Neural models for NLP Julia Hockenmaier juliahmr@illinois.edu 3324 Siebel Center Office hours: Tue/Thu 2pm-3pm

More information

Deep Learning Sequence to Sequence models: Attention Models. 17 March 2018

Deep Learning Sequence to Sequence models: Attention Models. 17 March 2018 Deep Learning Sequence to Sequence models: Attention Models 17 March 2018 1 Sequence-to-sequence modelling Problem: E.g. A sequence X 1 X N goes in A different sequence Y 1 Y M comes out Speech recognition:

More information

arxiv: v2 [cs.lg] 26 Jan 2016

arxiv: v2 [cs.lg] 26 Jan 2016 Stacked Attention Networks for Image Question Answering Zichao Yang 1, Xiaodong He 2, Jianfeng Gao 2, Li Deng 2, Alex Smola 1 1 Carnegie Mellon University, 2 Microsoft Research, Redmond, WA 98052, USA

More information

CS224N: Natural Language Processing with Deep Learning Winter 2018 Midterm Exam

CS224N: Natural Language Processing with Deep Learning Winter 2018 Midterm Exam CS224N: Natural Language Processing with Deep Learning Winter 2018 Midterm Exam This examination consists of 17 printed sides, 5 questions, and 100 points. The exam accounts for 20% of your total grade.

More information

Need for Deep Networks Perceptron. Can only model linear functions. Kernel Machines. Non-linearity provided by kernels

Need for Deep Networks Perceptron. Can only model linear functions. Kernel Machines. Non-linearity provided by kernels Need for Deep Networks Perceptron Can only model linear functions Kernel Machines Non-linearity provided by kernels Need to design appropriate kernels (possibly selecting from a set, i.e. kernel learning)

More information

Financial Risk and Returns Prediction with Modular Networked Learning

Financial Risk and Returns Prediction with Modular Networked Learning arxiv:1806.05876v1 [cs.lg] 15 Jun 2018 Financial Risk and Returns Prediction with Modular Networked Learning Carlos Pedro Gonçalves June 18, 2018 University of Lisbon, Instituto Superior de Ciências Sociais

More information

Generating Sequences with Recurrent Neural Networks

Generating Sequences with Recurrent Neural Networks Generating Sequences with Recurrent Neural Networks Alex Graves University of Toronto & Google DeepMind Presented by Zhe Gan, Duke University May 15, 2015 1 / 23 Outline Deep recurrent neural network based

More information

Combining Static and Dynamic Information for Clinical Event Prediction

Combining Static and Dynamic Information for Clinical Event Prediction Combining Static and Dynamic Information for Clinical Event Prediction Cristóbal Esteban 1, Antonio Artés 2, Yinchong Yang 1, Oliver Staeck 3, Enrique Baca-García 4 and Volker Tresp 1 1 Siemens AG and

More information

Deep Learning Recurrent Networks 2/28/2018

Deep Learning Recurrent Networks 2/28/2018 Deep Learning Recurrent Networks /8/8 Recap: Recurrent networks can be incredibly effective Story so far Y(t+) Stock vector X(t) X(t+) X(t+) X(t+) X(t+) X(t+5) X(t+) X(t+7) Iterated structures are good

More information

Machine Learning for Signal Processing Neural Networks Continue. Instructor: Bhiksha Raj Slides by Najim Dehak 1 Dec 2016

Machine Learning for Signal Processing Neural Networks Continue. Instructor: Bhiksha Raj Slides by Najim Dehak 1 Dec 2016 Machine Learning for Signal Processing Neural Networks Continue Instructor: Bhiksha Raj Slides by Najim Dehak 1 Dec 2016 1 So what are neural networks?? Voice signal N.Net Transcription Image N.Net Text

More information

Recurrent neural networks

Recurrent neural networks 12-1: Recurrent neural networks Prof. J.C. Kao, UCLA Recurrent neural networks Motivation Network unrollwing Backpropagation through time Vanishing and exploding gradients LSTMs GRUs 12-2: Recurrent neural

More information

COMPARING FIXED AND ADAPTIVE COMPUTATION TIME FOR RE-

COMPARING FIXED AND ADAPTIVE COMPUTATION TIME FOR RE- Workshop track - ICLR COMPARING FIXED AND ADAPTIVE COMPUTATION TIME FOR RE- CURRENT NEURAL NETWORKS Daniel Fojo, Víctor Campos, Xavier Giró-i-Nieto Universitat Politècnica de Catalunya, Barcelona Supercomputing

More information

Slide credit from Hung-Yi Lee & Richard Socher

Slide credit from Hung-Yi Lee & Richard Socher Slide credit from Hung-Yi Lee & Richard Socher 1 Review Recurrent Neural Network 2 Recurrent Neural Network Idea: condition the neural network on all previous words and tie the weights at each time step

More information

Neural Architectures for Image, Language, and Speech Processing

Neural Architectures for Image, Language, and Speech Processing Neural Architectures for Image, Language, and Speech Processing Karl Stratos June 26, 2018 1 / 31 Overview Feedforward Networks Need for Specialized Architectures Convolutional Neural Networks (CNNs) Recurrent

More information

CSC321 Lecture 16: ResNets and Attention

CSC321 Lecture 16: ResNets and Attention CSC321 Lecture 16: ResNets and Attention Roger Grosse Roger Grosse CSC321 Lecture 16: ResNets and Attention 1 / 24 Overview Two topics for today: Topic 1: Deep Residual Networks (ResNets) This is the state-of-the

More information

Lecture 11 Recurrent Neural Networks I

Lecture 11 Recurrent Neural Networks I Lecture 11 Recurrent Neural Networks I CMSC 35246: Deep Learning Shubhendu Trivedi & Risi Kondor niversity of Chicago May 01, 2017 Introduction Sequence Learning with Neural Networks Some Sequence Tasks

More information

Deep Learning Architectures and Algorithms

Deep Learning Architectures and Algorithms Deep Learning Architectures and Algorithms In-Jung Kim 2016. 12. 2. Agenda Introduction to Deep Learning RBM and Auto-Encoders Convolutional Neural Networks Recurrent Neural Networks Reinforcement Learning

More information

Recurrent Neural Network Training with Preconditioned Stochastic Gradient Descent

Recurrent Neural Network Training with Preconditioned Stochastic Gradient Descent Recurrent Neural Network Training with Preconditioned Stochastic Gradient Descent 1 Xi-Lin Li, lixilinx@gmail.com arxiv:1606.04449v2 [stat.ml] 8 Dec 2016 Abstract This paper studies the performance of

More information

Notes on Deep Learning for NLP

Notes on Deep Learning for NLP Notes on Deep Learning for NLP Antoine J.-P. Tixier Computer Science Department (DaSciM team) École Polytechnique, Palaiseau, France antoine.tixier-1@colorado.edu Last updated Thursday 30 th August, 2018

More information

TTIC 31230, Fundamentals of Deep Learning David McAllester, April Vanishing and Exploding Gradients. ReLUs. Xavier Initialization

TTIC 31230, Fundamentals of Deep Learning David McAllester, April Vanishing and Exploding Gradients. ReLUs. Xavier Initialization TTIC 31230, Fundamentals of Deep Learning David McAllester, April 2017 Vanishing and Exploding Gradients ReLUs Xavier Initialization Batch Normalization Highway Architectures: Resnets, LSTMs and GRUs Causes

More information

Introduction to RNNs!

Introduction to RNNs! Introduction to RNNs Arun Mallya Best viewed with Computer Modern fonts installed Outline Why Recurrent Neural Networks (RNNs)? The Vanilla RNN unit The RNN forward pass Backpropagation refresher The RNN

More information

Spatial Transformation

Spatial Transformation Spatial Transformation Presented by Liqun Chen June 30, 2017 1 Overview 2 Spatial Transformer Networks 3 STN experiments 4 Recurrent Models of Visual Attention (RAM) 5 Recurrent Models of Visual Attention

More information

Synaptic Devices and Neuron Circuits for Neuron-Inspired NanoElectronics

Synaptic Devices and Neuron Circuits for Neuron-Inspired NanoElectronics Synaptic Devices and Neuron Circuits for Neuron-Inspired NanoElectronics Byung-Gook Park Inter-university Semiconductor Research Center & Department of Electrical and Computer Engineering Seoul National

More information

arxiv: v3 [cs.lg] 25 Oct 2017

arxiv: v3 [cs.lg] 25 Oct 2017 Gated Orthogonal Recurrent Units: On Learning to Forget Li Jing 1, Caglar Gulcehre 2, John Peurifoy 1, Yichen Shen 1, Max Tegmark 1, Marin Soljačić 1, Yoshua Bengio 2 1 Massachusetts Institute of Technology,

More information

Machine Learning for Large-Scale Data Analysis and Decision Making A. Neural Networks Week #6

Machine Learning for Large-Scale Data Analysis and Decision Making A. Neural Networks Week #6 Machine Learning for Large-Scale Data Analysis and Decision Making 80-629-17A Neural Networks Week #6 Today Neural Networks A. Modeling B. Fitting C. Deep neural networks Today s material is (adapted)

More information

Demystifying deep learning. Artificial Intelligence Group Department of Computer Science and Technology, University of Cambridge, UK

Demystifying deep learning. Artificial Intelligence Group Department of Computer Science and Technology, University of Cambridge, UK Demystifying deep learning Petar Veličković Artificial Intelligence Group Department of Computer Science and Technology, University of Cambridge, UK London Data Science Summit 20 October 2017 Introduction

More information

CSC321 Lecture 10 Training RNNs

CSC321 Lecture 10 Training RNNs CSC321 Lecture 10 Training RNNs Roger Grosse and Nitish Srivastava February 23, 2015 Roger Grosse and Nitish Srivastava CSC321 Lecture 10 Training RNNs February 23, 2015 1 / 18 Overview Last time, we saw

More information

Deep Learning for Natural Language Processing. Sidharth Mudgal April 4, 2017

Deep Learning for Natural Language Processing. Sidharth Mudgal April 4, 2017 Deep Learning for Natural Language Processing Sidharth Mudgal April 4, 2017 Table of contents 1. Intro 2. Word Vectors 3. Word2Vec 4. Char Level Word Embeddings 5. Application: Entity Matching 6. Conclusion

More information

MACHINE LEARNING AND PATTERN RECOGNITION Fall 2005, Lecture 4 Gradient-Based Learning III: Architectures Yann LeCun

MACHINE LEARNING AND PATTERN RECOGNITION Fall 2005, Lecture 4 Gradient-Based Learning III: Architectures Yann LeCun Y. LeCun: Machine Learning and Pattern Recognition p. 1/3 MACHINE LEARNING AND PATTERN RECOGNITION Fall 2005, Lecture 4 Gradient-Based Learning III: Architectures Yann LeCun The Courant Institute, New

More information

RECURRENT NEURAL NETWORKS WITH FLEXIBLE GATES USING KERNEL ACTIVATION FUNCTIONS

RECURRENT NEURAL NETWORKS WITH FLEXIBLE GATES USING KERNEL ACTIVATION FUNCTIONS 2018 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 17 20, 2018, AALBORG, DENMARK RECURRENT NEURAL NETWORKS WITH FLEXIBLE GATES USING KERNEL ACTIVATION FUNCTIONS Simone Scardapane,

More information

Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction

Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction Feng Qian,LeiSha, Baobao Chang, Zhifang Sui Institute of Computational Linguistics, Peking

More information

Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation

Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation Dr. Yanjun Qi Department of Computer Science University of Virginia Tutorial @ ACM BCB-2018 8/29/18 Yanjun Qi / UVA

More information

EE-559 Deep learning LSTM and GRU

EE-559 Deep learning LSTM and GRU EE-559 Deep learning 11.2. LSTM and GRU François Fleuret https://fleuret.org/ee559/ Mon Feb 18 13:33:24 UTC 2019 ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE The Long-Short Term Memory unit (LSTM) by Hochreiter

More information

WHY ARE DEEP NETS REVERSIBLE: A SIMPLE THEORY,

WHY ARE DEEP NETS REVERSIBLE: A SIMPLE THEORY, WHY ARE DEEP NETS REVERSIBLE: A SIMPLE THEORY, WITH IMPLICATIONS FOR TRAINING Sanjeev Arora, Yingyu Liang & Tengyu Ma Department of Computer Science Princeton University Princeton, NJ 08540, USA {arora,yingyul,tengyu}@cs.princeton.edu

More information

Lecture 15: Recurrent Neural Nets

Lecture 15: Recurrent Neural Nets Lecture 15: Recurrent Neural Nets Roger Grosse 1 Introduction Most of the prediction tasks we ve looked at have involved pretty simple kinds of outputs, such as real values or discrete categories. But

More information

Based on the original slides of Hung-yi Lee

Based on the original slides of Hung-yi Lee Based on the original slides of Hung-yi Lee New Activation Function Rectified Linear Unit (ReLU) σ z a a = z Reason: 1. Fast to compute 2. Biological reason a = 0 [Xavier Glorot, AISTATS 11] [Andrew L.

More information

On the use of Long-Short Term Memory neural networks for time series prediction

On the use of Long-Short Term Memory neural networks for time series prediction On the use of Long-Short Term Memory neural networks for time series prediction Pilar Gómez-Gil National Institute of Astrophysics, Optics and Electronics ccc.inaoep.mx/~pgomez In collaboration with: J.

More information

Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) Long Short-Term Memory (LSTM) A brief introduction Daniel Renshaw 24th November 2014 1 / 15 Context and notation Just to give the LSTM something to do: neural network language modelling Vocabulary, size

More information

Part-of-Speech Tagging + Neural Networks 3: Word Embeddings CS 287

Part-of-Speech Tagging + Neural Networks 3: Word Embeddings CS 287 Part-of-Speech Tagging + Neural Networks 3: Word Embeddings CS 287 Review: Neural Networks One-layer multi-layer perceptron architecture, NN MLP1 (x) = g(xw 1 + b 1 )W 2 + b 2 xw + b; perceptron x is the

More information

Towards Universal Sentence Embeddings

Towards Universal Sentence Embeddings Towards Universal Sentence Embeddings Towards Universal Paraphrastic Sentence Embeddings J. Wieting, M. Bansal, K. Gimpel and K. Livescu, ICLR 2016 A Simple But Tough-To-Beat Baseline For Sentence Embeddings

More information

A QUESTION ANSWERING SYSTEM USING ENCODER-DECODER, SEQUENCE-TO-SEQUENCE, RECURRENT NEURAL NETWORKS. A Project. Presented to

A QUESTION ANSWERING SYSTEM USING ENCODER-DECODER, SEQUENCE-TO-SEQUENCE, RECURRENT NEURAL NETWORKS. A Project. Presented to A QUESTION ANSWERING SYSTEM USING ENCODER-DECODER, SEQUENCE-TO-SEQUENCE, RECURRENT NEURAL NETWORKS A Project Presented to The Faculty of the Department of Computer Science San José State University In

More information

Memory-Augmented Attention Model for Scene Text Recognition

Memory-Augmented Attention Model for Scene Text Recognition Memory-Augmented Attention Model for Scene Text Recognition Cong Wang 1,2, Fei Yin 1,2, Cheng-Lin Liu 1,2,3 1 National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences

More information

Task-Oriented Dialogue System (Young, 2000)

Task-Oriented Dialogue System (Young, 2000) 2 Review Task-Oriented Dialogue System (Young, 2000) 3 http://rsta.royalsocietypublishing.org/content/358/1769/1389.short Speech Signal Speech Recognition Hypothesis are there any action movies to see

More information

Learning to Query, Reason, and Answer Questions On Ambiguous Texts

Learning to Query, Reason, and Answer Questions On Ambiguous Texts Learning to Query, Reason, and Answer Questions On Ambiguous Texts Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P. Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerald Tesauro, Satinder

More information

arxiv: v1 [stat.ml] 29 Jul 2017

arxiv: v1 [stat.ml] 29 Jul 2017 ORHOGONAL RECURREN NEURAL NEWORKS WIH SCALED CAYLEY RANSFORM Kyle E. Helfrich, Devin Willmott, & Qiang Ye Department of Mathematics University of Kentucky Lexington, KY 40506, USA {kyle.helfrich,devin.willmott,qye3}@uky.edu

More information

Conditional Language Modeling. Chris Dyer

Conditional Language Modeling. Chris Dyer Conditional Language Modeling Chris Dyer Unconditional LMs A language model assigns probabilities to sequences of words,. w =(w 1,w 2,...,w`) It is convenient to decompose this probability using the chain

More information

Stephen Scott.

Stephen Scott. 1 / 35 (Adapted from Vinod Variyam and Ian Goodfellow) sscott@cse.unl.edu 2 / 35 All our architectures so far work on fixed-sized inputs neural networks work on sequences of inputs E.g., text, biological

More information

Seq2Tree: A Tree-Structured Extension of LSTM Network

Seq2Tree: A Tree-Structured Extension of LSTM Network Seq2Tree: A Tree-Structured Extension of LSTM Network Weicheng Ma Computer Science Department, Boston University 111 Cummington Mall, Boston, MA wcma@bu.edu Kai Cao Cambia Health Solutions kai.cao@cambiahealth.com

More information

Recurrent Neural Networks. deeplearning.ai. Why sequence models?

Recurrent Neural Networks. deeplearning.ai. Why sequence models? Recurrent Neural Networks deeplearning.ai Why sequence models? Examples of sequence data The quick brown fox jumped over the lazy dog. Speech recognition Music generation Sentiment classification There

More information

arxiv: v1 [cs.cl] 31 May 2015

arxiv: v1 [cs.cl] 31 May 2015 Recurrent Neural Networks with External Memory for Language Understanding Baolin Peng 1, Kaisheng Yao 2 1 The Chinese University of Hong Kong 2 Microsoft Research blpeng@se.cuhk.edu.hk, kaisheny@microsoft.com

More information

arxiv: v1 [cs.cl] 21 May 2017

arxiv: v1 [cs.cl] 21 May 2017 Spelling Correction as a Foreign Language Yingbo Zhou yingbzhou@ebay.com Utkarsh Porwal uporwal@ebay.com Roberto Konow rkonow@ebay.com arxiv:1705.07371v1 [cs.cl] 21 May 2017 Abstract In this paper, we

More information

arxiv: v1 [cs.ai] 19 May 2017

arxiv: v1 [cs.ai] 19 May 2017 Model-Based Planning in Discrete Action Spaces Mikael Henaff Courant Institute, New York University mbh305@nyu.edu William F. Whitney Courant Institute, New York University ww1114@nyu.edu arxiv:1705.07177v1

More information

Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries

Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries INTERSPEECH 2017 August 20 24, 2017, Stockholm, Sweden Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries Yu-Hsuan Wang, Cheng-Tao Chung, Hung-yi

More information

Graph Memory Networks for Molecular Activity Prediction

Graph Memory Networks for Molecular Activity Prediction Graph Memory Networks for Molecular Activity Prediction Trang Pham, Truyen Tran, Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University, Geelong, Australia {phtra, truyen.tran,

More information

Sequence Models. Ji Yang. Department of Computing Science, University of Alberta. February 14, 2018

Sequence Models. Ji Yang. Department of Computing Science, University of Alberta. February 14, 2018 Sequence Models Ji Yang Department of Computing Science, University of Alberta February 14, 2018 This is a note mainly based on Prof. Andrew Ng s MOOC Sequential Models. I also include materials (equations,

More information