Unnamed: 0.1 int64 0 41k | Unnamed: 0 int64 0 41k | author stringlengths 9 1.39k | id stringlengths 11 18 | summary stringlengths 25 3.66k | title stringlengths 4 258 | year int64 1.99k 2.02k | arxiv_url stringlengths 32 39 | info stringlengths 523 3.18k | embeddings stringlengths 16.9k 17.1k |
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800 | 800 | ['Kamil Rocki'] | 1603.08262v1 | There exists a theory of a single general-purpose learning algorithm which
could explain the principles of its operation. This theory assumes that the
brain has some initial rough architecture, a small library of simple innate
circuits which are prewired at birth and proposes that all significant mental
algorithms can ... | Towards Machine Intelligence | 2,016 | http://arxiv.org/pdf/1603.08262v1 | Title Towards Machine Intelligence Summary exists theory single generalpurpose learning algorithm could explain principle operation theory assumes brain initial rough architecture small library simple innate circuit prewired birth proposes significant mental algorithm learned Given current understanding observation pap... | [-0.017351990565657616, 0.024881742894649506, -0.04097719117999077, -0.012972159311175346, -0.008411930873990059, 0.01303438562899828, 0.0025483272038400173, -0.003180443774908781, 0.003136167535558343, 0.001992369769141078, 0.021276718005537987, 0.034689806401729584, 0.0003844456223305315, 0.04120863974094391, 0.06122... |
801 | 801 | ['Aravind S. Lakshminarayanan', 'Sahil Sharma', 'Balaraman Ravindran'] | 1605.05365v2 | Deep Reinforcement Learning methods have achieved state of the art
performance in learning control policies for the games in the Atari 2600
domain. One of the important parameters in the Arcade Learning Environment
(ALE) is the frame skip rate. It decides the granularity at which agents can
control game play. A frame s... | Dynamic Frame skip Deep Q Network | 2,016 | http://arxiv.org/pdf/1605.05365v2 | Title Dynamic Frame skip Deep Q Network Summary Deep Reinforcement Learning method achieved state art performance learning control policy game Atari 2600 domain One important parameter Arcade Learning Environment ALE frame skip rate decides granularity agent control game play frame skip value k allows agent repeat sele... | [-0.020967982709407806, 0.025493472814559937, -0.03489236533641815, -0.02994390018284321, 0.0019058444304391742, 0.0023948538582772017, 0.02895328775048256, -0.0014554777881130576, -0.05222182348370552, 0.022573666647076607, -0.02657044120132923, 0.008481152355670929, -0.027061982080340385, 0.10703840106725693, 0.01127... |
802 | 802 | ['Matko Bošnjak', 'Tim Rocktäschel', 'Jason Naradowsky', 'Sebastian Riedel'] | 1605.06640v3 | Given that in practice training data is scarce for all but a small set of
problems, a core question is how to incorporate prior knowledge into a model.
In this paper, we consider the case of prior procedural knowledge for neural
networks, such as knowing how a program should traverse a sequence, but not
what local acti... | Programming with a Differentiable Forth Interpreter | 2,016 | http://arxiv.org/pdf/1605.06640v3 | Title Programming Differentiable Forth Interpreter Summary Given practice training data scarce small set problem core question incorporate prior knowledge model paper consider case prior procedural knowledge neural network knowing program traverse sequence local action performed step end present endtoend differentiable... | [0.015784751623868942, 0.02968372032046318, -0.02548852376639843, 0.03009185940027237, -0.0074228448793292046, 0.014865677803754807, -0.004632242955267429, -0.026738736778497696, -0.010036690160632133, -0.0015159444883465767, -0.00042609203956089914, 0.030986011028289795, 0.02082044444978237, 0.09317515045404434, 0.002... |
803 | 803 | ['Luka Crnkovic-Friis', 'Louise Crnkovic-Friis'] | 1605.06921v1 | Recent advances in deep learning have enabled the extraction of high-level
features from raw sensor data which has opened up new possibilities in many
different fields, including computer generated choreography. In this paper we
present a system chor-rnn for generating novel choreographic material in the
nuanced choreo... | Generative Choreography using Deep Learning | 2,016 | http://arxiv.org/pdf/1605.06921v1 | Title Generative Choreography using Deep Learning Summary Recent advance deep learning enabled extraction highlevel feature raw sensor data opened new possibility many different field including computer generated choreography paper present system chorrnn generating novel choreographic material nuanced choreographic lan... | [-0.014845410361886024, 0.01873498037457466, -0.0018858957337215543, 0.01969604566693306, -0.0060719698667526245, -0.0038841378409415483, 0.010183590464293957, 0.007069035898894072, -0.02876858413219452, 0.01212883461266756, -0.04341796785593033, -0.028224367648363113, 0.0031514095608145, 0.08033016324043274, 0.0364874... |
804 | 804 | ['Luciano Serafini', "Artur d'Avila Garcez"] | 1606.04422v2 | We propose Logic Tensor Networks: a uniform framework for integrating
automatic learning and reasoning. A logic formalism called Real Logic is
defined on a first-order language whereby formulas have truth-value in the
interval [0,1] and semantics defined concretely on the domain of real numbers.
Logical constants are i... | Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and
Knowledge | 2,016 | http://arxiv.org/pdf/1606.04422v2 | Title Logic Tensor Networks Deep Learning Logical Reasoning Data Knowledge Summary propose Logic Tensor Networks uniform framework integrating automatic learning reasoning logic formalism called Real Logic defined firstorder language whereby formula truthvalue interval 01 semantics defined concretely domain real number... | [-0.005493875592947006, 0.061042167246341705, -0.020459052175283432, 0.0005539292469620705, -0.03672215715050697, 0.006175587419420481, 0.04792485013604164, 0.003593750298023224, 0.014896784909069538, -0.02431536838412285, 0.050967440009117126, 0.044971566647291183, -0.0448899008333683, 0.0772092193365097, -0.027581647... |
805 | 805 | ['Randal S. Olson', 'Jason H. Moore'] | 1607.08878v1 | As data science continues to grow in popularity, there will be an increasing
need to make data science tools more scalable, flexible, and accessible. In
particular, automated machine learning (AutoML) systems seek to automate the
process of designing and optimizing machine learning pipelines. In this
chapter, we presen... | Identifying and Harnessing the Building Blocks of Machine Learning
Pipelines for Sensible Initialization of a Data Science Automation Tool | 2,016 | http://arxiv.org/pdf/1607.08878v1 | Title Identifying Harnessing Building Blocks Machine Learning Pipelines Sensible Initialization Data Science Automation Tool Summary data science continues grow popularity increasing need make data science tool scalable flexible accessible particular automated machine learning AutoML system seek automate process design... | [-0.006545540411025286, 0.025100000202655792, -0.06344747543334961, -0.031780652701854706, -0.002485371194779873, 0.033455368131399155, 0.04328327625989914, -0.003430407028645277, 0.03076452575623989, -0.03438777104020119, 0.051863860338926315, 0.027494585141539574, -0.0013480602065101266, 0.124763622879982, 0.01202288... |
806 | 806 | ['Karan K. Budhraja', 'Tim Oates'] | 1608.02971v1 | The problem of Learning from Demonstration is targeted at learning to perform
tasks based on observed examples. One approach to Learning from Demonstration
is Inverse Reinforcement Learning, in which actions are observed to infer
rewards. This work combines a feature based state evaluation approach to
Inverse Reinforce... | Neuroevolution-Based Inverse Reinforcement Learning | 2,016 | http://arxiv.org/pdf/1608.02971v1 | Title NeuroevolutionBased Inverse Reinforcement Learning Summary problem Learning Demonstration targeted learning perform task based observed example One approach Learning Demonstration Inverse Reinforcement Learning action observed infer reward work combine feature based state evaluation approach Inverse Reinforcement... | [0.015042547136545181, 0.004591308068484068, -0.005603528581559658, -0.045446816831827164, -0.016664888709783554, -0.04285820201039314, -0.03574316203594208, 0.02535259909927845, -0.057598140090703964, 0.013222440145909786, -0.04284298047423363, 0.04760914668440819, -0.013777272775769234, 0.0709560215473175, 0.06078116... |
807 | 807 | ['Alexander L. Gaunt', 'Marc Brockschmidt', 'Rishabh Singh', 'Nate Kushman', 'Pushmeet Kohli', 'Jonathan Taylor', 'Daniel Tarlow'] | 1608.04428v1 | We study machine learning formulations of inductive program synthesis; given
input-output examples, we try to synthesize source code that maps inputs to
corresponding outputs. Our aims are to develop new machine learning approaches
based on neural networks and graphical models, and to understand the
capabilities of mac... | TerpreT: A Probabilistic Programming Language for Program Induction | 2,016 | http://arxiv.org/pdf/1608.04428v1 | Title TerpreT Probabilistic Programming Language Program Induction Summary study machine learning formulation inductive program synthesis given inputoutput example try synthesize source code map input corresponding output aim develop new machine learning approach based neural network graphical model understand capabili... | [-0.01722017489373684, 0.017943255603313446, -0.027007954195141792, -0.010089694522321224, -0.024836236611008644, -0.023966439068317413, -0.04823090881109238, -0.02402658388018608, 0.0008894599159248173, -0.03042583353817463, 0.08167152106761932, 0.08090608566999435, 0.025328921154141426, 0.10649725049734116, 0.0158349... |
808 | 808 | ['Rajasekar Venkatesan', 'Meng Joo Er'] | 1608.08435v1 | In this paper, an Extreme Learning Machine (ELM) based technique for
Multi-label classification problems is proposed and discussed. In multi-label
classification, each of the input data samples belongs to one or more than one
class labels. The traditional binary and multi-class classification problems
are the subset of... | Multi-Label Classification Method Based on Extreme Learning Machines | 2,016 | http://arxiv.org/pdf/1608.08435v1 | Title MultiLabel Classification Method Based Extreme Learning Machines Summary paper Extreme Learning Machine ELM based technique Multilabel classification problem proposed discussed multilabel classification input data sample belongs one one class label traditional binary multiclass classification problem subset multi... | [0.03651493787765503, -0.03894156590104103, -0.02149415761232376, -0.016799520701169968, 0.014798006042838097, 0.04487765580415726, 0.0054421694949269295, 0.023157594725489616, 0.021656595170497894, -0.04483086243271828, -0.013578305952250957, 0.056093357503414154, -0.013931783847510815, 0.01588790863752365, 0.01021040... |
809 | 809 | ['Rajasekar Venkatesan', 'Meng Joo Er', 'Shiqian Wu', 'Mahardhika Pratama'] | 1608.08905v1 | In this paper, a novel extreme learning machine based online multi-label
classifier for real-time data streams is proposed. Multi-label classification
is one of the actively researched machine learning paradigm that has gained
much attention in the recent years due to its rapidly increasing real world
applications. In ... | A Novel Online Real-time Classifier for Multi-label Data Streams | 2,016 | http://arxiv.org/pdf/1608.08905v1 | Title Novel Online Realtime Classifier Multilabel Data Streams Summary paper novel extreme learning machine based online multilabel classifier realtime data stream proposed Multilabel classification one actively researched machine learning paradigm gained much attention recent year due rapidly increasing real world app... | [-0.006298888940364122, 0.0011950135231018066, -0.015283727087080479, -0.010427319444715977, 0.01822822168469429, 0.033135946840047836, 0.03005293756723404, -0.00841550249606371, 0.024135859683156013, -0.03136824443936348, 0.005537543911486864, 0.0399785116314888, -0.029923059046268463, 0.07971092313528061, 0.021839916... |
810 | 810 | ['Rajasekar Venkatesan', 'Meng Joo Er'] | 1609.00085v2 | In this paper, a progressive learning technique for multi-class
classification is proposed. This newly developed learning technique is
independent of the number of class constraints and it can learn new classes
while still retaining the knowledge of previous classes. Whenever a new class
(non-native to the knowledge le... | A Novel Progressive Learning Technique for Multi-class Classification | 2,016 | http://arxiv.org/pdf/1609.00085v2 | Title Novel Progressive Learning Technique Multiclass Classification Summary paper progressive learning technique multiclass classification proposed newly developed learning technique independent number class constraint learn new class still retaining knowledge previous class Whenever new class nonnative knowledge lear... | [-0.009050623513758183, 0.07899542897939682, -0.013789976015686989, -0.0008588870405219495, -0.010595673695206642, -0.0043508633971214294, 0.03322456777095795, -0.007411185186356306, -0.022002218291163445, -0.026269644498825073, 0.01577657274901867, 0.0244437325745821, 0.007352571468800306, -0.005320791155099869, 0.016... |
811 | 811 | ['Rajasekar Venkatesan', 'Meng Joo Er', 'Mihika Dave', 'Mahardhika Pratama', 'Shiqian Wu'] | 1609.00086v1 | In this paper, a high-speed online neural network classifier based on extreme
learning machines for multi-label classification is proposed. In multi-label
classification, each of the input data sample belongs to one or more than one
of the target labels. The traditional binary and multi-class classification
where each ... | A novel online multi-label classifier for high-speed streaming data
applications | 2,016 | http://arxiv.org/pdf/1609.00086v1 | Title novel online multilabel classifier highspeed streaming data application Summary paper highspeed online neural network classifier based extreme learning machine multilabel classification proposed multilabel classification input data sample belongs one one target label traditional binary multiclass classification s... | [-0.0017909012967720628, 0.011116793379187584, -0.012937987223267555, -0.017560698091983795, 0.01685900054872036, 0.02884375862777233, 0.020700978115200996, 0.016809318214654922, 0.024953285232186317, -0.033660922199487686, -0.008609489537775517, 0.03520015627145767, -0.02639882266521454, 0.08068560063838959, 0.0044535... |
812 | 812 | ['Hande Alemdar', 'Vincent Leroy', 'Adrien Prost-Boucle', 'Frédéric Pétrot'] | 1609.00222v2 | The computation and storage requirements for Deep Neural Networks (DNNs) are
usually high. This issue limits their deployability on ubiquitous computing
devices such as smart phones, wearables and autonomous drones. In this paper,
we propose ternary neural networks (TNNs) in order to make deep learning more
resource-ef... | Ternary Neural Networks for Resource-Efficient AI Applications | 2,016 | http://arxiv.org/pdf/1609.00222v2 | Title Ternary Neural Networks ResourceEfficient AI Applications Summary computation storage requirement Deep Neural Networks DNNs usually high issue limit deployability ubiquitous computing device smart phone wearable autonomous drone paper propose ternary neural network TNNs order make deep learning resourceefficient ... | [0.007484246511012316, 0.03200938552618027, -0.02053963765501976, 0.029234547168016434, -0.0013657851377502084, -0.05449562519788742, 0.06825247406959534, -0.017961448058485985, -0.06530588120222092, -0.04260271415114403, 0.01697932556271553, -0.004746080841869116, -0.02053097076714039, 0.04266133904457092, 0.057883877... |
813 | 813 | ['Navid Kardan', 'Kenneth O. Stanley'] | 1609.02226v3 | Though deep learning has pushed the boundaries of classification forward, in
recent years hints of the limits of standard classification have begun to
emerge. Problems such as fooling, adding new classes over time, and the need to
retrain learning models only for small changes to the original problem all
point to a pot... | Fitted Learning: Models with Awareness of their Limits | 2,016 | http://arxiv.org/pdf/1609.02226v3 | Title Fitted Learning Models Awareness Limits Summary Though deep learning pushed boundary classification forward recent year hint limit standard classification begun emerge Problems fooling adding new class time need retrain learning model small change original problem point potential shortcoming classic classificatio... | [0.004553238861262798, 0.023862645030021667, -0.02515711449086666, 0.015982070937752724, 0.004415976349264383, 3.2951364119071513e-05, 0.02864004857838154, 0.004825477488338947, -0.037041936069726944, -0.014320683665573597, 0.0034049013629555702, 0.026574747636914253, -0.010187797248363495, 0.08596868813037872, -0.0012... |
814 | 814 | ['Thomas Miconi'] | 1609.02228v2 | Hebbian plasticity is a powerful principle that allows biological brains to
learn from their lifetime experience. By contrast, artificial neural networks
trained with backpropagation generally have fixed connection weights that do
not change once training is complete. While recent methods can endow neural
networks with... | Learning to learn with backpropagation of Hebbian plasticity | 2,016 | http://arxiv.org/pdf/1609.02228v2 | Title Learning learn backpropagation Hebbian plasticity Summary Hebbian plasticity powerful principle allows biological brain learn lifetime experience contrast artificial neural network trained backpropagation generally fixed connection weight change training complete recent method endow neural network longterm memory... | [-0.005512428469955921, 0.006936597637832165, -0.031196948140859604, -0.028671780601143837, 0.01135297678411007, -0.021965527907013893, 0.037528522312641144, 0.0007433979772031307, -0.019423458725214005, 0.037506431341171265, -0.016754431650042534, 0.021670395508408546, 0.027609504759311676, 0.048013459891080856, 0.020... |
815 | 815 | ['Lana Sinapayen', 'Atsushi Masumori', 'Takashi Ikegami'] | 1609.07706v1 | Learning based on networks of real neurons, and by extension biologically
inspired models of neural networks, has yet to find general learning rules
leading to widespread applications. In this paper, we argue for the existence
of a principle allowing to steer the dynamics of a biologically inspired neural
network. Usin... | Learning by Stimulation Avoidance: A Principle to Control Spiking Neural
Networks Dynamics | 2,016 | http://arxiv.org/pdf/1609.07706v1 | Title Learning Stimulation Avoidance Principle Control Spiking Neural Networks Dynamics Summary Learning based network real neuron extension biologically inspired model neural network yet find general learning rule leading widespread application paper argue existence principle allowing steer dynamic biologically inspir... | [-0.018463438376784325, -0.03528013452887535, -0.028859732672572136, -0.003135600360110402, 0.010782533325254917, -0.007587278727442026, -0.0055375006049871445, -0.003981065936386585, 0.01698615401983261, 0.027135254815220833, -0.036468978971242905, 0.027992412447929382, -0.030093610286712646, 0.05802058428525925, 0.04... |
816 | 816 | ['Kamil Rocki', 'Tomasz Kornuta', 'Tegan Maharaj'] | 1610.07675v6 | We propose a novel method of regularization for recurrent neural networks
called suprisal-driven zoneout. In this method, states zoneout (maintain their
previous value rather than updating), when the suprisal (discrepancy between
the last state's prediction and target) is small. Thus regularization is
adaptive and inpu... | Surprisal-Driven Zoneout | 2,016 | http://arxiv.org/pdf/1610.07675v6 | Title SurprisalDriven Zoneout Summary propose novel method regularization recurrent neural network called suprisaldriven zoneout method state zoneout maintain previous value rather updating suprisal discrepancy last state prediction target small Thus regularization adaptive inputdriven perneuron basis demonstrate effec... | [-0.02612912654876709, 0.04197278246283531, 0.0021873782388865948, 0.008346394635736942, -0.02123868092894554, -0.029539236798882484, -0.0008560279966332018, 0.0529327392578125, -0.08594375848770142, 0.029092682525515556, 0.05646948516368866, -0.0040793693624436855, 0.02608955092728138, 0.03944779932498932, 0.023502251... |
817 | 817 | ['Barret Zoph', 'Quoc V. Le'] | 1611.01578v2 | Neural networks are powerful and flexible models that work well for many
difficult learning tasks in image, speech and natural language understanding.
Despite their success, neural networks are still hard to design. In this paper,
we use a recurrent network to generate the model descriptions of neural
networks and trai... | Neural Architecture Search with Reinforcement Learning | 2,016 | http://arxiv.org/pdf/1611.01578v2 | Title Neural Architecture Search Reinforcement Learning Summary Neural network powerful flexible model work well many difficult learning task image speech natural language understanding Despite success neural network still hard design paper use recurrent network generate model description neural network train RNN reinf... | [0.04315446689724922, 0.041258394718170166, -0.010374544188380241, 0.03191472217440605, -0.0324622243642807, -0.016813019290566444, 0.0007508726557716727, 0.0015609796391800046, -0.047142814844846725, 0.00805315189063549, -0.015890780836343765, -0.03392862156033516, 0.012950747273862362, 0.07452162355184555, 0.01471887... |
818 | 818 | ['Brian Cheung', 'Eric Weiss', 'Bruno Olshausen'] | 1611.09430v2 | We describe a neural attention model with a learnable retinal sampling
lattice. The model is trained on a visual search task requiring the
classification of an object embedded in a visual scene amidst background
distractors using the smallest number of fixations. We explore the tiling
properties that emerge in the mode... | Emergence of foveal image sampling from learning to attend in visual
scenes | 2,016 | http://arxiv.org/pdf/1611.09430v2 | Title Emergence foveal image sampling learning attend visual scene Summary describe neural attention model learnable retinal sampling lattice model trained visual search task requiring classification object embedded visual scene amidst background distractors using smallest number fixation explore tiling property emerge... | [0.012428577058017254, -0.03871902450919151, -0.03312397003173828, 0.011463208124041557, 0.024118706583976746, -0.01312296837568283, 0.012522065080702305, -0.016382869333028793, -0.024642430245876312, 0.0025040465407073498, 0.016565145924687386, 0.01324431411921978, 0.0005026690778322518, 0.02636227384209633, 0.0162643... |
819 | 819 | ['Steven Stenberg Hansen'] | 1701.03866v1 | Credit assignment in traditional recurrent neural networks usually involves
back-propagating through a long chain of tied weight matrices. The length of
this chain scales linearly with the number of time-steps as the same network is
run at each time-step. This creates many problems, such as vanishing gradients,
that ha... | Long Timescale Credit Assignment in NeuralNetworks with External Memory | 2,017 | http://arxiv.org/pdf/1701.03866v1 | Title Long Timescale Credit Assignment NeuralNetworks External Memory Summary Credit assignment traditional recurrent neural network usually involves backpropagating long chain tied weight matrix length chain scale linearly number timesteps network run timestep creates many problem vanishing gradient well studied contr... | [0.01334431953728199, 0.025611329823732376, -0.0011260801693424582, 0.027909809723496437, -0.02556299977004528, -0.009520058520138264, 0.04025751352310181, -0.027384348213672638, -0.03948945179581642, 0.00924374908208847, 0.006796651054173708, -0.033932916820049286, 0.014555448666214943, 0.03412463143467903, 0.03704479... |
820 | 820 | ['Arman Afrasiyabi', 'Ozan Yildiz', 'Baris Nasir', 'Fatos T. Yarman Vural', 'A. Enis Cetin'] | 1702.02676v1 | In recent years, machine learning techniques based on neural networks for
mobile computing become increasingly popular. Classical multi-layer neural
networks require matrix multiplications at each stage. Multiplication operation
is not an energy efficient operation and consequently it drains the battery of
the mobile d... | Energy Saving Additive Neural Network | 2,017 | http://arxiv.org/pdf/1702.02676v1 | Title Energy Saving Additive Neural Network Summary recent year machine learning technique based neural network mobile computing become increasingly popular Classical multilayer neural network require matrix multiplication stage Multiplication operation energy efficient operation consequently drain battery mobile devic... | [-0.041559599339962006, 0.0196552537381649, -0.013178504072129726, 0.01698065921664238, 0.020249901339411736, -0.004165116231888533, 0.043071042746305466, -0.03175780177116394, -0.05672718212008476, -0.0013886174419894814, 0.003121745539829135, 0.01433127373456955, 0.022105585783720016, 0.007520826067775488, 0.04837945... |
821 | 821 | ['Sahil Sharma', 'Aravind S. Lakshminarayanan', 'Balaraman Ravindran'] | 1702.06054v1 | Reinforcement Learning algorithms can learn complex behavioral patterns for
sequential decision making tasks wherein an agent interacts with an environment
and acquires feedback in the form of rewards sampled from it. Traditionally,
such algorithms make decisions, i.e., select actions to execute, at every
single time s... | Learning to Repeat: Fine Grained Action Repetition for Deep
Reinforcement Learning | 2,017 | http://arxiv.org/pdf/1702.06054v1 | Title Learning Repeat Fine Grained Action Repetition Deep Reinforcement Learning Summary Reinforcement Learning algorithm learn complex behavioral pattern sequential decision making task wherein agent interacts environment acquires feedback form reward sampled Traditionally algorithm make decision ie select action exec... | [-0.007449817378073931, -0.017083393409848213, -0.020155394449830055, -0.014041852205991745, -0.0049669453874230385, 0.01620970480144024, -0.00025756648392416537, -0.018405510112643242, -0.04354967921972275, -0.008511803112924099, -0.0058097923174500465, 0.04968591779470444, -0.00846255011856556, 0.09525875747203827, 0... |
822 | 822 | ['Amit Sahu'] | 1702.06186v2 | Reason and inference require process as well as memory skills by humans.
Neural networks are able to process tasks like image recognition (better than
humans) but in memory aspects are still limited (by attention mechanism, size).
Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve
small mem... | Survey of reasoning using Neural networks | 2,017 | http://arxiv.org/pdf/1702.06186v2 | Title Survey reasoning using Neural network Summary Reason inference require process well memory skill human Neural network able process task like image recognition better human memory aspect still limited attention mechanism size Recurrent Neural Network RNN modified version LSTM able solve small memory context contex... | [0.05290656164288521, 0.045261893421411514, -0.00019537043408490717, 0.06497050076723099, -0.039999376982450485, 0.0035967694129794836, 0.022891681641340256, 0.00871207844465971, 0.014085907489061356, -0.042083173990249634, 0.01758415438234806, 0.008582827635109425, 0.004018485080450773, 0.018058646470308304, 0.0189506... |
823 | 823 | ['Yan Duan', 'Marcin Andrychowicz', 'Bradly C. Stadie', 'Jonathan Ho', 'Jonas Schneider', 'Ilya Sutskever', 'Pieter Abbeel', 'Wojciech Zaremba'] | 1703.07326v3 | Imitation learning has been commonly applied to solve different tasks in
isolation. This usually requires either careful feature engineering, or a
significant number of samples. This is far from what we desire: ideally, robots
should be able to learn from very few demonstrations of any given task, and
instantly general... | One-Shot Imitation Learning | 2,017 | http://arxiv.org/pdf/1703.07326v3 | Title OneShot Imitation Learning Summary Imitation learning commonly applied solve different task isolation usually requires either careful feature engineering significant number sample far desire ideally robot able learn demonstration given task instantly generalize new situation task without requiring taskspecific en... | [-0.02619013376533985, 0.003925428260117769, -0.004163840785622597, -0.022319246083498, -0.05592583492398262, -0.021797653287649155, 0.024787820875644684, 0.01689254678785801, -0.011839345097541809, -0.00804369617253542, -0.004566506016999483, -0.030544642359018326, -0.024636536836624146, 0.02313395030796528, 0.0157427... |
824 | 824 | ['Shumeet Baluja'] | 1703.07394v1 | In all but the most trivial optimization problems, the structure of the
solutions exhibit complex interdependencies between the input parameters.
Decades of research with stochastic search techniques has shown the benefit of
explicitly modeling the interactions between sets of parameters and the overall
quality of the ... | Deep Learning for Explicitly Modeling Optimization Landscapes | 2,017 | http://arxiv.org/pdf/1703.07394v1 | Title Deep Learning Explicitly Modeling Optimization Landscapes Summary trivial optimization problem structure solution exhibit complex interdependency input parameter Decades research stochastic search technique shown benefit explicitly modeling interaction set parameter overall quality solution discovered demonstrate... | [-0.010769948363304138, 0.0350692979991436, -0.03418678790330887, 0.027825459837913513, -0.011421464383602142, -0.0665297731757164, 0.013387186452746391, 0.00261049740947783, -0.08670773357152939, 0.004274922888725996, 0.008350112475454807, 0.007464745081961155, -0.007705566473305225, 0.07621415704488754, 0.01865247450... |
825 | 825 | ['Carlos Florensa', 'Yan Duan', 'Pieter Abbeel'] | 1704.03012v1 | Deep reinforcement learning has achieved many impressive results in recent
years. However, tasks with sparse rewards or long horizons continue to pose
significant challenges. To tackle these important problems, we propose a
general framework that first learns useful skills in a pre-training
environment, and then levera... | Stochastic Neural Networks for Hierarchical Reinforcement Learning | 2,017 | http://arxiv.org/pdf/1704.03012v1 | Title Stochastic Neural Networks Hierarchical Reinforcement Learning Summary Deep reinforcement learning achieved many impressive result recent year However task sparse reward long horizon continue pose significant challenge tackle important problem propose general framework first learns useful skill pretraining enviro... | [-0.0013203145936131477, 0.04116049036383629, -0.004599382169544697, -0.008501452393829823, 0.022824062034487724, -0.02133704349398613, 0.023822534829378128, -0.008480294607579708, -0.051138367503881454, 0.02041446790099144, -0.06494960933923721, 0.0006839332054369152, 0.0030479920096695423, 0.07526356726884842, 0.0206... |
826 | 826 | ['Daniel Hein', 'Steffen Udluft', 'Michel Tokic', 'Alexander Hentschel', 'Thomas A. Runkler', 'Volkmar Sterzing'] | 1705.07262v2 | The Particle Swarm Optimization Policy (PSO-P) has been recently introduced
and proven to produce remarkable results on interacting with academic
reinforcement learning benchmarks in an off-policy, batch-based setting. To
further investigate the properties and feasibility on real-world applications,
this paper investig... | Batch Reinforcement Learning on the Industrial Benchmark: First
Experiences | 2,017 | http://arxiv.org/pdf/1705.07262v2 | Title Batch Reinforcement Learning Industrial Benchmark First Experiences Summary Particle Swarm Optimization Policy PSOP recently introduced proven produce remarkable result interacting academic reinforcement learning benchmark offpolicy batchbased setting investigate property feasibility realworld application paper i... | [-0.04859529808163643, 0.009126704186201096, 0.004413751419633627, -0.036701805889606476, -0.009103127755224705, -0.053609319031238556, 0.0009935915004462004, 0.02723786048591137, -0.0376310721039772, 0.0017250364180654287, 0.024324486032128334, 0.02293894998729229, 0.003281214740127325, 0.029910871759057045, 0.0144245... |
827 | 827 | ['Tim Rocktäschel', 'Sebastian Riedel'] | 1705.11040v2 | We introduce neural networks for end-to-end differentiable proving of queries
to knowledge bases by operating on dense vector representations of symbols.
These neural networks are constructed recursively by taking inspiration from
the backward chaining algorithm as used in Prolog. Specifically, we replace
symbolic unif... | End-to-End Differentiable Proving | 2,017 | http://arxiv.org/pdf/1705.11040v2 | Title EndtoEnd Differentiable Proving Summary introduce neural network endtoend differentiable proving query knowledge base operating dense vector representation symbol neural network constructed recursively taking inspiration backward chaining algorithm used Prolog Specifically replace symbolic unification differentia... | [0.00876219104975462, 0.0424051396548748, -0.019259411841630936, 0.040070559829473495, -0.024717167019844055, -0.019320471212267876, 0.026828352361917496, 0.020608365535736084, -0.009044548496603966, 0.003754157805815339, 0.0062873768620193005, 0.061944689601659775, -0.0130495959892869, 0.041643187403678894, 0.02143714... |
828 | 828 | ['Ryan Lowe', 'Yi Wu', 'Aviv Tamar', 'Jean Harb', 'Pieter Abbeel', 'Igor Mordatch'] | 1706.02275v3 | We explore deep reinforcement learning methods for multi-agent domains. We
begin by analyzing the difficulty of traditional algorithms in the multi-agent
case: Q-learning is challenged by an inherent non-stationarity of the
environment, while policy gradient suffers from a variance that increases as
the number of agent... | Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments | 2,017 | http://arxiv.org/pdf/1706.02275v3 | Title MultiAgent ActorCritic Mixed CooperativeCompetitive Environments Summary explore deep reinforcement learning method multiagent domain begin analyzing difficulty traditional algorithm multiagent case Qlearning challenged inherent nonstationarity environment policy gradient suffers variance increase number agent gr... | [0.014611853286623955, 0.02163694240152836, -0.022250503301620483, -0.018286388367414474, -0.019145065918564796, -0.006084064487367868, 0.04140058159828186, -0.05955472216010094, -0.03481706976890564, 0.007267179433256388, -0.03561222925782204, -0.021177997812628746, -0.03134753555059433, 0.023660384118556976, -0.00548... |
829 | 829 | ['Joan Serrà', 'Alexandros Karatzoglou'] | 1706.03993v1 | Recommendation algorithms that incorporate techniques from deep learning are
becoming increasingly popular. Due to the structure of the data coming from
recommendation domains (i.e., one-hot-encoded vectors of item preferences),
these algorithms tend to have large input and output dimensionalities that
dominate their o... | Getting deep recommenders fit: Bloom embeddings for sparse binary
input/output networks | 2,017 | http://arxiv.org/pdf/1706.03993v1 | Title Getting deep recommenders fit Bloom embeddings sparse binary inputoutput network Summary Recommendation algorithm incorporate technique deep learning becoming increasingly popular Due structure data coming recommendation domain ie onehotencoded vector item preference algorithm tend large input output dimensionali... | [0.012883339077234268, -0.009338920004665852, -0.01198730431497097, 0.009027131833136082, -0.002386106178164482, -0.04708776995539665, 0.05819787085056305, 0.016367528587579727, 0.010520237497985363, -0.018471479415893555, 0.008583184331655502, -0.014873209409415722, -0.009784399531781673, 0.12243649363517761, 0.010296... |
830 | 830 | ['Jinzhuo Wang', 'Wenmin Wang', 'Ronggang Wang', 'Wen Gao'] | 1706.04052v1 | Monte Carlo tree search (MCTS) is extremely popular in computer Go which
determines each action by enormous simulations in a broad and deep search tree.
However, human experts select most actions by pattern analysis and careful
evaluation rather than brute search of millions of future nteractions. In this
paper, we pro... | Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural
Network and Long-Term Evaluation | 2,017 | http://arxiv.org/pdf/1706.04052v1 | Title Beyond Monte Carlo Tree Search Playing Go Deep Alternative Neural Network LongTerm Evaluation Summary Monte Carlo tree search MCTS extremely popular computer Go determines action enormous simulation broad deep search tree However human expert select action pattern analysis careful evaluation rather brute search m... | [-0.018440835177898407, 0.00823884829878807, -0.025559427216649055, 0.01030297763645649, 0.009421521797776222, -0.06514395773410797, -0.020029939711093903, 0.028141772374510765, -0.04764072597026825, 0.010844013653695583, -0.01397472620010376, 0.01675931178033352, -0.020373951643705368, 0.07995585352182388, 0.044008787... |
831 | 831 | ['Marcin Andrychowicz', 'Filip Wolski', 'Alex Ray', 'Jonas Schneider', 'Rachel Fong', 'Peter Welinder', 'Bob McGrew', 'Josh Tobin', 'Pieter Abbeel', 'Wojciech Zaremba'] | 1707.01495v3 | Dealing with sparse rewards is one of the biggest challenges in Reinforcement
Learning (RL). We present a novel technique called Hindsight Experience Replay
which allows sample-efficient learning from rewards which are sparse and binary
and therefore avoid the need for complicated reward engineering. It can be
combined... | Hindsight Experience Replay | 2,017 | http://arxiv.org/pdf/1707.01495v3 | Title Hindsight Experience Replay Summary Dealing sparse reward one biggest challenge Reinforcement Learning RL present novel technique called Hindsight Experience Replay allows sampleefficient learning reward sparse binary therefore avoid need complicated reward engineering combined arbitrary offpolicy RL algorithm ma... | [0.01251895260065794, -0.006217009387910366, 0.008013303391635418, -0.048185672610998154, -0.024119410663843155, -0.022304944694042206, 0.00254358584061265, 0.012106338515877724, -0.0323614664375782, -0.0015307181747630239, -0.002653340343385935, 0.03844265267252922, -0.033091168850660324, 0.04591533914208412, -0.02303... |
832 | 832 | ['William Saunders', 'Girish Sastry', 'Andreas Stuhlmueller', 'Owain Evans'] | 1707.05173v1 | AI systems are increasingly applied to complex tasks that involve interaction
with humans. During training, such systems are potentially dangerous, as they
haven't yet learned to avoid actions that could cause serious harm. How can an
AI system explore and learn without making a single mistake that harms humans
or othe... | Trial without Error: Towards Safe Reinforcement Learning via Human
Intervention | 2,017 | http://arxiv.org/pdf/1707.05173v1 | Title Trial without Error Towards Safe Reinforcement Learning via Human Intervention Summary AI system increasingly applied complex task involve interaction human training system potentially dangerous havent yet learned avoid action could cause serious harm AI system explore learn without making single mistake harm hum... | [0.016132855787873268, 0.04305306077003479, -0.005523810163140297, -0.06343850493431091, 0.003946242388337851, 0.03117883950471878, -1.495579181209905e-05, -0.007241299841552973, -1.276238435821142e-06, 0.02175363525748253, 0.02454415149986744, 0.05398100987076759, -0.042051032185554504, 0.0768401250243187, 0.029091153... |
833 | 833 | ['Carlos Florensa', 'David Held', 'Markus Wulfmeier', 'Michael Zhang', 'Pieter Abbeel'] | 1707.05300v2 | Many relevant tasks require an agent to reach a certain state, or to
manipulate objects into a desired configuration. For example, we might want a
robot to align and assemble a gear onto an axle or insert and turn a key in a
lock. These goal-oriented tasks present a considerable challenge for
reinforcement learning, si... | Reverse Curriculum Generation for Reinforcement Learning | 2,017 | http://arxiv.org/pdf/1707.05300v2 | Title Reverse Curriculum Generation Reinforcement Learning Summary Many relevant task require agent reach certain state manipulate object desired configuration example might want robot align assemble gear onto axle insert turn key lock goaloriented task present considerable challenge reinforcement learning since natura... | [0.02395312860608101, -0.03560492396354675, -0.022230951115489006, -0.027542339637875557, -0.009401685558259487, -0.01566183567047119, -0.00923572201281786, -0.021622776985168457, -0.03852638974785805, -0.0009287800639867783, -0.02003348246216774, 0.06214943900704384, -0.010654082521796227, 0.04130471497774124, -0.0070... |
834 | 834 | ['S. Hossein Hosseini', 'Afshin Ebrahimi'] | 1707.06992v2 | A population-based optimization algorithm was designed, inspired by two main
thinking modes in philosophy, both based on dialectic concept and
thesis-antithesis paradigm. They impose two different kinds of dialectics.
Idealistic and materialistic antitheses are formulated as optimization models.
Based on the models, th... | Ideological Sublations: Resolution of Dialectic in Population-based
Optimization | 2,017 | http://arxiv.org/pdf/1707.06992v2 | Title Ideological Sublations Resolution Dialectic Populationbased Optimization Summary populationbased optimization algorithm designed inspired two main thinking mode philosophy based dialectic concept thesisantithesis paradigm impose two different kind dialectic Idealistic materialistic antithesis formulated optimizat... | [0.008950497023761272, 0.06336093693971634, -0.013111977837979794, -0.01586339622735977, -0.02789732627570629, -0.04434084892272949, 0.017018530517816544, 0.016037438064813614, -0.07308778911828995, -0.0345175601541996, 0.050040144473314285, 0.0054401527158916, 0.007886533625423908, 0.009143506176769733, -0.04539664462... |
835 | 835 | ['Sujith Ravi'] | 1708.00630v2 | Deep neural networks have become ubiquitous for applications related to
visual recognition and language understanding tasks. However, it is often
prohibitive to use typical neural networks on devices like mobile phones or
smart watches since the model sizes are huge and cannot fit in the limited
memory available on suc... | ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural
Projections | 2,017 | http://arxiv.org/pdf/1708.00630v2 | Title ProjectionNet Learning Efficient OnDevice Deep Networks Using Neural Projections Summary Deep neural network become ubiquitous application related visual recognition language understanding task However often prohibitive use typical neural network device like mobile phone smart watch since model size huge cannot f... | [0.009888674132525921, 0.028102440759539604, -0.004740584641695023, 0.06274411827325821, -0.011353502050042152, -0.021599646657705307, 0.03855704516172409, -0.020037738606333733, -0.060172080993652344, 0.003622456919401884, -0.037688616663217545, 0.002514038933441043, 0.041991688311100006, 0.011390387080609798, -0.0009... |
836 | 836 | ['Zhen Li', 'Zuoqiang Shi'] | 1708.06257v2 | Based on a natural connection between ResNet and transport equation or its
characteristic equation, we propose a continuous flow model for both ResNet and
plain net. Through this continuous model, a ResNet can be explicitly
constructed as a refinement of a plain net. The flow model provides an
alternative perspective t... | A Flow Model of Neural Networks | 2,017 | http://arxiv.org/pdf/1708.06257v2 | Title Flow Model Neural Networks Summary Based natural connection ResNet transport equation characteristic equation propose continuous flow model ResNet plain net continuous model ResNet explicitly constructed refinement plain net flow model provides alternative perspective understand phenomenon deep neural network nec... | [-0.05974646657705307, -0.013680173084139824, -0.041853632777929306, 0.01939602755010128, -0.024163352325558662, -0.051294393837451935, 0.005934596993029118, -0.03236718475818634, -0.03148850426077843, 0.0065529001876711845, 0.05397547036409378, 0.013033125549554825, 0.007781938184052706, 0.05482274293899536, 0.0474607... |
837 | 837 | ['Nikhita Vedula', 'Wei Sun', 'Hyunhwan Lee', 'Harsh Gupta', 'Mitsunori Ogihara', 'Joseph Johnson', 'Gang Ren', 'Srinivasan Parthasarathy'] | 1709.03946v1 | The rapid advances in e-commerce and Web 2.0 technologies have greatly
increased the impact of commercial advertisements on the general public. As a
key enabling technology, a multitude of recommender systems exists which
analyzes user features and browsing patterns to recommend appealing
advertisements to users. In th... | Multimodal Content Analysis for Effective Advertisements on YouTube | 2,017 | http://arxiv.org/pdf/1709.03946v1 | Title Multimodal Content Analysis Effective Advertisements YouTube Summary rapid advance ecommerce Web 20 technology greatly increased impact commercial advertisement general public key enabling technology multitude recommender system exists analyzes user feature browsing pattern recommend appealing advertisement user ... | [0.05325677990913391, 0.01905878633260727, -0.04145479202270508, -0.01872815005481243, 0.0188889279961586, -0.016468115150928497, 0.04491739347577095, -0.013797054998576641, -0.022574132308363914, -0.10200163722038269, -0.054233718663454056, -0.02793099358677864, -0.001039214781485498, 0.10229802876710892, 0.0115968361... |
838 | 838 | ['Ashvin Nair', 'Bob McGrew', 'Marcin Andrychowicz', 'Wojciech Zaremba', 'Pieter Abbeel'] | 1709.10089v2 | Exploration in environments with sparse rewards has been a persistent problem
in reinforcement learning (RL). Many tasks are natural to specify with a sparse
reward, and manually shaping a reward function can result in suboptimal
performance. However, finding a non-zero reward is exponentially more difficult
with incre... | Overcoming Exploration in Reinforcement Learning with Demonstrations | 2,017 | http://arxiv.org/pdf/1709.10089v2 | Title Overcoming Exploration Reinforcement Learning Demonstrations Summary Exploration environment sparse reward persistent problem reinforcement learning RL Many task natural specify sparse reward manually shaping reward function result suboptimal performance However finding nonzero reward exponentially difficult incr... | [-0.000670247187372297, 0.007406280376017094, 0.008679671213030815, -0.04938372224569321, -0.01916898600757122, -0.008341330103576183, -0.0046310280449688435, -0.032446566969156265, -0.055457938462495804, 0.03828710317611694, -0.02534225583076477, 0.06884787976741791, -0.0331156887114048, 0.07698538899421692, -0.000784... |
839 | 839 | ['Chaitanya Ahuja', 'Louis-Philippe Morency'] | 1710.02254v2 | Recurrent neural networks have shown remarkable success in modeling
sequences. However low resource situations still adversely affect the
generalizability of these models. We introduce a new family of models, called
Lattice Recurrent Units (LRU), to address the challenge of learning deep
multi-layer recurrent models wi... | Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency
for Sequence Modeling | 2,017 | http://arxiv.org/pdf/1710.02254v2 | Title Lattice Recurrent Unit Improving Convergence Statistical Efficiency Sequence Modeling Summary Recurrent neural network shown remarkable success modeling sequence However low resource situation still adversely affect generalizability model introduce new family model called Lattice Recurrent Units LRU address chall... | [-0.02703038603067398, 0.056324027478694916, -0.019291691482067108, 0.06274662911891937, -0.03949630632996559, -0.0014617762062698603, 0.028200378641486168, -0.00469781132414937, -0.07082455605268478, -0.008138565346598625, 0.022258136421442032, -0.07556074857711792, 0.033635951578617096, 0.027700180187821388, 0.006997... |
840 | 840 | ['Matthew Riemer', 'Tim Klinger', 'Michele Franceschini', 'Djallel Bouneffouf'] | 1711.06761v3 | Given the recent success of Deep Learning applied to a variety of single
tasks, it is natural to consider more human-realistic settings. Perhaps the
most difficult of these settings is that of continual lifelong learning, where
the model must learn online over a continuous stream of non-stationary data. A
continual lif... | Scalable Recollections for Continual Lifelong Learning | 2,017 | http://arxiv.org/pdf/1711.06761v3 | Title Scalable Recollections Continual Lifelong Learning Summary Given recent success Deep Learning applied variety single task natural consider humanrealistic setting Perhaps difficult setting continual lifelong learning model must learn online continuous stream nonstationary data continual lifelong learning system mu... | [0.02174822986125946, 0.05593891069293022, -0.015595111064612865, 0.010836074128746986, -0.020778335630893707, 0.005336886737495661, 0.04968583956360817, -0.042867522686719894, -0.022607678547501564, -0.007372090592980385, -0.0037958917673677206, -0.03462539613246918, -0.06478039175271988, 0.09908340126276016, -0.02315... |
841 | 841 | ['Davide Bacciu'] | 1711.07784v1 | The paper introduces the Hidden Tree Markov Network (HTN), a
neuro-probabilistic hybrid fusing the representation power of generative models
for trees with the incremental and discriminative learning capabilities of
neural networks. We put forward a modular architecture in which multiple
generative models of limited co... | Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data | 2,017 | http://arxiv.org/pdf/1711.07784v1 | Title Hidden Tree Markov Networks Deep Wide Learning Structured Data Summary paper introduces Hidden Tree Markov Network HTN neuroprobabilistic hybrid fusing representation power generative model tree incremental discriminative learning capability neural network put forward modular architecture multiple generative mode... | [0.011593655683100224, 0.006323219742625952, 0.025108326226472855, 0.05469093099236488, -0.0006732799229212105, 0.027713587507605553, -0.053613532334566116, 0.0014808099949732423, -0.013125276193022728, -0.07022825628519058, -0.0016904890071600676, -0.006906906142830849, 0.01242598332464695, 0.08071021735668182, -0.015... |
842 | 842 | ['Andrew Levy', 'Robert Platt', 'Kate Saenko'] | 1712.00948v3 | The ability to learn at different resolutions in time may help overcome one
of the main challenges in deep reinforcement learning -- sample efficiency.
Hierarchical agents that operate at different levels of temporal abstraction
can learn tasks more quickly because they can divide the work of learning
behaviors among m... | Hierarchical Actor-Critic | 2,017 | http://arxiv.org/pdf/1712.00948v3 | Title Hierarchical ActorCritic Summary ability learn different resolution time may help overcome one main challenge deep reinforcement learning sample efficiency Hierarchical agent operate different level temporal abstraction learn task quickly divide work learning behavior among multiple policy also explore environmen... | [0.010711533948779106, 0.057223305106163025, -0.029990557581186295, -0.02625054307281971, 0.0012431297218427062, 0.007482779212296009, 0.04047413542866707, 0.01626020297408104, -0.007949854247272015, -0.00869183149188757, -0.0275528896600008, -0.01351903285831213, -0.04517769441008568, 0.08610741794109344, -0.002952864... |
843 | 843 | ['Freek Stulp', 'Pierre-Yves Oudeyer'] | 1712.05249v1 | To harness the complexity of their high-dimensional bodies during
sensorimotor development, infants are guided by patterns of freezing and
freeing of degrees of freedom. For instance, when learning to reach, infants
free the degrees of freedom in their arm proximodistally, i.e. from joints that
are closer to the body t... | Proximodistal Exploration in Motor Learning as an Emergent Property of
Optimization | 2,017 | http://arxiv.org/pdf/1712.05249v1 | Title Proximodistal Exploration Motor Learning Emergent Property Optimization Summary harness complexity highdimensional body sensorimotor development infant guided pattern freezing freeing degree freedom instance learning reach infant free degree freedom arm proximodistally ie joint closer body distant formulate study... | [-0.007591096218675375, -0.030919570475816727, -0.04300932586193085, -0.013939157128334045, -0.01064420398324728, 0.022560911253094673, 0.019957570359110832, -0.005076027940958738, -0.029832862317562103, 0.026859911158680916, -0.024408021941781044, -0.03122008964419365, 0.02185315266251564, 0.028434840962290764, 0.0178... |
844 | 844 | ['M. J. Gagen'] | 1712.09014v1 | The hard problem in artificial intelligence asks how the shuffling of
syntactical symbols in a program can lead to systems which experience semantics
and qualia. We address this question in three stages. First, we introduce a new
class of human semantic symbols which appears when unexpected and drastic
environmental ch... | Null Dynamical State Models of Human Cognitive Dysfunction | 2,017 | http://arxiv.org/pdf/1712.09014v1 | Title Null Dynamical State Models Human Cognitive Dysfunction Summary hard problem artificial intelligence asks shuffling syntactical symbol program lead system experience semantics qualia address question three stage First introduce new class human semantic symbol appears unexpected drastic environmental change cause ... | [0.02669551596045494, 0.01971989870071411, -0.04531700164079666, -0.03280062973499298, -0.0007659009424969554, 0.03856762871146202, -0.01750306226313114, 0.026557743549346924, 0.042881399393081665, -0.0329393595457077, 0.03975263983011246, 0.027196673676371574, 0.006306413095444441, 0.057519715279340744, 0.018768649548... |
845 | 845 | ['Haik Manukian', 'Fabio L. Traversa', 'Massimiliano Di Ventra'] | 1801.00512v2 | Restricted Boltzmann machines (RBMs) and their extensions, called
'deep-belief networks', are powerful neural networks that have found
applications in the fields of machine learning and big data. The standard way
to training these models resorts to an iterative unsupervised procedure based
on Gibbs sampling, called 'co... | Accelerating Deep Learning with Memcomputing | 2,018 | http://arxiv.org/pdf/1801.00512v2 | Title Accelerating Deep Learning Memcomputing Summary Restricted Boltzmann machine RBMs extension called deepbelief network powerful neural network found application field machine learning big data standard way training model resort iterative unsupervised procedure based Gibbs sampling called contrastive divergence CD ... | [-0.021321767941117287, 0.008590729907155037, -0.04020331799983978, 0.0603061243891716, 0.013501854613423347, 0.004984626546502113, 0.006701979320496321, -0.006113486364483833, -0.0337209552526474, 0.019319409504532814, -0.03536015748977661, 0.015597990714013577, -0.016139866784214973, 0.03549143299460411, 0.0296674575... |
846 | 846 | ['Yu Shi', 'Fangqiu Han', 'Xinran He', 'Carl Yang', 'Jie Luo', 'Jiawei Han'] | 1801.06597v1 | Multi-view networks are ubiquitous in real-world applications. In order to
extract knowledge or business value, it is of interest to transform such
networks into representations that are easily machine-actionable. Meanwhile,
network embedding has emerged as an effective approach to generate distributed
network represen... | mvn2vec: Preservation and Collaboration in Multi-View Network Embedding | 2,018 | http://arxiv.org/pdf/1801.06597v1 | Title mvn2vec Preservation Collaboration MultiView Network Embedding Summary Multiview network ubiquitous realworld application order extract knowledge business value interest transform network representation easily machineactionable Meanwhile network embedding emerged effective approach generate distributed network re... | [-0.0012199279153719544, 0.03439829498529434, -0.014865078963339329, 0.005650732666254044, 0.013211688958108425, -0.023278461769223213, -0.00024796868092380464, -0.02037089131772518, 0.020105179399251938, -0.020993001759052277, 0.036786191165447235, 0.02955283224582672, -0.0013284682063385844, 0.06180468946695328, 0.03... |
847 | 847 | ['Patrick Schwab', 'Walter Karlen'] | 1802.02195v1 | Several methods have recently been proposed to detect salient input features
for outputs of neural networks. Those methods offer a qualitative glimpse at
feature importance, but they fall short of providing quantifiable attributions
that can be compared across decisions and measures of the expected quality of
their exp... | Granger-causal Attentive Mixtures of Experts | 2,018 | http://arxiv.org/pdf/1802.02195v1 | Title Grangercausal Attentive Mixtures Experts Summary Several method recently proposed detect salient input feature output neural network method offer qualitative glimpse feature importance fall short providing quantifiable attribution compared across decision measure expected quality explanation address shortcoming p... | [0.00200763251632452, 0.010885958559811115, -0.006908542010933161, 0.019717030227184296, 0.01309158094227314, 0.019467514008283615, 0.0177998598664999, -0.010452842339873314, -0.03912140801548958, 0.004165454301983118, -0.014568503014743328, 0.013057363219559193, 0.04707609862089157, 0.036716509610414505, 0.01283021923... |
848 | 848 | ['Adam Liška', 'Germán Kruszewski', 'Marco Baroni'] | 1802.06467v1 | Neural networks are very powerful learning systems, but they do not readily
generalize from one task to the other. This is partly due to the fact that they
do not learn in a compositional way, that is, by discovering skills that are
shared by different tasks, and recombining them to solve new problems. In this
paper, w... | Memorize or generalize? Searching for a compositional RNN in a haystack | 2,018 | http://arxiv.org/pdf/1802.06467v1 | Title Memorize generalize Searching compositional RNN haystack Summary Neural network powerful learning system readily generalize one task partly due fact learn compositional way discovering skill shared different task recombining solve new problem paper explore compositional generalization capability recurrent neural ... | [0.029544081538915634, 0.060220688581466675, -0.0016016733134165406, -0.006507180165499449, -0.018431834876537323, -0.021829428151249886, 0.0024944611359387636, -0.01524284016340971, -0.07089319080114365, 0.004815997555851936, -0.011547374539077282, -0.035060349851846695, 0.006641760002821684, 0.00652617821469903, 0.02... |
849 | 849 | ['Christos Kaplanis', 'Murray Shanahan', 'Claudia Clopath'] | 1802.07239v1 | Unlike humans, who are capable of continual learning over their lifetimes,
artificial neural networks have long been known to suffer from a phenomenon
known as catastrophic forgetting, whereby new learning can lead to abrupt
erasure of previously acquired knowledge. Whereas in a neural network the
parameters are typica... | Continual Reinforcement Learning with Complex Synapses | 2,018 | http://arxiv.org/pdf/1802.07239v1 | Title Continual Reinforcement Learning Complex Synapses Summary Unlike human capable continual learning lifetime artificial neural network long known suffer phenomenon known catastrophic forgetting whereby new learning lead abrupt erasure previously acquired knowledge Whereas neural network parameter typically modelled... | [-0.01147836446762085, 0.009462928399443626, -0.021536637097597122, -0.05127372220158577, 0.006826843600720167, 0.022359762340784073, -0.010852308012545109, -0.03854924067854881, -0.016124475747346878, 0.0515826940536499, -0.006582262925803661, -0.002793267834931612, -0.019202113151550293, 0.04928802698850632, 0.002875... |
850 | 850 | ['Abhishek Gupta', 'Russell Mendonca', 'YuXuan Liu', 'Pieter Abbeel', 'Sergey Levine'] | 1802.07245v1 | Exploration is a fundamental challenge in reinforcement learning (RL). Many
of the current exploration methods for deep RL use task-agnostic objectives,
such as information gain or bonuses based on state visitation. However, many
practical applications of RL involve learning more than a single task, and
prior tasks can... | Meta-Reinforcement Learning of Structured Exploration Strategies | 2,018 | http://arxiv.org/pdf/1802.07245v1 | Title MetaReinforcement Learning Structured Exploration Strategies Summary Exploration fundamental challenge reinforcement learning RL Many current exploration method deep RL use taskagnostic objective information gain bonus based state visitation However many practical application RL involve learning single task prior... | [0.022845996543765068, -0.010327394120395184, 0.00637950049713254, -0.044209763407707214, 0.012235644273459911, -0.014252508990466595, 0.008930147625505924, -0.013155504129827023, -0.06443705409765244, -0.008876400999724865, -0.02665579505264759, 0.05503110960125923, -0.04755299538373947, 0.0789950042963028, -0.0079069... |
851 | 851 | ['Matthew Streeter'] | 1802.07697v1 | We present an approximation algorithm that takes a pool of pre-trained models
as input and produces from it a cascaded model with similar accuracy but lower
average-case cost. Applied to state-of-the-art ImageNet classification models,
this yields up to a 2x reduction in floating point multiplications, and up to a
6x r... | Approximation Algorithms for Cascading Prediction Models | 2,018 | http://arxiv.org/pdf/1802.07697v1 | Title Approximation Algorithms Cascading Prediction Models Summary present approximation algorithm take pool pretrained model input produce cascaded model similar accuracy lower averagecase cost Applied stateoftheart ImageNet classification model yield 2x reduction floating point multiplication 6x reduction averagecase... | [-0.01582586206495762, 0.03970373049378395, -0.02020801231265068, 0.02555765211582184, -0.009261867962777615, 0.013759392313659191, 0.0602111853659153, 0.026877330616116524, -0.01674836501479149, 0.02368089184165001, 0.010600359179079533, -0.036255110055208206, 0.01976088061928749, -0.00016322686860803515, -0.037982922... |
852 | 852 | ['Wlodzislaw Duch'] | 1802.08478v1 | Neural networks are commonly regarded as black boxes performing
incomprehensible functions. For classification problems networks provide maps
from high dimensional feature space to K-dimensional image space. Images of
training vector are projected on polygon vertices, providing visualization of
network function. Such v... | Coloring black boxes: visualization of neural network decisions | 2,018 | http://arxiv.org/pdf/1802.08478v1 | Title Coloring black box visualization neural network decision Summary Neural network commonly regarded black box performing incomprehensible function classification problem network provide map high dimensional feature space Kdimensional image space Images training vector projected polygon vertex providing visualizatio... | [-0.027500305324792862, 0.00057623052271083, -0.024883462116122246, 0.01702534779906273, -0.04228878766298294, -0.013837902806699276, 0.06630858778953552, 0.03646942973136902, -0.016635505482554436, 0.013931164517998695, 0.020103393122553825, 0.035459134727716446, 0.01946960762143135, 0.08091599494218826, 0.03291395679... |
853 | 853 | ['Sjoerd van Steenkiste', 'Michael Chang', 'Klaus Greff', 'Jürgen Schmidhuber'] | 1802.10353v1 | Common-sense physical reasoning is an essential ingredient for any
intelligent agent operating in the real-world. For example, it can be used to
simulate the environment, or to infer the state of parts of the world that are
currently unobserved. In order to match real-world conditions this causal
knowledge must be lear... | Relational Neural Expectation Maximization: Unsupervised Discovery of
Objects and their Interactions | 2,018 | http://arxiv.org/pdf/1802.10353v1 | Title Relational Neural Expectation Maximization Unsupervised Discovery Objects Interactions Summary Commonsense physical reasoning essential ingredient intelligent agent operating realworld example used simulate environment infer state part world currently unobserved order match realworld condition causal knowledge mu... | [0.012887672521173954, 0.04423285648226738, 0.008632563054561615, 0.07505037635564804, -0.009485420770943165, -0.02576008066534996, 0.017814286053180695, -0.03150689974427223, 0.03437905013561249, -0.003737053833901882, 0.011476302519440651, 0.010449493303894997, 0.0022367045748978853, 0.07655835151672363, 0.0180681329... |
854 | 854 | ['Henrique X. Goulart', 'Guilherme A. Wachs-Lopes'] | 1802.10393v1 | In events that are composed by many activities, there is a problem that
involves retrieve and management the information of visitors that are visiting
the activities. This management is crucial to find some activities that are
drawing attention of visitors; identify an ideal positioning for activities;
which path is mo... | A Bayesian Model for Activities Recommendation and Event Structure
Optimization Using Visitors Tracking | 2,018 | http://arxiv.org/pdf/1802.10393v1 | Title Bayesian Model Activities Recommendation Event Structure Optimization Using Visitors Tracking Summary event composed many activity problem involves retrieve management information visitor visiting activity management crucial find activity drawing attention visitor identify ideal positioning activity path frequent... | [-0.02488214522600174, -0.020626138895750046, -0.025866858661174774, 0.002105511724948883, -0.015047399327158928, -0.04339215159416199, -0.009036407805979252, -0.015825681388378143, 0.01833384670317173, -0.026449795812368393, -0.002201515482738614, -0.00040884671034291387, 0.018582487478852272, 0.06292422115802765, -0.... |
855 | 855 | ['Jonathan Frankle', 'Michael Carbin'] | 1803.03635v1 | Recent work on neural network pruning indicates that, at training time,
neural networks need to be significantly larger in size than is necessary to
represent the eventual functions that they learn. This paper articulates a new
hypothesis to explain this phenomenon. This conjecture, which we term the
"lottery ticket hy... | The Lottery Ticket Hypothesis: Training Pruned Neural Networks | 2,018 | http://arxiv.org/pdf/1803.03635v1 | Title Lottery Ticket Hypothesis Training Pruned Neural Networks Summary Recent work neural network pruning indicates training time neural network need significantly larger size necessary represent eventual function learn paper articulates new hypothesis explain phenomenon conjecture term lottery ticket hypothesis propo... | [0.004814235493540764, 0.05187557265162468, -0.036668747663497925, 0.024571796879172325, -0.029595941305160522, -0.01452797744423151, 0.025005890056490898, 0.022376859560608864, -0.06452710926532745, 0.029688477516174316, 0.007371921092271805, -0.062144190073013306, 0.014480029232800007, 0.0454886369407177, 0.021643474... |
856 | 856 | ['Christopher Kim', 'Carson Chow'] | 1803.06622v1 | Spiking activity of neurons engaged in learning and performing a task show
complex spatiotemporal dynamics. While the output of recurrent network models
can learn to perform various tasks, the possible range of recurrent dynamics
that emerge after learning remains unknown. Here we show that modifying the
recurrent conn... | Learning recurrent dynamics in spiking networks | 2,018 | http://arxiv.org/pdf/1803.06622v1 | Title Learning recurrent dynamic spiking network Summary Spiking activity neuron engaged learning performing task show complex spatiotemporal dynamic output recurrent network model learn perform various task possible range recurrent dynamic emerge learning remains unknown show modifying recurrent connectivity recursive... | [-0.03337254002690315, -0.05719134956598282, -0.017811812460422516, -0.0037006523925811052, 0.015769802033901215, 0.019087083637714386, -0.02505081333220005, -0.021392086520791054, -0.009612438268959522, 0.004935021046549082, -0.009756585583090782, 0.0027564247138798237, 0.02311229519546032, 0.09632956981658936, 0.0255... |
857 | 857 | ['A. N. Gorban', 'A. Y. Zinovyev'] | 0809.0490v2 | In many physical, statistical, biological and other investigations it is
desirable to approximate a system of points by objects of lower dimension
and/or complexity. For this purpose, Karl Pearson invented principal component
analysis in 1901 and found 'lines and planes of closest fit to system of
points'. The famous k... | Principal Graphs and Manifolds | 2,008 | http://arxiv.org/pdf/0809.0490v2 | Title Principal Graphs Manifolds Summary many physical statistical biological investigation desirable approximate system point object lower dimension andor complexity purpose Karl Pearson invented principal component analysis 1901 found line plane closest fit system point famous kmeans algorithm solves approximation pr... | [-0.05076771602034569, -0.029868997633457184, -0.0129631869494915, 0.026080191135406494, -0.014954281970858574, -0.023005656898021698, 0.009431012906134129, -0.003656127257272601, 0.014218808151781559, 0.019609374925494194, 0.0367770753800869, 0.01792166940867901, 0.013533476740121841, 0.017544740810990334, 0.051993824... |
858 | 858 | ['Xanadu Halkias', 'Sebastien Paris', 'Herve Glotin'] | 1301.3533v2 | Deep Belief Networks (DBN) have been successfully applied on popular machine
learning tasks. Specifically, when applied on hand-written digit recognition,
DBNs have achieved approximate accuracy rates of 98.8%. In an effort to
optimize the data representation achieved by the DBN and maximize their
descriptive power, re... | Sparse Penalty in Deep Belief Networks: Using the Mixed Norm Constraint | 2,013 | http://arxiv.org/pdf/1301.3533v2 | Title Sparse Penalty Deep Belief Networks Using Mixed Norm Constraint Summary Deep Belief Networks DBN successfully applied popular machine learning task Specifically applied handwritten digit recognition DBNs achieved approximate accuracy rate 988 effort optimize data representation achieved DBN maximize descriptive p... | [-0.05097030848264694, 0.11358065903186798, -0.018799735233187675, 0.04781665652990341, -0.004256457556039095, 0.031146284192800522, 0.09198739379644394, 0.025534050539135933, 0.028942802920937538, 0.012865393422544003, -0.00016754117677919567, -0.06164489686489105, 0.002961382968351245, 0.015032391063869, -0.006708024... |
859 | 859 | ['Kyunghyun Cho'] | 1306.2801v4 | In this paper, a simple, general method of adding auxiliary stochastic
neurons to a multi-layer perceptron is proposed. It is shown that the proposed
method is a generalization of recently successful methods of dropout (Hinton et
al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and
semantic has... | Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary
Independent Stochastic Neurons | 2,013 | http://arxiv.org/pdf/1306.2801v4 | Title Understanding Dropout Training MultiLayer Perceptrons Auxiliary Independent Stochastic Neurons Summary paper simple general method adding auxiliary stochastic neuron multilayer perceptron proposed shown proposed method generalization recently successful method dropout Hinton et al 2012 explicit noise injection Vi... | [-0.0067490264773368835, 0.002243395894765854, -0.0053978790529072285, 0.02976827323436737, -0.007662035990506411, 0.004343375097960234, 0.05553474649786949, -0.04696306958794594, -0.001002069446258247, 0.02266785502433777, -0.010503916069865227, 0.03321674466133118, 0.0028659862000495195, 0.07331717759370804, 0.043902... |
860 | 860 | ['Linlin Cao', 'Ran He', 'Bao-Gang Hu'] | 1604.05198v1 | This work is a further study on the Generalized Constraint Neural Network
(GCNN) model [1], [2]. Two challenges are encountered in the study, that is, to
embed any type of prior information and to select its imposing schemes. The
work focuses on the second challenge and studies a new constraint imposing
scheme for equa... | Locally Imposing Function for Generalized Constraint Neural Networks - A
Study on Equality Constraints | 2,016 | http://arxiv.org/pdf/1604.05198v1 | Title Locally Imposing Function Generalized Constraint Neural Networks Study Equality Constraints Summary work study Generalized Constraint Neural Network GCNN model 1 2 Two challenge encountered study embed type prior information select imposing scheme work focus second challenge study new constraint imposing scheme e... | [0.012120881117880344, 0.06214816868305206, -0.016614563763141632, 0.014303338713943958, -0.018026331439614296, -0.07098133116960526, 0.04174258932471275, -0.0015680597862228751, -0.0076708621345460415, 0.01642751693725586, 0.008081131614744663, -0.021789826452732086, -0.00266508967615664, 0.03973434492945671, 0.034251... |
861 | 861 | ['Gabriel Kronberger', 'Michael Kommenda'] | 1305.3794v2 | In this contribution we describe an approach to evolve composite covariance
functions for Gaussian processes using genetic programming. A critical aspect
of Gaussian processes and similar kernel-based models such as SVM is, that the
covariance function should be adapted to the modeled data. Frequently, the
squared expo... | Evolution of Covariance Functions for Gaussian Process Regression using
Genetic Programming | 2,013 | http://arxiv.org/pdf/1305.3794v2 | Title Evolution Covariance Functions Gaussian Process Regression using Genetic Programming Summary contribution describe approach evolve composite covariance function Gaussian process using genetic programming critical aspect Gaussian process similar kernelbased model SVM covariance function adapted modeled data Freque... | [-0.03350526839494705, 0.07776971906423569, -0.022528696805238724, -0.0037425400223582983, 0.006588541436940432, 0.022453904151916504, 0.03150999918580055, -0.01943681761622429, -0.011949557811021805, 0.0022121621295809746, 0.04391717538237572, 0.034531835466623306, -0.000879958737641573, 0.1294478476047516, 0.00305702... |
862 | 862 | ['Nan Wang', 'Jan Melchior', 'Laurenz Wiskott'] | 1401.5900v1 | We present a theoretical analysis of Gaussian-binary restricted Boltzmann
machines (GRBMs) from the perspective of density models. The key aspect of this
analysis is to show that GRBMs can be formulated as a constrained mixture of
Gaussians, which gives a much better insight into the model's capabilities and
limitation... | Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image
Statistics | 2,014 | http://arxiv.org/pdf/1401.5900v1 | Title Gaussianbinary Restricted Boltzmann Machines Modeling Natural Image Statistics Summary present theoretical analysis Gaussianbinary restricted Boltzmann machine GRBMs perspective density model key aspect analysis show GRBMs formulated constrained mixture Gaussians give much better insight model capability limitati... | [0.037242915481328964, -0.025194264948368073, -0.02230438031256199, 0.05198498070240021, -0.0034332561772316694, -0.01782248169183731, 0.045624710619449615, 0.02695692516863346, -0.04261785000562668, -0.01116385217756033, -0.01630368083715439, -0.008443581871688366, 0.022364016622304916, 0.0454404279589653, 0.008694514... |
863 | 863 | ['Siamak Ravanbakhsh', 'Russell Greiner', 'Brendan Frey'] | 1405.1436v1 | A new approach to maximum likelihood learning of discrete graphical models
and RBM in particular is introduced. Our method, Perturb and Descend (PD) is
inspired by two ideas (I) perturb and MAP method for sampling (II) learning by
Contrastive Divergence minimization. In contrast to perturb and MAP, PD
leverages trainin... | Training Restricted Boltzmann Machine by Perturbation | 2,014 | http://arxiv.org/pdf/1405.1436v1 | Title Training Restricted Boltzmann Machine Perturbation Summary new approach maximum likelihood learning discrete graphical model RBM particular introduced method Perturb Descend PD inspired two idea perturb MAP method sampling II learning Contrastive Divergence minimization contrast perturb MAP PD leverage training d... | [-0.013479205779731274, -0.030411183834075928, -0.029634486883878708, 0.02206467278301716, -0.012852332554757595, -0.001959493849426508, 0.02294575795531273, 0.00039903982542455196, 0.005045519210398197, 0.001177957165054977, -0.030495693907141685, 0.04076455906033516, -0.002123118145391345, 0.020608248189091682, 0.038... |
864 | 864 | ['Xiao-Lei Zhang'] | 1408.0848v8 | Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear
network from bottom up for unsupervised nonlinear dimensionality reduction.
Each layer of the network is a nonparametric density estimator. It consists of
a group of k-centroids clusterings. Each clustering randomly selects data
points with r... | Multilayer bootstrap networks | 2,014 | http://arxiv.org/pdf/1408.0848v8 | Title Multilayer bootstrap network Summary Multilayer bootstrap network build gradually narrowed multilayer nonlinear network bottom unsupervised nonlinear dimensionality reduction layer network nonparametric density estimator consists group kcentroids clustering clustering randomly selects data point randomly selected... | [-0.04715077579021454, 0.016762183979153633, -0.039488036185503006, -0.005515534430742264, -0.037119973450899124, -0.019222823902964592, 0.04221014678478241, -0.003004320664331317, -0.06774306297302246, 0.051473766565322876, -0.024001939222216606, 0.024524623528122902, 0.015463277697563171, 0.025538334622979164, 0.0409... |
865 | 865 | ['Sergey Demyanov', 'James Bailey', 'Ramamohanarao Kotagiri', 'Christopher Leckie'] | 1502.04434v3 | In many classification problems a classifier should be robust to small
variations in the input vector. This is a desired property not only for
particular transformations, such as translation and rotation in image
classification problems, but also for all others for which the change is small
enough to retain the object ... | Invariant backpropagation: how to train a transformation-invariant
neural network | 2,015 | http://arxiv.org/pdf/1502.04434v3 | Title Invariant backpropagation train transformationinvariant neural network Summary many classification problem classifier robust small variation input vector desired property particular transformation translation rotation image classification problem also others change small enough retain object perceptually indistin... | [0.015735244378447533, 0.03271784633398056, -0.03202942758798599, 0.018016867339611053, 0.02261238917708397, -0.018340522423386574, 0.025017978623509407, 0.0011434972984716296, -0.05588329955935478, -0.028222892433404922, 0.0007722434820607305, 0.03622652217745781, 0.030474750325083733, 0.004983484745025635, 0.05090219... |
866 | 866 | ['Danila Doroshin', 'Alexander Yamshinin', 'Nikolay Lubimov', 'Marina Nastasenko', 'Mikhail Kotov', 'Maxim Tkachenko'] | 1503.05471v1 | This paper presents a novel approach to speaker subspace modelling based on
Gaussian-Binary Restricted Boltzmann Machines (GRBM). The proposed model is
based on the idea of shared factors as in the Probabilistic Linear Discriminant
Analysis (PLDA). GRBM hidden layer is divided into speaker and channel factors,
herein t... | Shared latent subspace modelling within Gaussian-Binary Restricted
Boltzmann Machines for NIST i-Vector Challenge 2014 | 2,015 | http://arxiv.org/pdf/1503.05471v1 | Title Shared latent subspace modelling within GaussianBinary Restricted Boltzmann Machines NIST iVector Challenge 2014 Summary paper present novel approach speaker subspace modelling based GaussianBinary Restricted Boltzmann Machines GRBM proposed model based idea shared factor Probabilistic Linear Discriminant Analysi... | [0.019772611558437347, 0.007206613197922707, -0.02559203840792179, 0.08306772261857986, -0.011368933133780956, 0.014688745141029358, 0.029226558282971382, -0.027302538976073265, -0.04247799888253212, 0.02238532342016697, -0.05575773864984512, -0.03574942797422409, 0.03611341491341591, 0.017466768622398376, 0.0057443357... |
867 | 867 | ['Sebastian Urban', 'Patrick van der Smagt'] | 1503.05724v3 | Existing approaches to combine both additive and multiplicative neural units
either use a fixed assignment of operations or require discrete optimization to
determine what function a neuron should perform. This leads either to an
inefficient distribution of computational resources or an extensive increase in
the comput... | A Neural Transfer Function for a Smooth and Differentiable Transition
Between Additive and Multiplicative Interactions | 2,015 | http://arxiv.org/pdf/1503.05724v3 | Title Neural Transfer Function Smooth Differentiable Transition Additive Multiplicative Interactions Summary Existing approach combine additive multiplicative neural unit either use fixed assignment operation require discrete optimization determine function neuron perform lead either inefficient distribution computatio... | [-0.02768981270492077, 0.05566762387752533, 0.0052767000161111355, 0.03848479315638542, 0.04850592091679573, -0.008199125528335571, 0.028266819193959236, -0.0469738207757473, -0.0611237995326519, 0.005652204621583223, -0.024118052795529366, -0.042379822582006454, 0.04574961215257645, 0.06665749847888947, 0.034806590527... |
868 | 868 | ['Ankit B. Patel', 'Tan Nguyen', 'Richard G. Baraniuk'] | 1612.01936v1 | We develop a probabilistic framework for deep learning based on the Deep
Rendering Mixture Model (DRMM), a new generative probabilistic model that
explicitly capture variations in data due to latent task nuisance variables. We
demonstrate that max-sum inference in the DRMM yields an algorithm that exactly
reproduces th... | A Probabilistic Framework for Deep Learning | 2,016 | http://arxiv.org/pdf/1612.01936v1 | Title Probabilistic Framework Deep Learning Summary develop probabilistic framework deep learning based Deep Rendering Mixture Model DRMM new generative probabilistic model explicitly capture variation data due latent task nuisance variable demonstrate maxsum inference DRMM yield algorithm exactly reproduces operation ... | [-0.023877430707216263, 0.028987117111682892, -0.0010197419906035066, 0.02622407302260399, 0.01665334403514862, -0.02270248532295227, 0.027868950739502907, -0.021976148709654808, -0.0785486102104187, 0.040055111050605774, 0.025343673303723335, 0.004170405212789774, 0.017193099483847618, 0.06400845944881439, 0.031361274... |
869 | 869 | ['Timothy J. Draelos', 'Nadine E. Miner', 'Christopher C. Lamb', 'Jonathan A. Cox', 'Craig M. Vineyard', 'Kristofor D. Carlson', 'William M. Severa', 'Conrad D. James', 'James B. Aimone'] | 1612.03770v2 | Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages... | Neurogenesis Deep Learning | 2,016 | http://arxiv.org/pdf/1612.03770v2 | Title Neurogenesis Deep Learning Summary Neural machine learning method deep neural network DNN achieved remarkable success number complex data processing task method arguably strongest impact task image audio processing data processing domain human long held clear advantage conventional algorithm contrast biological n... | [0.007181879132986069, 0.07644831389188766, -0.030216600745916367, -0.029585018754005432, 0.05933033302426338, 0.008174392394721508, 0.058180071413517, 0.01502329669892788, -0.03695451468229294, 0.041550710797309875, 0.0292380228638649, 0.0026918291114270687, 0.00036305346293374896, 0.044411759823560715, 0.032330386340... |
870 | 870 | ['Sergey M. Plis', 'Devon R. Hjelm', 'Ruslan Salakhutdinov', 'Vince D. Calhoun'] | 1312.5847v3 | Deep learning methods have recently made notable advances in the tasks of
classification and representation learning. These tasks are important for brain
imaging and neuroscience discovery, making the methods attractive for porting
to a neuroimager's toolbox. Success of these methods is, in part, explained by
the flexi... | Deep learning for neuroimaging: a validation study | 2,013 | http://arxiv.org/pdf/1312.5847v3 | Title Deep learning neuroimaging validation study Summary Deep learning method recently made notable advance task classification representation learning task important brain imaging neuroscience discovery making method attractive porting neuroimagers toolbox Success method part explained flexibility deep learning model... | [-0.01837492361664772, 0.01100934948772192, -0.02576216496527195, -0.012718332931399345, 0.02512184903025627, 0.015584123320877552, 0.04696991294622421, 0.023571521043777466, -6.270770245464519e-05, 0.053926657885313034, -0.05788812041282654, -0.022314229980111122, 0.04795408248901367, 0.06083568558096886, 0.0602912306... |
871 | 871 | ['Jost Tobias Springenberg', 'Martin Riedmiller'] | 1312.6116v2 | We present a probabilistic variant of the recently introduced maxout unit.
The success of deep neural networks utilizing maxout can partly be attributed
to favorable performance under dropout, when compared to rectified linear
units. It however also depends on the fact that each maxout unit performs a
pooling operation... | Improving Deep Neural Networks with Probabilistic Maxout Units | 2,013 | http://arxiv.org/pdf/1312.6116v2 | Title Improving Deep Neural Networks Probabilistic Maxout Units Summary present probabilistic variant recently introduced maxout unit success deep neural network utilizing maxout partly attributed favorable performance dropout compared rectified linear unit however also depends fact maxout unit performs pooling operati... | [-0.005381505936384201, 0.019057119265198708, 0.016910037025809288, 0.03946641832590103, -0.018241185694932938, 0.012659365311264992, 0.048342134803533554, -0.019333038479089737, -0.04003531485795975, 0.022451849654316902, 0.011953772976994514, -0.03398923575878143, 0.002058961894363165, 0.07588708400726318, 0.02126079... |
872 | 872 | ['Fabrice Rossi'] | 1407.0611v1 | In numerous applicative contexts, data are too rich and too complex to be
represented by numerical vectors. A general approach to extend machine learning
and data mining techniques to such data is to really on a dissimilarity or on a
kernel that measures how different or similar two objects are. This approach
has been ... | How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need? | 2,014 | http://arxiv.org/pdf/1407.0611v1 | Title Many DissimilarityKernel Self Organizing Map Variants Need Summary numerous applicative context data rich complex represented numerical vector general approach extend machine learning data mining technique data really dissimilarity kernel measure different similar two object approach used define several variant S... | [-0.03707399219274521, -0.017206881195306778, -0.04620763659477234, 0.03050832822918892, -0.014078105799853802, -0.007796098478138447, 0.07212647050619125, -0.014766501262784004, 0.0195127222687006, -0.016467800363898277, -0.03621876239776611, 0.07111173868179321, -0.01064393576234579, 0.002003050409257412, -0.02756491... |
873 | 873 | ['John R. Hershey', 'Jonathan Le Roux', 'Felix Weninger'] | 1409.2574v4 | Model-based methods and deep neural networks have both been tremendously
successful paradigms in machine learning. In model-based methods, problem
domain knowledge can be built into the constraints of the model, typically at
the expense of difficulties during inference. In contrast, deterministic deep
neural networks a... | Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures | 2,014 | http://arxiv.org/pdf/1409.2574v4 | Title Deep Unfolding ModelBased Inspiration Novel Deep Architectures Summary Modelbased method deep neural network tremendously successful paradigm machine learning modelbased method problem domain knowledge built constraint model typically expense difficulty inference contrast deterministic deep neural network constru... | [-0.04810643196105957, 0.036373239010572433, 0.010821335017681122, 0.016885582357645035, 0.01888921670615673, -0.050883520394563675, 0.04695967584848404, -0.012539033778011799, -0.07194012403488159, 0.028032740578055382, -0.024332191795110703, 0.010094077326357365, 0.02667004056274891, 0.07313326746225357, 0.0220590103... |
874 | 874 | ['Niklas Wahlström', 'Thomas B. Schön', 'Marc Peter Deisenroth'] | 1410.7550v1 | Modeling dynamical systems is important in many disciplines, e.g., control,
robotics, or neurotechnology. Commonly the state of these systems is not
directly observed, but only available through noisy and potentially
high-dimensional observations. In these cases, system identification, i.e.,
finding the measurement map... | Learning deep dynamical models from image pixels | 2,014 | http://arxiv.org/pdf/1410.7550v1 | Title Learning deep dynamical model image pixel Summary Modeling dynamical system important many discipline eg control robotics neurotechnology Commonly state system directly observed available noisy potentially highdimensional observation case system identification ie finding measurement mapping transition mapping sys... | [-0.044298529624938965, 0.030958494171500206, -0.03170099854469299, 0.05849612504243851, -0.0041681816801428795, -0.015608853660523891, 0.04241286963224411, -0.0038738218136131763, -0.06255733221769333, 0.014287787489593029, 0.044057298451662064, 0.01682785153388977, -0.003498351201415062, 0.08716800808906555, 0.041112... |
875 | 875 | ['Harri Valpola'] | 1411.7783v2 | A network supporting deep unsupervised learning is presented. The network is
an autoencoder with lateral shortcut connections from the encoder to decoder at
each level of the hierarchy. The lateral shortcut connections allow the higher
levels of the hierarchy to focus on abstract invariant features. While standard
auto... | From neural PCA to deep unsupervised learning | 2,014 | http://arxiv.org/pdf/1411.7783v2 | Title neural PCA deep unsupervised learning Summary network supporting deep unsupervised learning presented network autoencoder lateral shortcut connection encoder decoder level hierarchy lateral shortcut connection allow higher level hierarchy focus abstract invariant feature standard autoencoders analogous latent var... | [-0.020638780668377876, 0.08852510899305344, -0.011860786005854607, 0.04436541721224785, 0.026439804583787918, 0.018264351412653923, 0.06064463034272194, -0.012935417704284191, -0.04578427970409393, 0.012000597082078457, 0.023024294525384903, 0.0461929589509964, -0.006049367133527994, 0.11999958753585815, 0.02286304533... |
876 | 876 | ['Ian J. Goodfellow', 'Oriol Vinyals', 'Andrew M. Saxe'] | 1412.6544v6 | Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
are... | Qualitatively characterizing neural network optimization problems | 2,014 | http://arxiv.org/pdf/1412.6544v6 | Title Qualitatively characterizing neural network optimization problem Summary Training neural network involves solving largescale nonconvex optimization problem task long believed extremely difficult fear local minimum obstacle motivating variety scheme improve optimization unsupervised pretraining However modern neur... | [-0.026326825842261314, 0.024307403713464737, -0.014821956865489483, 0.03130592033267021, -0.016995619982481003, -0.03342808410525322, 0.008735844865441322, -0.001642549759708345, -0.034666623920202255, 0.01714233122766018, 0.011867056600749493, 0.031946271657943726, -0.0019691416528075933, 0.02467413991689682, 0.04791... |
877 | 877 | ['Arnab Paul', 'Suresh Venkatasubramanian'] | 1412.6621v3 | Why does Deep Learning work? What representations does it capture? How do
higher-order representations emerge? We study these questions from the
perspective of group theory, thereby opening a new approach towards a theory of
Deep learning.
One factor behind the recent resurgence of the subject is a key algorithmic
st... | Why does Deep Learning work? - A perspective from Group Theory | 2,014 | http://arxiv.org/pdf/1412.6621v3 | Title Deep Learning work perspective Group Theory Summary Deep Learning work representation capture higherorder representation emerge study question perspective group theory thereby opening new approach towards theory Deep learning One factor behind recent resurgence subject key algorithmic step called pretraining firs... | [-0.017468933016061783, 0.08319175988435745, -0.036700539290905, -0.01308742817491293, -0.01370431762188673, 0.003490353235974908, 0.08844083547592163, 0.004378741607069969, -0.08824407309293747, 0.004143625032156706, 0.019065510481595993, -0.03476626053452492, -0.005053828004747629, 0.03994399309158325, 0.083658069372... |
878 | 878 | ['Caglar Gulcehre', 'Marcin Moczulski', 'Yoshua Bengio'] | 1412.7419v5 | Stochastic gradient algorithms have been the main focus of large-scale
learning problems and they led to important successes in machine learning. The
convergence of SGD depends on the careful choice of learning rate and the
amount of the noise in stochastic estimates of the gradients. In this paper, we
propose a new ad... | ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient | 2,014 | http://arxiv.org/pdf/1412.7419v5 | Title ADASECANT Robust Adaptive Secant Method Stochastic Gradient Summary Stochastic gradient algorithm main focus largescale learning problem led important success machine learning convergence SGD depends careful choice learning rate amount noise stochastic estimate gradient paper propose new adaptive learning rate al... | [0.009424963966012001, -0.013777581974864006, -0.00192730943672359, 0.027817241847515106, 0.014022969640791416, -0.02599353902041912, 0.032566990703344345, -0.04041124880313873, -0.0035957740619778633, 0.03253929316997528, -0.001778207952156663, -0.020874634385108948, 0.022946560755372047, 0.029129309579730034, 0.00011... |
879 | 879 | ['Yongxin Yang', 'Timothy M. Hospedales'] | 1412.7489v3 | In this paper, we provide a new neural-network based perspective on
multi-task learning (MTL) and multi-domain learning (MDL). By introducing the
concept of a semantic descriptor, this framework unifies MDL and MTL as well as
encompassing various classic and recent MTL/MDL algorithms by interpreting them
as different w... | A Unified Perspective on Multi-Domain and Multi-Task Learning | 2,014 | http://arxiv.org/pdf/1412.7489v3 | Title Unified Perspective MultiDomain MultiTask Learning Summary paper provide new neuralnetwork based perspective multitask learning MTL multidomain learning MDL introducing concept semantic descriptor framework unifies MDL MTL well encompassing various classic recent MTLMDL algorithm interpreting different way constr... | [-0.03835504502058029, -0.0007626500446349382, 0.006105609238147736, 0.002690607914701104, 0.00775135587900877, 0.02102486789226532, 0.08897290378808975, -0.029509609565138817, -0.028897147625684738, -0.0681108683347702, -0.0457310825586319, -0.033412013202905655, -0.03511973097920418, 0.029965292662382126, 0.031980104... |
880 | 880 | ['Robert A. Murphy'] | 1501.07227v5 | The random cluster model is used to define an upper bound on a distance
measure as a function of the number of data points to be classified and the
expected value of the number of classes to form in a hybrid K-means and
regression classification methodology, with the intent of detecting anomalies.
Conditions are given ... | A Neural Network Anomaly Detector Using the Random Cluster Model | 2,015 | http://arxiv.org/pdf/1501.07227v5 | Title Neural Network Anomaly Detector Using Random Cluster Model Summary random cluster model used define upper bound distance measure function number data point classified expected value number class form hybrid Kmeans regression classification methodology intent detecting anomaly Conditions given identification class... | [0.002476252382621169, -0.014347896911203861, -0.020285645499825478, 0.05037723854184151, -0.021373439580202103, -0.014406777918338776, 0.02718907780945301, 0.016552196815609932, 0.02085110917687416, 0.014222637750208378, 0.10309093445539474, 0.022898592054843903, -0.01529272273182869, 0.05098186060786247, 0.0070929424... |
881 | 881 | ['Arnab Paul', 'Suresh Venkatasubramanian'] | 1504.02462v3 | Why does Deep Learning work? What representations does it capture? How do
higher-order representations emerge? We study these questions from the
perspective of group theory, thereby opening a new approach towards a theory of
Deep learning.
One factor behind the recent resurgence of the subject is a key algorithmic
st... | A Group Theoretic Perspective on Unsupervised Deep Learning | 2,015 | http://arxiv.org/pdf/1504.02462v3 | Title Group Theoretic Perspective Unsupervised Deep Learning Summary Deep Learning work representation capture higherorder representation emerge study question perspective group theory thereby opening new approach towards theory Deep learning One factor behind recent resurgence subject key algorithmic step called em pr... | [-0.009884160943329334, 0.07413185387849808, -0.028366418555378914, -0.0016227675369009376, -0.008837842382490635, 0.014756530523300171, 0.07110969722270966, -0.017646580934524536, -0.0825798287987709, 0.0019772634841501713, -0.0016793075483292341, -0.04635702818632126, -0.008833921514451504, 0.03528766334056854, 0.081... |
882 | 882 | ['Yunchen Pu', 'Xin Yuan', 'Lawrence Carin'] | 1504.04054v1 | A generative model is developed for deep (multi-layered) convolutional
dictionary learning. A novel probabilistic pooling operation is integrated into
the deep model, yielding efficient bottom-up (pretraining) and top-down
(refinement) probabilistic learning. Experimental results demonstrate powerful
capabilities of th... | A Generative Model for Deep Convolutional Learning | 2,015 | http://arxiv.org/pdf/1504.04054v1 | Title Generative Model Deep Convolutional Learning Summary generative model developed deep multilayered convolutional dictionary learning novel probabilistic pooling operation integrated deep model yielding efficient bottomup pretraining topdown refinement probabilistic learning Experimental result demonstrate powerful... | [-0.015482412651181221, 0.014986480586230755, -0.0065272171050310135, 0.0221563708037138, -0.005253560375422239, -0.02257644571363926, 0.043189022690057755, -0.03754134103655815, -0.02604890801012516, 0.002766217105090618, 0.05996289104223251, 0.02258172072470188, -0.0118293147534132, 0.07253018766641617, 0.03399497643... |
883 | 883 | ['Zhiyuan Tang', 'Dong Wang', 'Yiqiao Pan', 'Zhiyong Zhang'] | 1506.02256v1 | Pre-training is crucial for learning deep neural networks. Most of existing
pre-training methods train simple models (e.g., restricted Boltzmann machines)
and then stack them layer by layer to form the deep structure. This layer-wise
pre-training has found strong theoretical foundation and broad empirical
support. Howe... | Knowledge Transfer Pre-training | 2,015 | http://arxiv.org/pdf/1506.02256v1 | Title Knowledge Transfer Pretraining Summary Pretraining crucial learning deep neural network existing pretraining method train simple model eg restricted Boltzmann machine stack layer layer form deep structure layerwise pretraining found strong theoretical foundation broad empirical support However easy employ method ... | [-0.012281407602131367, 0.05470841005444527, 0.009022770449519157, 0.024301918223500252, 0.02616831474006176, -0.01433711126446724, 0.016306878998875618, -0.03301497548818588, -0.04225548729300499, -0.01741214282810688, -0.0617535226047039, 0.03170616924762726, -0.0021883330773562193, 0.028970902785658836, 0.0057556550... |
884 | 884 | ['Junbo Zhao', 'Michael Mathieu', 'Ross Goroshin', 'Yann LeCun'] | 1506.02351v8 | We present a novel architecture, the "stacked what-where auto-encoders"
(SWWAE), which integrates discriminative and generative pathways and provides a
unified approach to supervised, semi-supervised and unsupervised learning
without relying on sampling during training. An instantiation of SWWAE uses a
convolutional ne... | Stacked What-Where Auto-encoders | 2,015 | http://arxiv.org/pdf/1506.02351v8 | Title Stacked WhatWhere Autoencoders Summary present novel architecture stacked whatwhere autoencoders SWWAE integrates discriminative generative pathway provides unified approach supervised semisupervised unsupervised learning without relying sampling training instantiation SWWAE us convolutional net Convnet LeCun et ... | [-0.01981442980468273, 0.08821431547403336, -0.01798466593027115, 0.053672514855861664, -0.0015582708874717355, 0.007265186402946711, 0.06560763716697693, -0.007751486264169216, -0.011257002130150795, -0.020326735451817513, -0.043799884617328644, 0.010031813755631447, -0.005495553836226463, 0.05339856073260307, 0.05001... |
885 | 885 | ['Yann Ollivier', 'Corentin Tallec', 'Guillaume Charpiat'] | 1507.07680v2 | We introduce the "NoBackTrack" algorithm to train the parameters of dynamical
systems such as recurrent neural networks. This algorithm works in an online,
memoryless setting, thus requiring no backpropagation through time, and is
scalable, avoiding the large computational and memory cost of maintaining the
full gradie... | Training recurrent networks online without backtracking | 2,015 | http://arxiv.org/pdf/1507.07680v2 | Title Training recurrent network online without backtracking Summary introduce NoBackTrack algorithm train parameter dynamical system recurrent neural network algorithm work online memoryless setting thus requiring backpropagation time scalable avoiding large computational memory cost maintaining full gradient current ... | [-0.010960999876260757, 0.04125826060771942, 0.006236083339899778, -0.0005737123428843915, 0.001664411392994225, -0.019761379808187485, -0.024306312203407288, -0.019674770534038544, -0.020873045548796654, -0.006680076476186514, 0.009317561984062195, 0.023852698504924774, 0.02771538496017456, 0.055159419775009155, 0.042... |
886 | 886 | ['John R. Hershey', 'Zhuo Chen', 'Jonathan Le Roux', 'Shinji Watanabe'] | 1508.04306v1 | We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network ... | Deep clustering: Discriminative embeddings for segmentation and
separation | 2,015 | http://arxiv.org/pdf/1508.04306v1 | Title Deep clustering Discriminative embeddings segmentation separation Summary address problem acoustic source separation deep learning framework call deep clustering Rather directly estimating signal masking function train deep network produce spectrogram embeddings discriminative partition label given training data ... | [-0.031613484025001526, 0.0043369196355342865, 0.003046144498512149, 0.059624943882226944, -0.002430841326713562, -0.03009999915957451, 0.0710962787270546, 0.001544583821669221, -0.053623396903276443, 0.010651652701199055, -0.062092483043670654, 0.024329975247383118, 0.0025264392606914043, 0.01707950234413147, 0.019115... |
887 | 887 | ['Aren Jansen', 'Gregory Sell', 'Vince Lyzinski'] | 1508.04422v3 | Several popular graph embedding techniques for representation learning and
dimensionality reduction rely on performing computationally expensive
eigendecompositions to derive a nonlinear transformation of the input data
space. The resulting eigenvectors encode the embedding coordinates for the
training samples only, an... | Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural
Networks | 2,015 | http://arxiv.org/pdf/1508.04422v3 | Title Scalable OutofSample Extension Graph Embeddings Using Deep Neural Networks Summary Several popular graph embedding technique representation learning dimensionality reduction rely performing computationally expensive eigendecompositions derive nonlinear transformation input data space resulting eigenvectors encode... | [-0.026009386405348778, -0.004638715647161007, -0.00652187317609787, 0.007820820435881615, 0.02224012091755867, 0.008739415556192398, 0.0071080937050282955, -0.002112424233928323, 0.03175557032227516, 0.03627576306462288, 0.00741588743403554, 0.05737767741084099, -0.005602273158729076, 0.007984697818756104, 0.049217622... |
888 | 888 | ['Suyog Gupta', 'Wei Zhang', 'Fei Wang'] | 1509.04210v3 | This paper presents Rudra, a parameter server based distributed computing
framework tuned for training large-scale deep neural networks. Using variants
of the asynchronous stochastic gradient descent algorithm we study the impact
of synchronization protocol, stale gradient updates, minibatch size, learning
rates, and n... | Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A
Systematic Study | 2,015 | http://arxiv.org/pdf/1509.04210v3 | Title Model Accuracy Runtime Tradeoff Distributed Deep LearningA Systematic Study Summary paper present Rudra parameter server based distributed computing framework tuned training largescale deep neural network Using variant asynchronous stochastic gradient descent algorithm study impact synchronization protocol stale ... | [0.0023449964355677366, 0.04158483445644379, 0.010681022889912128, 0.05070742592215538, 0.0064909085631370544, 0.0016931610880419612, 0.06247880682349205, -0.03652351349592209, -0.039995741099119186, 0.03298861160874367, -0.024701006710529327, -0.010304884985089302, 0.02436564676463604, 0.03155328333377838, -0.04015511... |
889 | 889 | ['David Duvenaud', 'Dougal Maclaurin', 'Jorge Aguilera-Iparraguirre', 'Rafael Gómez-Bombarelli', 'Timothy Hirzel', 'Alán Aspuru-Guzik', 'Ryan P. Adams'] | 1509.09292v2 | We introduce a convolutional neural network that operates directly on graphs.
These networks allow end-to-end learning of prediction pipelines whose inputs
are graphs of arbitrary size and shape. The architecture we present generalizes
standard molecular feature extraction methods based on circular fingerprints.
We sho... | Convolutional Networks on Graphs for Learning Molecular Fingerprints | 2,015 | http://arxiv.org/pdf/1509.09292v2 | Title Convolutional Networks Graphs Learning Molecular Fingerprints Summary introduce convolutional neural network operates directly graph network allow endtoend learning prediction pipeline whose input graph arbitrary size shape architecture present generalizes standard molecular feature extraction method based circul... | [0.02733602561056614, 0.013026212342083454, -0.02836785279214382, 0.0018979154992848635, -0.014865434728562832, -0.01779913902282715, 0.04825456440448761, 0.017269223928451538, 0.02236657217144966, 0.035699907690286636, 0.05706275627017021, 0.03128650039434433, -0.021096352487802505, 0.10005316138267517, 0.033814862370... |
890 | 890 | ['Oswin Krause', 'Asja Fischer', 'Christian Igel'] | 1510.01624v4 | Estimating the log-likelihood gradient with respect to the parameters of a
Restricted Boltzmann Machine (RBM) typically requires sampling using Markov
Chain Monte Carlo (MCMC) techniques. To save computation time, the Markov
chains are only run for a small number of steps, which leads to a biased
estimate. This bias ca... | Population-Contrastive-Divergence: Does Consistency help with RBM
training? | 2,015 | http://arxiv.org/pdf/1510.01624v4 | Title PopulationContrastiveDivergence Consistency help RBM training Summary Estimating loglikelihood gradient respect parameter Restricted Boltzmann Machine RBM typically requires sampling using Markov Chain Monte Carlo MCMC technique save computation time Markov chain run small number step lead biased estimate bias ca... | [0.01718243584036827, -0.009649938903748989, -0.010894007980823517, -0.009393826127052307, -0.006687752902507782, -0.025903603062033653, 0.034462735056877136, -0.005490076262503862, -0.05730156973004341, 0.031931161880493164, 0.008940186351537704, 0.0010358005529269576, 0.04077569395303726, 0.005169510841369629, 0.0228... |
891 | 891 | ['Izhar Wallach', 'Michael Dzamba', 'Abraham Heifets'] | 1510.02855v1 | Deep convolutional neural networks comprise a subclass of deep neural
networks (DNN) with a constrained architecture that leverages the spatial and
temporal structure of the domain they model. Convolutional networks achieve the
best predictive performance in areas such as speech and image recognition by
hierarchically ... | AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction
in Structure-based Drug Discovery | 2,015 | http://arxiv.org/pdf/1510.02855v1 | Title AtomNet Deep Convolutional Neural Network Bioactivity Prediction Structurebased Drug Discovery Summary Deep convolutional neural network comprise subclass deep neural network DNN constrained architecture leverage spatial temporal structure domain model Convolutional network achieve best predictive performance are... | [0.010271945968270302, 0.053097814321517944, -0.019327670335769653, 0.025630924850702286, 0.045571018010377884, -0.03217602148652077, 0.06780460476875305, 0.019847920164465904, 0.048916690051555634, 0.017969787120819092, -0.04063716530799866, 0.03772808238863945, -0.016215762123465538, 0.06311431527137756, 0.0283563230... |
892 | 892 | ['Nicolas Papernot', 'Patrick McDaniel', 'Xi Wu', 'Somesh Jha', 'Ananthram Swami'] | 1511.04508v2 | Deep learning algorithms have been shown to perform extremely well on many
classical machine learning problems. However, recent studies have shown that
deep learning, like other machine learning techniques, is vulnerable to
adversarial samples: inputs crafted to force a deep neural network (DNN) to
provide adversary-se... | Distillation as a Defense to Adversarial Perturbations against Deep
Neural Networks | 2,015 | http://arxiv.org/pdf/1511.04508v2 | Title Distillation Defense Adversarial Perturbations Deep Neural Networks Summary Deep learning algorithm shown perform extremely well many classical machine learning problem However recent study shown deep learning like machine learning technique vulnerable adversarial sample input crafted force deep neural network DN... | [0.00978070218116045, 0.056645773351192474, -0.016303276643157005, 0.007887858897447586, -0.03942437097430229, -0.020568089559674263, 0.04720168560743332, -0.01884683221578598, -0.028821565210819244, -0.017682690173387527, 0.023345734924077988, 0.04580553621053696, -0.0046011218801140785, 0.03757333382964134, 0.0346834... |
893 | 893 | ['Dustin Tran', 'Rajesh Ranganath', 'David M. Blei'] | 1511.06499v4 | Variational inference is a powerful tool for approximate inference, and it
has been recently applied for representation learning with deep generative
models. We develop the variational Gaussian process (VGP), a Bayesian
nonparametric variational family, which adapts its shape to match complex
posterior distributions. T... | The Variational Gaussian Process | 2,015 | http://arxiv.org/pdf/1511.06499v4 | Title Variational Gaussian Process Summary Variational inference powerful tool approximate inference recently applied representation learning deep generative model develop variational Gaussian process VGP Bayesian nonparametric variational family adapts shape match complex posterior distribution VGP generates approxima... | [-0.020552849397063255, 0.09730485081672668, -0.01959320902824402, -0.021689001470804214, -0.012611591257154942, -0.008213704451918602, 0.015472936443984509, -0.02307627536356449, -0.08204922080039978, 0.02722841314971447, 0.01838023029267788, -0.0056540523655712605, 0.0158979631960392, 0.08420240879058838, 0.052530273... |
894 | 894 | ['Ilia Zintchenko', 'Matthew Hastings', 'Nathan Wiebe', 'Ethan Brown', 'Matthias Troyer'] | 1512.03025v1 | Heuristic optimisers which search for an optimal configuration of variables
relative to an objective function often get stuck in local optima where the
algorithm is unable to find further improvement. The standard approach to
circumvent this problem involves periodically restarting the algorithm from
random initial con... | Partial Reinitialisation for Optimisers | 2,015 | http://arxiv.org/pdf/1512.03025v1 | Title Partial Reinitialisation Optimisers Summary Heuristic optimisers search optimal configuration variable relative objective function often get stuck local optimum algorithm unable find improvement standard approach circumvent problem involves periodically restarting algorithm random initial configuration improvemen... | [-0.037638649344444275, -0.0100403456017375, -0.012705590575933456, -0.029467321932315826, 0.01377557497471571, -0.05436856299638748, -0.03977998346090317, 0.008652768097817898, -0.03583249822258949, -0.031532831490039825, 0.06650076061487198, 0.03275195509195328, -0.010251748375594616, 0.05194227397441864, -0.02478437... |
895 | 895 | ['Ronen Basri', 'David Jacobs'] | 1602.04723v1 | We consider the ability of deep neural networks to represent data that lies
near a low-dimensional manifold in a high-dimensional space. We show that deep
networks can efficiently extract the intrinsic, low-dimensional coordinates of
such data. We first show that the first two layers of a deep network can
exactly embed... | Efficient Representation of Low-Dimensional Manifolds using Deep
Networks | 2,016 | http://arxiv.org/pdf/1602.04723v1 | Title Efficient Representation LowDimensional Manifolds using Deep Networks Summary consider ability deep neural network represent data lie near lowdimensional manifold highdimensional space show deep network efficiently extract intrinsic lowdimensional coordinate data first show first two layer deep network exactly em... | [-0.08361966162919998, 0.02978036180138588, -0.018027890473604202, 0.045523494482040405, -0.05180705338716507, 0.012776571325957775, 0.04660985991358757, 0.000518214306794107, 0.020451143383979797, 0.00042285610106773674, 0.00424810079857707, 0.040981099009513855, -0.025472229346632957, 0.059133823961019516, 0.06639036... |
896 | 896 | ['André L. V. Coelho', 'Fabrício O. de França'] | 1603.06859v1 | Perceptrons are neuronal devices capable of fully discriminating linearly
separable classes. Although straightforward to implement and train, their
applicability is usually hindered by non-trivial requirements imposed by
real-world classification problems. Therefore, several approaches, such as
kernel perceptrons, have... | Enhanced perceptrons using contrastive biclusters | 2,016 | http://arxiv.org/pdf/1603.06859v1 | Title Enhanced perceptrons using contrastive biclusters Summary Perceptrons neuronal device capable fully discriminating linearly separable class Although straightforward implement train applicability usually hindered nontrivial requirement imposed realworld classification problem Therefore several approach kernel perc... | [-0.025995424017310143, -0.018527960404753685, -0.04075160250067711, 0.07005468755960464, -0.03558843210339546, 0.002725470345467329, 0.07659794390201569, 0.009443908929824829, 0.06532938033342361, -0.012848589569330215, -0.04604990780353546, 0.011575860902667046, -0.01611573062837124, 0.020199354737997055, 0.018403012... |
897 | 897 | ['Tetsuya Sakurai', 'Akira Imakura', 'Yuto Inoue', 'Yasunori Futamura'] | 1605.04639v1 | The backpropagation algorithm for calculating gradients has been widely used
in computation of weights for deep neural networks (DNNs). This method requires
derivatives of objective functions and has some difficulties finding
appropriate parameters such as learning rate. In this paper, we propose a novel
approach for c... | Alternating optimization method based on nonnegative matrix
factorizations for deep neural networks | 2,016 | http://arxiv.org/pdf/1605.04639v1 | Title Alternating optimization method based nonnegative matrix factorization deep neural network Summary backpropagation algorithm calculating gradient widely used computation weight deep neural network DNNs method requires derivative objective function difficulty finding appropriate parameter learning rate paper propo... | [-0.02732893079519272, 0.02925928123295307, -0.006862843409180641, 0.055406827479600906, 0.027116430923342705, -0.028970245271921158, 0.01844571717083454, -0.021081620827317238, -0.022841472178697586, -0.01237912755459547, -0.07110622525215149, -0.017962569370865822, -0.008172176778316498, 0.041417695581912994, 0.03918... |
898 | 898 | ['Jure Sokolic', 'Raja Giryes', 'Guillermo Sapiro', 'Miguel R. D. Rodrigues'] | 1605.08254v3 | The generalization error of deep neural networks via their classification
margin is studied in this work. Our approach is based on the Jacobian matrix of
a deep neural network and can be applied to networks with arbitrary
non-linearities and pooling layers, and to networks with different
architectures such as feed forw... | Robust Large Margin Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1605.08254v3 | Title Robust Large Margin Deep Neural Networks Summary generalization error deep neural network via classification margin studied work approach based Jacobian matrix deep neural network applied network arbitrary nonlinearities pooling layer network different architecture feed forward network residual network analysis l... | [-0.011793049052357674, 0.015552207827568054, -0.02487330697476864, 0.05208481103181839, 0.04645862057805061, -0.029161689803004265, 0.027501219883561134, -0.020609071478247643, -0.0381009578704834, 0.03582538664340973, -0.019938170909881592, 0.03781475871801376, -0.0024499783758074045, 0.018411971628665924, 0.01985204... |
899 | 899 | ['Daniel Soudry', 'Yair Carmon'] | 1605.08361v2 | We use smoothed analysis techniques to provide guarantees on the training
loss of Multilayer Neural Networks (MNNs) at differentiable local minima.
Specifically, we examine MNNs with piecewise linear activation functions,
quadratic loss and a single output, under mild over-parametrization. We prove
that for a MNN with ... | No bad local minima: Data independent training error guarantees for
multilayer neural networks | 2,016 | http://arxiv.org/pdf/1605.08361v2 | Title bad local minimum Data independent training error guarantee multilayer neural network Summary use smoothed analysis technique provide guarantee training loss Multilayer Neural Networks MNNs differentiable local minimum Specifically examine MNNs piecewise linear activation function quadratic loss single output mil... | [-0.02413204498589039, 0.04211192950606346, -0.0025384053587913513, 0.04445235803723335, 0.013818440027534962, -0.03091418743133545, 0.07037099450826645, 0.00656122388318181, -0.02051335759460926, 0.011605988256633282, -0.0040564266964793205, 0.046770330518484116, -0.009697603061795235, 0.022402862086892128, 0.04765422... |
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