PPT-Mimicking deep neural networks with shallow and narrow ones

ISHAY BEERY ELAD KNOLL OUTLINES Motivation Model c ompression mimicking large networks FITNETS HINTS FOR THIN DEEP NETS A Romero 2014 DO DEEP NETS REALLY NEED TO BE DEEP Rich Caruana amp Lei Jimmy Ba 2014. Find 318 145 Step Show 318 Subtract the ones Step Regroup 1 hundred as 10 tens Subtract the tens 11 tens 4 tens Step Subtract the hundreds 2 hundreds 1 hundred Use charts Subtract 1 2 3 4 Writing and Reasoning How do you show that you have regro PUBLIC SWIM FEES ARE LISTED BELOW 1145 AM 115 PM Rec Fitness 1145 AM115 PM WHOLE POOL Rec Fitness 1145 AM115 PM WHOLE POOL Rec Fitness 1145 AM115 PM WHOLE POOL Rec Fitness 1145 AM115 PM WHOLE POOL Longcourse Rec Fitness 1145 AM115 PM WHOLE POOL withthefollowingstructuralproperties:1)eachrowconsistsof “ones”;2)eachcolumnconsistsof “ones”;3)thenumberof“ones”incommonbetweenanytwocolumns, ,isnogreaterthan ;4)both an None Narrow Narrow Broad Broad Broad Narrow Broad Narrow None Narrow Narrow BroadNone Broad None Narrow Broad None Broad None Broad NarrowNoneNarrowNarrow Narrow Broad Narrow NarrowBroad Narrow Broad Deep Learning. Zhiting. Hu. 2014-4-1. Outline. Motivation: why go deep?. DL since 2006. Some DL Models. Discussion. 2. Outline. Motivation: why go deep?. DL since 2006. Some DL Models. Discussion. 3. 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. 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. 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. Dongwoo Lee. University of Illinois at Chicago . CSUN (Complex and Sustainable Urban Networks Laboratory). Contents. Concept. Data . Methodologies. Analytical Process. Results. Limitations and Conclusion. Introduction 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Hinton’s Brief History of Machine Learning. What was hot in 1987?. Fall 2018/19. 7. Recurrent Neural Networks. (Some figures adapted from . NNDL book. ). Recurrent Neural Networks. Noriko Tomuro. 2. Recurrent Neural Networks (RNNs). RNN Training. Loss Minimization. Bidirectional RNNs. Introduction to Back Propagation Neural . Networks BPNN. By KH Wong. Neural Networks Ch9. , ver. 8d. 1. Introduction. Neural Network research is are very . hot. . A high performance Classifier (multi-class). Dr David Wong. (With thanks to Dr Gari Clifford, G.I.T). The Multi-Layer Perceptron. single layer can only deal with linearly separable data. Composed of many connected neurons . Three general layers; . . 循环神经网络. Neural Networks. Recurrent Neural Networks. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence..