DEEP LEARNING DEEP STRUCTURED LEARNING LEARNING DEEP MACHINE BRANCH MACHINE LEARNING ALGORITHM ATTEMPT MODEL DATA GRAPH MULTIPLE PROCESSING LINEAR LINEAR TRANSFORMATION FAMILY LEARNING REPRESENTATION OBSERVATION VECTOR INTENSITY VALUES ABSTRACT REGIONS PARTICULAR REPRESENTATIONS BETTER THAN TASK FACE RECOGNITION FACIAL EXPRESSION PROMISES HANDCRAFTED FEATURES EFFICIENT ALGORITHMS UNSUPERVISED FEATURE LEARNING FEATURE EXTRACTION RESEARCH AREA ATTEMPTS CREATE LEARN INSPIRED NEUROSCIENCE ON INTERPRETATION INFORMATION PROCESSING COMMUNICATION PATTERNS NERVOUS SYSTEM NEURAL CODING DEFINE RELATIONSHIP BETWEEN NEURONAL THE BRAIN DEEP NEURAL NETWORK DEEP NEURAL NETWORKS DEEP BELIEF NETWORK RECURRENT NEURAL NETWORK FIELDS COMPUTER VISION AUTOMATIC SPEECH NATURAL LANGUAGE PROCESSING AUDIO BIOINFORMATICS PRODUCE RESULTS BUZZWORD REBRANDING NEURAL NETWORK NUMBER THE FIELD RINA DECHTER CONCEPTS ORDER SECOND ORDER THE CONTEXT CONSTRAINT SATISFACTION CLASS CASCADE LAYERS NONLINEAR UNITS LAYER USES OUTPUT PREVIOUS APPLICATIONS PATTERN ANALYSIS CLASSIFICATION HIGHER LEVEL DERIVED LEVEL FIELD ABSTRACTION HIERARCHY DEFINITIONS IN COMMON UNSUPERVISED LEARNING FEATURE FORMING COMPOSITION OF DEPENDS PROBLEM IN DEEP HIDDEN ARTIFICIAL NEURAL NETWORK PROPOSITIONAL FORMULA ORGANIZED NODES BELIEF NETWORKS DEEP BOLTZMANN MACHINES TRANSFORM THE SIGNAL ARTIFICIAL NEURON WHOSE PARAMETERS LEARNED CHAIN TRANSFORMATIONS INPUT CREDIT ASSIGNMENT PATH CAPS DESCRIBE POTENTIALLY CAUSAL CONNECTIONS INPUT AND OUTPUT FEEDFORWARD NEURAL NETWORK DEPTH THUS PLUS SIGNAL PROPAGATE UNLIMITED THRESHOLD RESEARCHERS UNDERLYING BEHIND INTERACTIONS FACTORS ADDS THE ASSUMPTION NUMBERS BE USED TO EXPLOITS IDEA EXPLANATORY ABSTRACT CONCEPTS PICK USEFUL SUPERVISED LEARNING FEATURE ENGINEERING COMPACT AKIN PRINCIPAL COMPONENTS DERIVE STRUCTURES REMOVE BENEFIT DEEP STRUCTURES TRAINED MANNER NEURAL COMPRESSORS DEEP BELIEF NETWORKS GENERALLY INTERPRETED TERMS UNIVERSAL APPROXIMATION THEOREM THE UNIVERSAL APPROXIMATION CAPACITY FEEDFORWARD NEURAL NETWORKS SIZE CONTINUOUS FUNCTIONS PROOF PUBLISHED GEORGE CYBENKO ACTIVATION FUNCTIONS GENERALISED KURT INTERPRETATION OPTIMIZATION TRAINING TESTING RELATED GENERALIZATION NONLINEARITY CUMULATIVE DISTRIBUTION FUNCTION INTRODUCTION DROPOUT NEURAL NETWORKS POPULARIZED GEOFF HINTON YOSHUA BENGIO YANN LECUN JUERGEN SCHMIDHUBER LEARNING ALGORITHM FEEDFORWARD PERCEPTRONS LAPA PAPER ALREADY NETWORK THE GROUP METHOD HANDLING POPULAR THE CURRENT IDEAS COMPUTER IDENTIFICATION SYSTEM ALPHA LEARNING PROCESS ARTIFICIAL NEURAL NETWORKS DATE NEOCOGNITRON KUNIHIKO FUKUSHIMA THEMSELVES CHALLENGE HOW TO TRAIN ABLE APPLY THE STANDARD BACKPROPAGATION REVERSE MODE AUTOMATIC DIFFERENTIATION PURPOSE RECOGNIZING HANDWRITTEN ZIP CODE SUCCESS THE ALGORITHM THE NETWORK DATASET GENERAL COMPRESSOR STACK RECURRENT NEURAL NETWORKS BRENDAN FREY POSSIBLE FULLY CONNECTED HUNDRED PETER DAYAN GEOFFREY HINTON SLOW ONE BEING THE VANISHING GRADIENT SEPP HOCHREITER DONE MATCHING IMAGES OBJECT MODEL WENG HUMAN BRAIN PERFORMING OBJECT RECOGNITION SIMILAR PROGRAMMER OPEN CONVOLUTION KERNEL OBJECT SCENE MAX POOLING IMAGENET REDUCE THE POSITION RESOLUTION FACTOR THE CASCADE SIMPLER GABOR FILTER SUPPORT VECTOR MACHINE CHOICE COMPUTATIONAL COST LACK UNDERSTANDING BIOLOGICAL HISTORY OF SPEECH RECOGNITION RECURRENT MANY YEARS NEVER GAUSSIAN MIXTURE MODEL HIDDEN MARKOV MODEL TECHNOLOGY SPEECH WEAK CORRELATION STRUCTURE PREDICTIVE MODELS TRAINING DATA WEAKER COMPUTING POWER UNDERSTOOD BARRIERS AWAY NEURAL NETS EXCEPTION SRI INTERNATIONAL THE US GOVERNMENTS DARPA CONDUCTED SPEAKER RECOGNITION THE SPEAKER SPEECH PROCESSING THE JOURNAL OF SPEECH COMMUNICATION HINTON DENG RECENT COLLABORATION EACH OTHER COLLEAGUES RENAISSANCE TAKEN LONG SHORT TERM MEMORY LSTM MEMORIES EVENTS THOUSANDS DISCRETE STEPS COMPETITIVE TRADITIONAL RECOGNIZERS CERTAIN STACKS EXPERIENCED DRAMATIC PERFORMANCE JUMP AVAILABLE GOOGLE VOICE SMARTPHONE SHOW CASE THE EXPRESSION THE MACHINE LEARNING COMMUNITY IGOR GOOGLE NGRAM CHART SHOWS USAGE TERM TRACTION PUBLICATION RUSLAN DREW ATTENTION SHOWING EFFECTIVELY TREATING TURN RESTRICTED BOLTZMANN MACHINE THE MORE GENERAL CASE HIERARCHIES EXPERIMENTALLY BENEFITS SPEEDING RESURGENCE SYSTEMS PARTICULARLY EVALUATION TIMIT MNIST IMAGE CLASSIFICATION RANGE RECENTLY CONVOLUTIONAL NEURAL NETWORK NEARLY MODERN SCALE THE REAL IMPACT INDUSTRY APPARENTLY THE EARLY THE CHECKS WRITTEN IN INDUSTRIAL APPLICATIONS OF WORK MICROSOFT THE NIPS WORKSHOP THE WORKSHOP LIMITATIONS THE POSSIBILITY BELIEVED BELIEF NETS DISCOVERED ERROR RATES DRAMATICALLY ADVANCED GENERATIVE MODEL FINDING VERIFIED MAJOR NATURE ERRORS OFFERING TECHNICAL INSIGHTS INTEGRATE EXISTING HIGHLY EFFICIENT DECODING PLAYERS THE HISTORY OF DEVELOPMENT BOOKS HARDWARE ENABLING INTEREST POWERFUL GRAPHICS PROCESSING UNIT KIND MATH INVOLVED GPUS SPEED UP ORDERS RUNNING TIMES WEEKS INVOLVE NOBEL LAUREATE DAVID HUBEL TORSTEN WIESEL CELLS PRIMARY VISUAL CORTEX SIMPLE CELL COMPLEX CELL VIEWED CASCADING CELL CONVOLUTION CONVOLUTI