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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2,000 | 2,000 | ['Sergey Levine'] | 1311.1761v1 | Sophisticated multilayer neural networks have achieved state of the art
results on multiple supervised tasks. However, successful applications of such
multilayer networks to control have so far been limited largely to the
perception portion of the control pipeline. In this paper, we explore the
application of deep and ... | Exploring Deep and Recurrent Architectures for Optimal Control | 2,013 | http://arxiv.org/pdf/1311.1761v1 | Title Exploring Deep Recurrent Architectures Optimal Control Summary Sophisticated multilayer neural network achieved state art result multiple supervised task However successful application multilayer network control far limited largely perception portion control pipeline paper explore application deep recurrent neura... | [-0.01812690682709217, -0.0016304963501170278, -0.012724400497972965, 0.041018854826688766, -0.007980655878782272, -0.014565364457666874, 0.02182168699800968, -0.014321783557534218, -0.01280233170837164, 0.0027682483196258545, -0.05299396067857742, -0.005680026952177286, 0.000297649297863245, 0.03269273042678833, 0.043... |
2,001 | 2,001 | ['Antoine Cully', 'Jeff Clune', 'Danesh Tarapore', 'Jean-Baptiste Mouret'] | 1407.3501v4 | As robots leave the controlled environments of factories to autonomously
function in more complex, natural environments, they will have to respond to
the inevitable fact that they will become damaged. However, while animals can
quickly adapt to a wide variety of injuries, current robots cannot "think
outside the box" t... | Robots that can adapt like animals | 2,014 | http://arxiv.org/pdf/1407.3501v4 | Title Robots adapt like animal Summary robot leave controlled environment factory autonomously function complex natural environment respond inevitable fact become damaged However animal quickly adapt wide variety injury current robot cannot think outside box find compensatory behavior damaged limited prespecified selfs... | [0.01717059500515461, -0.0015626351814717054, -0.03809599205851555, -0.06259647011756897, 0.017703227698802948, 0.004668708890676498, 0.016614459455013275, 0.025765415281057358, 0.004476375877857208, -0.04502919688820839, 0.02251107431948185, 0.08268415927886963, -0.007880711928009987, 0.08072320371866226, 0.0362542122... |
2,002 | 2,002 | ['Samuel Rönnqvist', 'Peter Sarlin'] | 1507.07870v1 | News is a pertinent source of information on financial risks and stress
factors, which nevertheless is challenging to harness due to the sparse and
unstructured nature of natural text. We propose an approach based on
distributional semantics and deep learning with neural networks to model and
link text to a scarce set ... | Detect & Describe: Deep learning of bank stress in the news | 2,015 | http://arxiv.org/pdf/1507.07870v1 | Title Detect Describe Deep learning bank stress news Summary News pertinent source information financial risk stress factor nevertheless challenging harness due sparse unstructured nature natural text propose approach based distributional semantics deep learning neural network model link text scarce set bank distress e... | [-0.002058073179796338, 0.08103138953447342, -0.019490301609039307, 0.04193035885691643, 0.0015634261071681976, 0.04260951653122902, 0.003340967930853367, 0.05740674212574959, 0.002434351248666644, -0.03450623154640198, 0.00831101555377245, 0.02494077943265438, -0.0042356932535767555, 0.08027864992618561, 0.00114562909... |
2,003 | 2,003 | ['Keisuke Fujii', 'Kohei Nakajima'] | 1602.08159v2 | Quantum computer has an amazing potential of fast information processing.
However, realisation of a digital quantum computer is still a challenging
problem requiring highly accurate controls and key application strategies. Here
we propose a novel platform, quantum reservoir computing, to solve these issues
successfully... | Harnessing disordered quantum dynamics for machine learning | 2,016 | http://arxiv.org/pdf/1602.08159v2 | Title Harnessing disordered quantum dynamic machine learning Summary Quantum computer amazing potential fast information processing However realisation digital quantum computer still challenging problem requiring highly accurate control key application strategy propose novel platform quantum reservoir computing solve i... | [-0.02031875215470791, 0.04621300846338272, -0.07461051642894745, 0.03353401646018028, -0.026260945945978165, 0.0033485046587884426, -0.038478244096040726, 0.010527705773711205, 0.01091979630291462, 0.01661529764533043, 0.013808435760438442, -0.030230678617954254, -0.05104776471853256, 0.05325693637132645, 0.0055174240... |
2,004 | 2,004 | ['Ivo Bukovsky', 'Noriyasu Homma'] | 1606.07149v1 | Stability evaluation of a weight-update system of higher-order neural units
(HONUs) with polynomial aggregation of neural inputs (also known as classes of
polynomial neural networks) for adaptation of both feedforward and recurrent
HONUs by a gradient descent method is introduced. An essential core of the
approach is b... | An Approach to Stable Gradient Descent Adaptation of Higher-Order Neural
Units | 2,016 | http://arxiv.org/pdf/1606.07149v1 | Title Approach Stable Gradient Descent Adaptation HigherOrder Neural Units Summary Stability evaluation weightupdate system higherorder neural unit HONUs polynomial aggregation neural input also known class polynomial neural network adaptation feedforward recurrent HONUs gradient descent method introduced essential cor... | [-0.02515062503516674, 0.027309082448482513, -0.007191912271082401, 0.02789294719696045, 0.07024575024843216, -0.00807950273156166, -0.009035298600792885, -0.005039596930146217, 0.010862094350159168, -0.0057184211909770966, 0.01970582641661167, -0.00034858236904256046, -0.015107551589608192, 0.05062922462821007, 0.0163... |
2,005 | 2,005 | ['Anna Levit', 'Daniel Crawford', 'Navid Ghadermarzy', 'Jaspreet S. Oberoi', 'Ehsan Zahedinejad', 'Pooya Ronagh'] | 1706.00074v1 | Recent theoretical and experimental results suggest the possibility of using
current and near-future quantum hardware in challenging sampling tasks. In this
paper, we introduce free energy-based reinforcement learning (FERL) as an
application of quantum hardware. We propose a method for processing a quantum
annealer's ... | Free energy-based reinforcement learning using a quantum processor | 2,017 | http://arxiv.org/pdf/1706.00074v1 | Title Free energybased reinforcement learning using quantum processor Summary Recent theoretical experimental result suggest possibility using current nearfuture quantum hardware challenging sampling task paper introduce free energybased reinforcement learning FERL application quantum hardware propose method processing... | [-0.029590751975774765, 0.02536092698574066, -0.039754316210746765, 0.029157575219869614, -0.03638704866170883, 0.012705642729997635, -0.042786747217178345, 0.0190141461789608, 0.016105892136693, 0.0266190804541111, -0.02405303157866001, 0.004558765795081854, -0.04941065236926079, 0.030446887016296387, 0.01874563843011... |
2,006 | 2,006 | ['Qiyang Li', 'Xintong Du', 'Yizhou Huang', 'Quinlan Sykora', 'Angela P. Schoellig'] | 1709.06620v1 | Inspired by biological swarms, robotic swarms are envisioned to solve
real-world problems that are difficult for individual agents. Biological swarms
can achieve collective intelligence based on local interactions and simple
rules; however, designing effective distributed policies for large-scale
robotic swarms to achi... | Learning of Coordination Policies for Robotic Swarms | 2,017 | http://arxiv.org/pdf/1709.06620v1 | Title Learning Coordination Policies Robotic Swarms Summary Inspired biological swarm robotic swarm envisioned solve realworld problem difficult individual agent Biological swarm achieve collective intelligence based local interaction simple rule however designing effective distributed policy largescale robotic swarm a... | [0.04172025993466377, -0.02785640023648739, 0.020467156544327736, -0.08492074906826019, -0.006545148324221373, -0.027242621406912804, 0.040079500526189804, -0.051649708300828934, -0.0018864271696656942, 0.006202727556228638, -0.02832169085741043, 0.013419161550700665, 0.0056116352789103985, 0.022869165986776352, -0.007... |
2,007 | 2,007 | ['Marius Stärk', 'Damian Backes', 'Christian Kehl'] | 1711.04518v1 | In this article an automation system for human-machine-interfaces (HMI) for
setpoint adjustment using supervised learning is presented. We use HMIs of
multi-modal thermal conditioning systems in passenger cars as example for a
complex setpoint selection system. The goal is the reduction of interaction
complexity up to ... | A Supervised Learning Concept for Reducing User Interaction in Passenger
Cars | 2,017 | http://arxiv.org/pdf/1711.04518v1 | Title Supervised Learning Concept Reducing User Interaction Passenger Cars Summary article automation system humanmachineinterfaces HMI setpoint adjustment using supervised learning presented use HMIs multimodal thermal conditioning system passenger car example complex setpoint selection system goal reduction interacti... | [-0.051267970353364944, -0.01783495768904686, -0.04232129082083702, 0.03551000729203224, 0.019671272486448288, 0.023947130888700485, 0.07834162563085556, 0.03173733875155449, 0.016758592799305916, -0.034353941679000854, 0.006254457402974367, 0.06817091256380081, 0.03771190717816353, 0.028343942016363144, 0.004085161257... |
2,008 | 2,008 | ['Nathalia Nascimento', 'Carlos Lucena', 'Paulo Alencar', 'Donald Cowan'] | 1802.01096v2 | Several papers have recently contained reports on applying machine learning
(ML) to the automation of software engineering (SE) tasks, such as project
management, modeling and development. However, there appear to be no approaches
comparing how software engineers fare against machine-learning algorithms as
applied to s... | Software Engineers vs. Machine Learning Algorithms: An Empirical Study
Assessing Performance and Reuse Tasks | 2,018 | http://arxiv.org/pdf/1802.01096v2 | Title Software Engineers v Machine Learning Algorithms Empirical Study Assessing Performance Reuse Tasks Summary Several paper recently contained report applying machine learning ML automation software engineering SE task project management modeling development However appear approach comparing software engineer fare m... | [0.0384913794696331, 0.006360893603414297, -0.0469672828912735, 0.016465842723846436, -0.03592854365706444, -0.011814495548605919, 0.03724271059036255, -0.006641787476837635, -0.02455751597881317, -0.04381786659359932, 0.04981112852692604, 0.13979297876358032, -0.004972834140062332, 0.0583360530436039, 0.00965753756463... |
2,009 | 2,009 | ['Philip Graff', 'Farhan Feroz', 'Michael P. Hobson', 'Anthony N. Lasenby'] | 1309.0790v2 | We present the first public release of our generic neural network training
algorithm, called SkyNet. This efficient and robust machine learning tool is
able to train large and deep feed-forward neural networks, including
autoencoders, for use in a wide range of supervised and unsupervised learning
applications, such as... | SKYNET: an efficient and robust neural network training tool for machine
learning in astronomy | 2,013 | http://arxiv.org/pdf/1309.0790v2 | Title SKYNET efficient robust neural network training tool machine learning astronomy Summary present first public release generic neural network training algorithm called SkyNet efficient robust machine learning tool able train large deep feedforward neural network including autoencoders use wide range supervised unsu... | [0.0013033621944487095, 0.02770918980240822, 0.00297247595153749, 0.011962088756263256, 0.020562922582030296, 0.01774660497903824, 0.04850605130195618, 0.00537571357563138, 0.010160477831959724, 0.028806500136852264, -0.017204442992806435, 0.033716946840286255, 0.033303845673799515, 0.032450608909130096, -6.00027924519... |
2,010 | 2,010 | ['Zeyuan Allen-Zhu', 'Elad Hazan'] | 1603.05643v2 | We consider the fundamental problem in non-convex optimization of efficiently
reaching a stationary point. In contrast to the convex case, in the long
history of this basic problem, the only known theoretical results on
first-order non-convex optimization remain to be full gradient descent that
converges in $O(1/\varep... | Variance Reduction for Faster Non-Convex Optimization | 2,016 | http://arxiv.org/pdf/1603.05643v2 | Title Variance Reduction Faster NonConvex Optimization Summary consider fundamental problem nonconvex optimization efficiently reaching stationary point contrast convex case long history basic problem known theoretical result firstorder nonconvex optimization remain full gradient descent converges O1varepsilon iteratio... | [-0.021687358617782593, 0.051756419241428375, 0.0033574197441339493, 0.03755512461066246, 0.030704880133271217, -0.026085689663887024, -0.0361269935965538, -0.009252406656742096, -0.055426280945539474, 0.012872538529336452, -0.06470102816820145, 0.01928531937301159, 0.020311487838625908, 0.06275063753128052, 0.03695965... |
2,011 | 2,011 | ['Christian Beck', 'Weinan E', 'Arnulf Jentzen'] | 1709.05963v1 | High-dimensional partial differential equations (PDE) appear in a number of
models from the financial industry, such as in derivative pricing models,
credit valuation adjustment (CVA) models, or portfolio optimization models. The
PDEs in such applications are high-dimensional as the dimension corresponds to
the number ... | Machine learning approximation algorithms for high-dimensional fully
nonlinear partial differential equations and second-order backward stochastic
differential equations | 2,017 | http://arxiv.org/pdf/1709.05963v1 | Title Machine learning approximation algorithm highdimensional fully nonlinear partial differential equation secondorder backward stochastic differential equation Summary Highdimensional partial differential equation PDE appear number model financial industry derivative pricing model credit valuation adjustment CVA mod... | [-0.005092541687190533, 0.019588958472013474, -0.014310956001281738, -0.028722012415528297, 0.05115117132663727, 0.016217291355133057, 0.06456783413887024, -0.008842868730425835, -0.01795208640396595, 0.027860041707754135, 0.003746198723092675, -0.026106422767043114, -0.0382755808532238, 0.040379174053668976, 0.0211240... |
2,012 | 2,012 | ['Milad Makkie', 'Heng Huang', 'Yu Zhao', 'Athanasios V. Vasilakos', 'Tianming Liu'] | 1710.08961v3 | In recent years, analyzing task-based fMRI (tfMRI) data has become an
essential tool for understanding brain function and networks. However, due to
the sheer size of tfMRI data, its intrinsic complex structure, and lack of
ground truth of underlying neural activities, modeling tfMRI data is hard and
challenging. Previo... | Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI
Big Data Analytics | 2,017 | http://arxiv.org/pdf/1710.08961v3 | Title Fast Scalable Distributed Deep Convolutional Autoencoder fMRI Big Data Analytics Summary recent year analyzing taskbased fMRI tfMRI data become essential tool understanding brain function network However due sheer size tfMRI data intrinsic complex structure lack ground truth underlying neural activity modeling tf... | [-0.026247989386320114, 0.07213917374610901, -0.05202491581439972, 0.05725902318954468, 0.032201915979385376, 0.05146174132823944, 0.07805132865905762, 0.0352652482688427, -0.0056503708474338055, 0.04314083233475685, -0.056958772242069244, -0.026309439912438393, 0.02684960700571537, 0.09834939986467361, 0.0395484492182... |
2,013 | 2,013 | ['Kamil Nar', 'Shankar Sastry'] | 1803.08203v1 | While training error of most deep neural networks degrades as the depth of
the network increases, residual networks appear to be an exception. We show
that the main reason for this is the Lyapunov stability of the gradient descent
algorithm: for an arbitrarily chosen step size, the equilibria of the gradient
descent ar... | Residual Networks: Lyapunov Stability and Convex Decomposition | 2,018 | http://arxiv.org/pdf/1803.08203v1 | Title Residual Networks Lyapunov Stability Convex Decomposition Summary training error deep neural network degrades depth network increase residual network appear exception show main reason Lyapunov stability gradient descent algorithm arbitrarily chosen step size equilibrium gradient descent likely remain stable param... | [-0.03477063030004501, 0.04301203414797783, -0.007012563291937113, 0.038955360651016235, 0.03203439339995384, -0.02981133945286274, -0.019885094836354256, -0.011481289751827717, -0.057549115270376205, -0.007173300255089998, -0.00899304635822773, 0.02435251511633396, -0.00628232304006815, 0.03490883484482765, -0.0050924... |
2,014 | 2,014 | ['Ken Miura', 'Tatsuya Harada'] | 1503.05743v1 | Deep learning can achieve outstanding results in various fields. However, it
requires so significant computational power that graphics processing units
(GPUs) and/or numerous computers are often required for the practical
application. We have developed a new distributed calculation framework called
"Sashimi" that allow... | Implementation of a Practical Distributed Calculation System with
Browsers and JavaScript, and Application to Distributed Deep Learning | 2,015 | http://arxiv.org/pdf/1503.05743v1 | Title Implementation Practical Distributed Calculation System Browsers JavaScript Application Distributed Deep Learning Summary Deep learning achieve outstanding result various field However requires significant computational power graphic processing unit GPUs andor numerous computer often required practical applicatio... | [-0.018067961558699608, 0.054072119295597076, -0.02249765396118164, 0.0032335149589926004, -0.03342723101377487, -0.011014917865395546, 0.11566087603569031, -0.011257179081439972, -0.03878922760486603, -0.004986946005374193, -0.023649191483855247, -0.0246924739331007, 0.05027389153838158, 0.08078402280807495, -0.010249... |
2,015 | 2,015 | ['Niek Tax', 'Ilya Verenich', 'Marcello La Rosa', 'Marlon Dumas'] | 1612.02130v2 | Predictive business process monitoring methods exploit logs of completed
cases of a process in order to make predictions about running cases thereof.
Existing methods in this space are tailor-made for specific prediction tasks.
Moreover, their relative accuracy is highly sensitive to the dataset at hand,
thus requiring... | Predictive Business Process Monitoring with LSTM Neural Networks | 2,016 | http://arxiv.org/pdf/1612.02130v2 | Title Predictive Business Process Monitoring LSTM Neural Networks Summary Predictive business process monitoring method exploit log completed case process order make prediction running case thereof Existing method space tailormade specific prediction task Moreover relative accuracy highly sensitive dataset hand thus re... | [-0.006255712825804949, 0.006085309199988842, -0.0003740624524652958, 0.0015210873680189252, -0.027735453099012375, -0.033664148300886154, -0.02745058946311474, 0.015885265544056892, -0.016868505626916885, -0.003363007679581642, 0.017423110082745552, -0.019847236573696136, -0.02224508859217167, 0.10149114578962326, -0.... |
2,016 | 2,016 | ['Yasser Roudi', 'Graham Taylor'] | 1506.00354v2 | Learning and inferring features that generate sensory input is a task
continuously performed by cortex. In recent years, novel algorithms and
learning rules have been proposed that allow neural network models to learn
such features from natural images, written text, audio signals, etc. These
networks usually involve de... | Learning with hidden variables | 2,015 | http://arxiv.org/pdf/1506.00354v2 | Title Learning hidden variable Summary Learning inferring feature generate sensory input task continuously performed cortex recent year novel algorithm learning rule proposed allow neural network model learn feature natural image written text audio signal etc network usually involve deep architecture many layer hidden ... | [-0.0007743870373815298, -0.03279505670070648, -0.03820051625370979, 0.02613869123160839, -0.0005967774195596576, -0.00454524252563715, 0.03191288188099861, 0.02065906673669815, 0.0454266220331192, 0.007685813121497631, -0.02416374161839485, 0.07315533608198166, 0.027180617675185204, 0.04847079515457153, 0.031994044780... |
2,017 | 2,017 | ['Philipp Moritz', 'Robert Nishihara', 'Ion Stoica', 'Michael I. Jordan'] | 1511.06051v4 | Training deep networks is a time-consuming process, with networks for object
recognition often requiring multiple days to train. For this reason, leveraging
the resources of a cluster to speed up training is an important area of work.
However, widely-popular batch-processing computational frameworks like
MapReduce and ... | SparkNet: Training Deep Networks in Spark | 2,015 | http://arxiv.org/pdf/1511.06051v4 | Title SparkNet Training Deep Networks Spark Summary Training deep network timeconsuming process network object recognition often requiring multiple day train reason leveraging resource cluster speed training important area work However widelypopular batchprocessing computational framework like MapReduce Spark designed ... | [-0.03600901737809181, -0.011357429437339306, -0.013876308687031269, 0.10761848092079163, -0.0030507389456033707, 0.010952086187899113, 0.07274629920721054, 0.006284222938120365, -0.018066421151161194, 0.01399421039968729, -0.004684088286012411, 0.04369967430830002, -0.013751113787293434, 0.06591138988733292, -0.031525... |
2,018 | 2,018 | ['Miguel Á. Carreira-Perpiñán', 'Mehdi Alizadeh'] | 1605.09114v1 | Many powerful machine learning models are based on the composition of
multiple processing layers, such as deep nets, which gives rise to nonconvex
objective functions. A general, recent approach to optimise such "nested"
functions is the method of auxiliary coordinates (MAC). MAC introduces an
auxiliary coordinate for ... | ParMAC: distributed optimisation of nested functions, with application
to learning binary autoencoders | 2,016 | http://arxiv.org/pdf/1605.09114v1 | Title ParMAC distributed optimisation nested function application learning binary autoencoders Summary Many powerful machine learning model based composition multiple processing layer deep net give rise nonconvex objective function general recent approach optimise nested function method auxiliary coordinate MAC MAC int... | [-0.011301681399345398, 0.08279135823249817, -0.029512060806155205, 0.05706649273633957, 0.00954418070614338, 0.022494150325655937, 0.09995493292808533, -0.005045757628977299, -0.03464629128575325, -0.015753133222460747, -0.03685590997338295, -0.004006894305348396, 0.013802451081573963, 0.0744948461651802, 0.0261586233... |
2,019 | 2,019 | ['Raja Giryes', 'Yonina C. Eldar', 'Alex M. Bronstein', 'Guillermo Sapiro'] | 1605.09232v3 | Solving inverse problems with iterative algorithms is popular, especially for
large data. Due to time constraints, the number of possible iterations is
usually limited, potentially affecting the achievable accuracy. Given an error
one is willing to tolerate, an important question is whether it is possible to
modify the... | Tradeoffs between Convergence Speed and Reconstruction Accuracy in
Inverse Problems | 2,016 | http://arxiv.org/pdf/1605.09232v3 | Title Tradeoffs Convergence Speed Reconstruction Accuracy Inverse Problems Summary Solving inverse problem iterative algorithm popular especially large data Due time constraint number possible iteration usually limited potentially affecting achievable accuracy Given error one willing tolerate important question whether... | [-0.014694998040795326, 0.05637459456920624, -0.005076027940958738, -0.01117612887173891, -0.02791164629161358, -0.030020011588931084, -0.01772126369178295, 0.07129532843828201, -0.03703445568680763, 0.07119801640510559, 0.0352848656475544, -0.047085702419281006, 0.006755237001925707, 0.004361252300441265, 0.0133960340... |
2,020 | 2,020 | ['Dmitry Krotov', 'John J Hopfield'] | 1606.01164v2 | A model of associative memory is studied, which stores and reliably retrieves
many more patterns than the number of neurons in the network. We propose a
simple duality between this dense associative memory and neural networks
commonly used in deep learning. On the associative memory side of this duality,
a family of mo... | Dense Associative Memory for Pattern Recognition | 2,016 | http://arxiv.org/pdf/1606.01164v2 | Title Dense Associative Memory Pattern Recognition Summary model associative memory studied store reliably retrieves many pattern number neuron network propose simple duality dense associative memory neural network commonly used deep learning associative memory side duality family model smoothly interpolates two limiti... | [-0.017949843779206276, 0.013669214211404324, -0.042994752526283264, 0.1022338941693306, 0.011293089017271996, 0.005240985192358494, 0.04680291563272476, -0.00044335110578686, 0.0091016236692667, 0.00502577843144536, -0.010723235085606575, -0.045773450285196304, -0.0005698268068954349, 0.1003163754940033, 0.01505169831... |
2,021 | 2,021 | ['Gilles Louppe', 'Michael Kagan', 'Kyle Cranmer'] | 1611.01046v3 | Several techniques for domain adaptation have been proposed to account for
differences in the distribution of the data used for training and testing. The
majority of this work focuses on a binary domain label. Similar problems occur
in a scientific context where there may be a continuous family of plausible
data genera... | Learning to Pivot with Adversarial Networks | 2,016 | http://arxiv.org/pdf/1611.01046v3 | Title Learning Pivot Adversarial Networks Summary Several technique domain adaptation proposed account difference distribution data used training testing majority work focus binary domain label Similar problem occur scientific context may continuous family plausible data generation process associated presence systemati... | [-0.00578206917271018, 0.030609216541051865, -0.009134912863373756, 0.03254467993974686, 0.005282165482640266, -0.037463292479515076, 0.05304005742073059, -0.008202996104955673, -0.02523788809776306, -0.008198181167244911, -0.024386510252952576, 0.010031395591795444, -0.005763475317507982, 0.08096377551555634, 0.010937... |
2,022 | 2,022 | ['Benjamin Paul Chamberlain', 'Angelo Cardoso', 'C. H. Bryan Liu', 'Roberto Pagliari', 'Marc Peter Deisenroth'] | 1703.02596v3 | We describe the Customer LifeTime Value (CLTV) prediction system deployed at
ASOS.com, a global online fashion retailer. CLTV prediction is an important
problem in e-commerce where an accurate estimate of future value allows
retailers to effectively allocate marketing spend, identify and nurture high
value customers an... | Customer Lifetime Value Prediction Using Embeddings | 2,017 | http://arxiv.org/pdf/1703.02596v3 | Title Customer Lifetime Value Prediction Using Embeddings Summary describe Customer LifeTime Value CLTV prediction system deployed ASOScom global online fashion retailer CLTV prediction important problem ecommerce accurate estimate future value allows retailer effectively allocate marketing spend identify nurture high ... | [0.011920434422791004, 0.019795875996351242, -0.04552529752254486, 0.004753754939883947, 0.059290435165166855, 0.02248445712029934, 0.032154809683561325, -0.0207077544182539, 0.031140625476837158, -0.01666601374745369, 0.020840253680944443, 0.005243254359811544, -0.009288365021348, 0.10536667704582214, -0.0026288863737... |
2,023 | 2,023 | ['Peva Blanchard', 'El Mahdi El Mhamdi', 'Rachid Guerraoui', 'Julien Stainer'] | 1703.02757v1 | The growth of data, the need for scalability and the complexity of models
used in modern machine learning calls for distributed implementations. Yet, as
of today, distributed machine learning frameworks have largely ignored the
possibility of arbitrary (i.e., Byzantine) failures. In this paper, we study
the robustness ... | Byzantine-Tolerant Machine Learning | 2,017 | http://arxiv.org/pdf/1703.02757v1 | Title ByzantineTolerant Machine Learning Summary growth data need scalability complexity model used modern machine learning call distributed implementation Yet today distributed machine learning framework largely ignored possibility arbitrary ie Byzantine failure paper study robustness Byzantine failure fundamental lev... | [0.014003139920532703, 0.0076744030229747295, -0.0024432498030364513, 0.0038727535866200924, -0.03820353373885155, -0.023203250020742416, 0.051640622317790985, 0.0031589900609105825, -0.006378535646945238, 0.03524745628237724, 0.02720540203154087, 0.007371662184596062, -0.00945590902119875, 0.03931954503059387, 0.00459... |
2,024 | 2,024 | ['Chris Donahue', 'Zachary C. Lipton', 'Julian McAuley'] | 1703.06891v3 | Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players
perform steps on a dance platform in synchronization with music as directed by
on-screen step charts. While many step charts are available in standardized
packs, players may grow tired of existing charts, or wish to dance to a song
for which no ... | Dance Dance Convolution | 2,017 | http://arxiv.org/pdf/1703.06891v3 | Title Dance Dance Convolution Summary Dance Dance Revolution DDR popular rhythmbased video game Players perform step dance platform synchronization music directed onscreen step chart many step chart available standardized pack player may grow tired existing chart wish dance song chart exists introduce task learning cho... | [-0.02125982753932476, -0.014263171702623367, -0.0072663212195038795, 0.021834857761859894, 0.026882274076342583, -0.019840974360704422, 0.024172013625502586, 0.006754991598427296, -0.0362471267580986, 0.029287822544574738, -0.026448026299476624, -0.009825498796999454, -0.008124668151140213, 0.06087211146950722, 0.0309... |
2,025 | 2,025 | ['Ran Rubin', 'L. F. Abbott', 'Haim Sompolinsky'] | 1705.01502v1 | Neurons and networks in the cerebral cortex must operate reliably despite
multiple sources of noise. To evaluate the impact of both input and output
noise, we determine the robustness of single-neuron stimulus selective
responses, as well as the robustness of attractor states of networks of neurons
performing memory ta... | Balanced Excitation and Inhibition are Required for High-Capacity,
Noise-Robust Neuronal Selectivity | 2,017 | http://arxiv.org/pdf/1705.01502v1 | Title Balanced Excitation Inhibition Required HighCapacity NoiseRobust Neuronal Selectivity Summary Neurons network cerebral cortex must operate reliably despite multiple source noise evaluate impact input output noise determine robustness singleneuron stimulus selective response well robustness attractor state network... | [-0.007646242622286081, -0.02206738106906414, -0.027318857610225677, 0.009025407023727894, 0.010219786316156387, -0.0004709403438027948, -0.013681769371032715, -0.022798771038651466, -0.03567575290799141, -0.0075999898836016655, -0.02909901738166809, -0.011098186485469341, 0.015837835147976875, 0.05734848603606224, 0.0... |
2,026 | 2,026 | ['Weinan E', 'Jiequn Han', 'Arnulf Jentzen'] | 1706.04702v1 | We propose a new algorithm for solving parabolic partial differential
equations (PDEs) and backward stochastic differential equations (BSDEs) in high
dimension, by making an analogy between the BSDE and reinforcement learning
with the gradient of the solution playing the role of the policy function, and
the loss functi... | Deep learning-based numerical methods for high-dimensional parabolic
partial differential equations and backward stochastic differential equations | 2,017 | http://arxiv.org/pdf/1706.04702v1 | Title Deep learningbased numerical method highdimensional parabolic partial differential equation backward stochastic differential equation Summary propose new algorithm solving parabolic partial differential equation PDEs backward stochastic differential equation BSDEs high dimension making analogy BSDE reinforcement ... | [-0.024330155923962593, 0.0095877880230546, -0.01567768305540085, -0.019662097096443176, 0.0329156294465065, -0.0012965750647708774, 0.05849071219563484, -0.009511989541351795, -0.025799810886383057, 0.014982212334871292, -0.0008000397938303649, -0.0082828588783741, -0.04176219552755356, 0.02431187592446804, 0.02580942... |
2,027 | 2,027 | ['Zeyuan Allen-Zhu'] | 1708.08694v3 | We design a stochastic algorithm to train any smooth neural network to
$\varepsilon$-approximate local minima, using $O(\varepsilon^{-3.25})$
backpropagations. The best result was essentially $O(\varepsilon^{-4})$ by SGD.
More broadly, it finds $\varepsilon$-approximate local minima of any smooth
nonconvex function i... | Natasha 2: Faster Non-Convex Optimization Than SGD | 2,017 | http://arxiv.org/pdf/1708.08694v3 | Title Natasha 2 Faster NonConvex Optimization SGD Summary design stochastic algorithm train smooth neural network varepsilonapproximate local minimum using Ovarepsilon325 backpropagations best result essentially Ovarepsilon4 SGD broadly find varepsilonapproximate local minimum smooth nonconvex function rate Ovarepsilon... | [-0.008425440639257431, 0.037313465029001236, 0.0021528340876102448, 0.06180174648761749, 0.017349978908896446, -0.028018075972795486, -0.019652241840958595, -0.0030623753555119038, -0.05035003274679184, 0.009007447399199009, 0.016909301280975342, -0.04319723695516586, 0.006903092376887798, 0.05348312482237816, 0.02222... |
2,028 | 2,028 | ['Zeyuan Allen-Zhu', 'Yuanzhi Li'] | 1711.06673v2 | We propose a reduction for non-convex optimization that can (1) turn an
stationary-point finding algorithm into an local-minimum finding one, and (2)
replace the Hessian-vector product computations with only gradient
computations. It works both in the stochastic and the deterministic settings,
without hurting the algor... | Neon2: Finding Local Minima via First-Order Oracles | 2,017 | http://arxiv.org/pdf/1711.06673v2 | Title Neon2 Finding Local Minima via FirstOrder Oracles Summary propose reduction nonconvex optimization 1 turn stationarypoint finding algorithm localminimum finding one 2 replace Hessianvector product computation gradient computation work stochastic deterministic setting without hurting algorithm performance applicat... | [-0.005456711631268263, 0.01931118033826351, 0.0056770481169223785, 0.013831201009452343, 0.007601416669785976, -0.048116303980350494, 0.013809758238494396, 0.02284700982272625, -0.008668143302202225, 0.017026616260409355, 0.04537370428442955, 0.01841217279434204, 0.03634457662701607, -0.012530078180134296, -0.00762208... |
2,029 | 2,029 | ['Aditya Gilra', 'Wulfram Gerstner'] | 1712.10158v1 | Learning weights in a spiking neural network with hidden neurons, using
local, stable and online rules, to control non-linear body dynamics is an open
problem. Here, we employ a supervised scheme, Feedback-based Online Local
Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking
neurons with hidden l... | Non-linear motor control by local learning in spiking neural networks | 2,017 | http://arxiv.org/pdf/1712.10158v1 | Title Nonlinear motor control local learning spiking neural network Summary Learning weight spiking neural network hidden neuron using local stable online rule control nonlinear body dynamic open problem employ supervised scheme Feedbackbased Online Local Learning Weights FOLLOW train network heterogeneous spiking neur... | [-0.012003475800156593, 0.005737202242016792, -0.008865077048540115, 0.0027784772682935, 0.039121419191360474, -0.015537511557340622, -0.007534600794315338, -0.00042721806676127017, -0.007622255012392998, -0.03883801028132439, -0.04280460998415947, 0.0296793133020401, -0.014119190163910389, 0.07034950703382492, 0.04173... |
2,030 | 2,030 | ['William H. Guss', 'Ruslan Salakhutdinov'] | 1802.04443v1 | The learnability of different neural architectures can be characterized
directly by computable measures of data complexity. In this paper, we reframe
the problem of architecture selection as understanding how data determines the
most expressive and generalizable architectures suited to that data, beyond
inductive bias.... | On Characterizing the Capacity of Neural Networks using Algebraic
Topology | 2,018 | http://arxiv.org/pdf/1802.04443v1 | Title Characterizing Capacity Neural Networks using Algebraic Topology Summary learnability different neural architecture characterized directly computable measure data complexity paper reframe problem architecture selection understanding data determines expressive generalizable architecture suited data beyond inductiv... | [-0.06849805265665054, 0.03658169135451317, -0.052782222628593445, 0.00018513103714212775, -0.06804034113883972, -0.031000711023807526, 0.008091111667454243, -0.05223986506462097, -0.016717880964279175, 0.07998142391443253, -0.025118285790085793, -0.034265220165252686, 0.006439163349568844, 0.08858127892017365, 0.05770... |
2,031 | 2,031 | ['Alain Lifchitz', 'Sandra Jhean-Larose', 'Guy Denhière'] | 0811.0146v3 | This paper presents the current state of a work in progress, whose objective
is to better understand the effects of factors that significantly influence the
performance of Latent Semantic Analysis (LSA). A difficult task, which consists
in answering (French) biology Multiple Choice Questions, is used to test the
semant... | Effect of Tuned Parameters on a LSA MCQ Answering Model | 2,008 | http://arxiv.org/pdf/0811.0146v3 | Title Effect Tuned Parameters LSA MCQ Answering Model Summary paper present current state work progress whose objective better understand effect factor significantly influence performance Latent Semantic Analysis LSA difficult task consists answering French biology Multiple Choice Questions used test semantic property ... | [0.04870137572288513, -0.014742557890713215, -0.01124025508761406, 0.03252638131380081, 0.00900041963905096, -0.00010843017662409693, 0.009053592570126057, -0.006825895048677921, -0.07263848930597305, -0.062038227915763855, 0.0005176533595658839, 0.026962295174598694, -0.03885701298713684, -0.014458428137004375, 0.0311... |
2,032 | 2,032 | ['Jonathan Huang', 'Carlos Guestrin'] | 1006.1328v1 | Representing distributions over permutations can be a daunting task due to
the fact that the number of permutations of $n$ objects scales factorially in
$n$. One recent way that has been used to reduce storage complexity has been to
exploit probabilistic independence, but as we argue, full independence
assumptions impo... | Uncovering the Riffled Independence Structure of Rankings | 2,010 | http://arxiv.org/pdf/1006.1328v1 | Title Uncovering Riffled Independence Structure Rankings Summary Representing distribution permutation daunting task due fact number permutation n object scale factorially n One recent way used reduce storage complexity exploit probabilistic independence argue full independence assumption impose strong sparsity constra... | [0.01070319302380085, 0.03744146600365639, -0.040114667266607285, 0.04331768676638603, -0.04185255616903305, -0.020824437960982323, 0.001161194872111082, -0.0009177264873869717, -0.05335071310400963, -0.020563025027513504, 0.00015713978791609406, 0.014739014208316803, 0.018826432526111603, -0.02269006334245205, -0.0313... |
2,033 | 2,033 | ['Stephane Ross', 'Geoffrey J. Gordon', 'J. Andrew Bagnell'] | 1011.0686v3 | Sequential prediction problems such as imitation learning, where future
observations depend on previous predictions (actions), violate the common
i.i.d. assumptions made in statistical learning. This leads to poor performance
in theory and often in practice. Some recent approaches provide stronger
guarantees in this se... | A Reduction of Imitation Learning and Structured Prediction to No-Regret
Online Learning | 2,010 | http://arxiv.org/pdf/1011.0686v3 | Title Reduction Imitation Learning Structured Prediction NoRegret Online Learning Summary Sequential prediction problem imitation learning future observation depend previous prediction action violate common iid assumption made statistical learning lead poor performance theory often practice recent approach provide stro... | [0.0204716045409441, 0.020499711856245995, 0.010235220193862915, -0.04037196561694145, -0.008327016606926918, 0.0036689976695924997, 0.007895061746239662, 0.021950384601950645, 0.0015781872207298875, -0.012559520080685616, 0.005670324433594942, -0.034390415996313095, -0.005173556040972471, 0.04169555380940437, -0.00796... |
2,034 | 2,034 | ['Shakir Mohamed', 'Katherine Heller', 'Zoubin Ghahramani'] | 1106.1157v3 | The use of L1 regularisation for sparse learning has generated immense
research interest, with successful application in such diverse areas as signal
acquisition, image coding, genomics and collaborative filtering. While existing
work highlights the many advantages of L1 methods, in this paper we find that
L1 regularis... | Bayesian and L1 Approaches to Sparse Unsupervised Learning | 2,011 | http://arxiv.org/pdf/1106.1157v3 | Title Bayesian L1 Approaches Sparse Unsupervised Learning Summary use L1 regularisation sparse learning generated immense research interest successful application diverse area signal acquisition image coding genomics collaborative filtering existing work highlight many advantage L1 method paper find L1 regularisation o... | [-0.017403138801455498, 0.07317555695772171, -0.003959259018301964, -0.012596470303833485, 0.0026626905892044306, 0.011355162598192692, 0.06071782857179642, 0.07096890360116959, -0.06533068418502808, 0.01665109395980835, 0.03860961273312569, 0.011163071729242802, 0.06076592952013016, 0.04308663681149483, -0.00667293043... |
2,035 | 2,035 | ['Stephane Ross', 'J. Andrew Bagnell'] | 1203.1007v2 | A fundamental problem in control is to learn a model of a system from
observations that is useful for controller synthesis. To provide good
performance guarantees, existing methods must assume that the real system is in
the class of models considered during learning. We present an iterative method
with strong guarantee... | Agnostic System Identification for Model-Based Reinforcement Learning | 2,012 | http://arxiv.org/pdf/1203.1007v2 | Title Agnostic System Identification ModelBased Reinforcement Learning Summary fundamental problem control learn model system observation useful controller synthesis provide good performance guarantee existing method must assume real system class model considered learning present iterative method strong guarantee even ... | [-0.028020884841680527, 0.026918133720755577, 0.0014182617887854576, -0.02571752853691578, 0.010957300662994385, -0.03969736769795418, 0.011495900340378284, -0.0029728510417044163, -0.03642187640070915, -0.010284488089382648, -0.006900146137923002, 0.05500250309705734, -0.01847567781805992, 0.07307399809360504, 0.01990... |
2,036 | 2,036 | ['Ryan Rossi', 'Brian Gallagher', 'Jennifer Neville', 'Keith Henderson'] | 1203.2200v1 | To understand the structural dynamics of a large-scale social, biological or
technological network, it may be useful to discover behavioral roles
representing the main connectivity patterns present over time. In this paper,
we propose a scalable non-parametric approach to automatically learn the
structural dynamics of ... | Role-Dynamics: Fast Mining of Large Dynamic Networks | 2,012 | http://arxiv.org/pdf/1203.2200v1 | Title RoleDynamics Fast Mining Large Dynamic Networks Summary understand structural dynamic largescale social biological technological network may useful discover behavioral role representing main connectivity pattern present time paper propose scalable nonparametric approach automatically learn structural dynamic netw... | [-0.017037220299243927, -0.021922385320067406, -0.0523039847612381, -0.02144777402281761, -0.019708188250660896, -0.047278933227062225, -0.025712937116622925, -0.03269904479384422, 0.009306465275585651, 0.005421167705208063, 0.008689609356224537, 0.024475054815411568, -0.007395640481263399, 0.09341133385896683, 0.01910... |
2,037 | 2,037 | ['Jonathan Sorg', 'Satinder Singh', 'Richard L. Lewis'] | 1203.3518v1 | The explore{exploit dilemma is one of the central challenges in Reinforcement
Learning (RL). Bayesian RL solves the dilemma by providing the agent with
information in the form of a prior distribution over environments; however,
full Bayesian planning is intractable. Planning with the mean MDP is a common
myopic approxi... | Variance-Based Rewards for Approximate Bayesian Reinforcement Learning | 2,012 | http://arxiv.org/pdf/1203.3518v1 | Title VarianceBased Rewards Approximate Bayesian Reinforcement Learning Summary exploreexploit dilemma one central challenge Reinforcement Learning RL Bayesian RL solves dilemma providing agent information form prior distribution environment however full Bayesian planning intractable Planning mean MDP common myopic app... | [0.01071979571133852, 0.028033098205924034, 0.010107972659170628, -0.07604625821113586, -0.023447997868061066, -0.016904447227716446, -0.06917952001094818, -0.00045450724428519607, -0.0422193706035614, 0.046049926429986954, -0.008258876390755177, 0.06446950882673264, -0.039577651768922806, 0.03924889117479324, -0.02098... |
2,038 | 2,038 | ['Jin Tian', 'Ru He', 'Lavanya Ram'] | 1203.3520v1 | We study the problem of learning Bayesian network structures from data. We
develop an algorithm for finding the k-best Bayesian network structures. We
propose to compute the posterior probabilities of hypotheses of interest by
Bayesian model averaging over the k-best Bayesian networks. We present
empirical results on s... | Bayesian Model Averaging Using the k-best Bayesian Network Structures | 2,012 | http://arxiv.org/pdf/1203.3520v1 | Title Bayesian Model Averaging Using kbest Bayesian Network Structures Summary study problem learning Bayesian network structure data develop algorithm finding kbest Bayesian network structure propose compute posterior probability hypothesis interest Bayesian model averaging kbest Bayesian network present empirical res... | [-0.052136149257421494, -0.000834397622384131, -0.0029849200509488583, -0.040096938610076904, -0.018577590584754944, -0.008039669133722782, 0.02240077592432499, -0.016614142805337906, 0.012867109850049019, 0.03170822188258171, 0.04131025820970535, -0.01624329574406147, 0.03699670732021332, 0.09165886044502258, 0.012026... |
2,039 | 2,039 | ['Giorgos Borboudakis', 'Ioannis Tsamardinos'] | 1209.6561v2 | A significant theoretical advantage of search-and-score methods for learning
Bayesian Networks is that they can accept informative prior beliefs for each
possible network, thus complementing the data. In this paper, a method is
presented for assigning priors based on beliefs on the presence or absence of
certain paths ... | Scoring and Searching over Bayesian Networks with Causal and Associative
Priors | 2,012 | http://arxiv.org/pdf/1209.6561v2 | Title Scoring Searching Bayesian Networks Causal Associative Priors Summary significant theoretical advantage searchandscore method learning Bayesian Networks accept informative prior belief possible network thus complementing data paper method presented assigning prior based belief presence absence certain path true n... | [-0.028305981308221817, 0.07308406382799149, -0.009107922203838825, 0.011773981153964996, -0.03658344969153404, -0.014694617129862309, 0.001623761490918696, -0.0004610821488313377, 0.013032759539783001, 0.022091764956712723, 0.07266254723072052, 0.04407047852873802, -0.011009159497916698, 0.06972576677799225, 0.0135916... |
2,040 | 2,040 | ['Tom Schaul', 'Yann LeCun'] | 1301.3764v2 | Recent work has established an empirically successful framework for adapting
learning rates for stochastic gradient descent (SGD). This effectively removes
all needs for tuning, while automatically reducing learning rates over time on
stationary problems, and permitting learning rates to grow appropriately in
non-stati... | Adaptive learning rates and parallelization for stochastic, sparse,
non-smooth gradients | 2,013 | http://arxiv.org/pdf/1301.3764v2 | Title Adaptive learning rate parallelization stochastic sparse nonsmooth gradient Summary Recent work established empirically successful framework adapting learning rate stochastic gradient descent SGD effectively remove need tuning automatically reducing learning rate time stationary problem permitting learning rate g... | [-0.00770012941211462, 0.03009985201060772, 0.016913704574108124, 0.01749691367149353, 0.02146337740123272, 0.0042340815998613834, 0.023733124136924744, -0.03104708343744278, -0.0475950725376606, -0.009931407868862152, -0.029077570885419846, -0.010648654773831367, 0.01663900353014469, -0.03186197206377983, 0.0044883890... |
2,041 | 2,041 | ['Scott Davies', 'Andrew Moore'] | 1301.3852v1 | Recently developed techniques have made it possible to quickly learn accurate
probability density functions from data in low-dimensional continuous space. In
particular, mixtures of Gaussians can be fitted to data very quickly using an
accelerated EM algorithm that employs multiresolution kd-trees (Moore, 1999).
In thi... | Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed
Continuous And Discrete Variables | 2,013 | http://arxiv.org/pdf/1301.3852v1 | Title Mixnets Factored Mixtures Gaussians Bayesian Networks Mixed Continuous Discrete Variables Summary Recently developed technique made possible quickly learn accurate probability density function data lowdimensional continuous space particular mixture Gaussians fitted data quickly using accelerated EM algorithm empl... | [-0.05988125130534172, 0.03586592897772789, -0.027995092794299126, 0.0011349852429702878, 0.0015404297737404704, -0.004157483112066984, 0.05991163104772568, -0.009784708730876446, -0.01533148530870676, 0.05427972227334976, 0.013676410540938377, -0.0034554642625153065, 0.020802130922675133, 0.06798982620239258, 0.024268... |
2,042 | 2,042 | ['Nir Friedman', 'Iftach Nachman', "Dana Pe'er"] | 1301.6696v1 | Learning Bayesian networks is often cast as an optimization problem, where
the computational task is to find a structure that maximizes a statistically
motivated score. By and large, existing learning tools address this
optimization problem using standard heuristic search techniques. Since the
search space is extremely... | Learning Bayesian Network Structure from Massive Datasets: The "Sparse
Candidate" Algorithm | 2,013 | http://arxiv.org/pdf/1301.6696v1 | Title Learning Bayesian Network Structure Massive Datasets Sparse Candidate Algorithm Summary Learning Bayesian network often cast optimization problem computational task find structure maximizes statistically motivated score large existing learning tool address optimization problem using standard heuristic search tech... | [-0.040849220007658005, 0.04517680034041405, -0.011775216087698936, 0.001119519816711545, -0.014401460997760296, -0.02173343487083912, 0.010028047487139702, 0.03629545867443085, -0.006224121432751417, 0.003549830289557576, 0.03286667913198471, 0.01224564015865326, 0.010467269457876682, 0.05504394695162773, -0.008310147... |
2,043 | 2,043 | ['Kevin Murphy'] | 1301.6724v1 | We show how to use a variational approximation to the logistic function to
perform approximate inference in Bayesian networks containing discrete nodes
with continuous parents. Essentially, we convert the logistic function to a
Gaussian, which facilitates exact inference, and then iteratively adjust the
variational par... | A Variational Approximation for Bayesian Networks with Discrete and
Continuous Latent Variables | 2,013 | http://arxiv.org/pdf/1301.6724v1 | Title Variational Approximation Bayesian Networks Discrete Continuous Latent Variables Summary show use variational approximation logistic function perform approximate inference Bayesian network containing discrete node continuous parent Essentially convert logistic function Gaussian facilitates exact inference iterati... | [-0.02868802472949028, 0.07039254158735275, -0.02848905324935913, -0.03319445997476578, -0.02847660332918167, -0.03733184561133385, 0.03213633596897125, -0.02282426506280899, -0.06048077344894409, 0.05302250012755394, 0.04888530820608139, 0.022066308185458183, 0.011386001482605934, 0.06789471209049225, 0.02160042896866... |
2,044 | 2,044 | ['Trong Nghia Hoang', 'Kian Hsiang Low'] | 1304.2024v3 | Recent advances in Bayesian reinforcement learning (BRL) have shown that
Bayes-optimality is theoretically achievable by modeling the environment's
latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior. In
self-interested multi-agent environments, the transition dynamics are mainly
controlled by the other agent'... | A General Framework for Interacting Bayes-Optimally with Self-Interested
Agents using Arbitrary Parametric Model and Model Prior | 2,013 | http://arxiv.org/pdf/1304.2024v3 | Title General Framework Interacting BayesOptimally SelfInterested Agents using Arbitrary Parametric Model Model Prior Summary Recent advance Bayesian reinforcement learning BRL shown Bayesoptimality theoretically achievable modeling environment latent dynamic using FlatDirichletMultinomial FDM prior selfinterested mult... | [-0.008783993311226368, -0.013359913602471352, 0.01246991939842701, -0.06280293315649033, -0.00895239133387804, 0.01078389398753643, 0.005427168216556311, -0.03246433660387993, -0.03030364029109478, -0.02517791837453842, -0.04702572897076607, 0.03297744691371918, -0.05547180771827698, 0.022714046761393547, 0.0010580907... |
2,045 | 2,045 | ['Teppo Niinimaki', 'Pekka Parviainen'] | 1210.4888v1 | Learning a Bayesian network structure from data is an NP-hard problem and
thus exact algorithms are feasible only for small data sets. Therefore, network
structures for larger networks are usually learned with various heuristics.
Another approach to scaling up the structure learning is local learning. In
local learning... | Local Structure Discovery in Bayesian Networks | 2,012 | http://arxiv.org/pdf/1210.4888v1 | Title Local Structure Discovery Bayesian Networks Summary Learning Bayesian network structure data NPhard problem thus exact algorithm feasible small data set Therefore network structure larger network usually learned various heuristic Another approach scaling structure learning local learning local learning modeler on... | [-0.03021683730185032, 0.01832403801381588, -0.021297162398695946, 0.014605916105210781, -0.03379257768392563, -0.04351023957133293, 0.019156308844685555, 0.0197920985519886, 0.05640349164605141, 0.03279845044016838, 0.057172149419784546, 0.0645824670791626, 0.008599729277193546, 0.05661757290363312, 0.0187212191522121... |
2,046 | 2,046 | ['Khaled S. Refaat', 'Arthur Choi', 'Adnan Darwiche'] | 1210.4910v1 | EDML is a recently proposed algorithm for learning MAP parameters in Bayesian
networks. In this paper, we present a number of new advances and insights on
the EDML algorithm. First, we provide the multivalued extension of EDML,
originally proposed for Bayesian networks over binary variables. Next, we
identify a simplif... | New Advances and Theoretical Insights into EDML | 2,012 | http://arxiv.org/pdf/1210.4910v1 | Title New Advances Theoretical Insights EDML Summary EDML recently proposed algorithm learning MAP parameter Bayesian network paper present number new advance insight EDML algorithm First provide multivalued extension EDML originally proposed Bayesian network binary variable Next identify simplified characterization ED... | [-0.05227961763739586, -0.01664128713309765, -0.0218343548476696, -0.04614487662911415, -0.006675169337540865, 0.0207839272916317, -0.012842217460274696, 0.012745200656354427, 0.0030856046359986067, -0.008536397479474545, 0.04923897981643677, 0.027395859360694885, 0.03968125954270363, 0.02879505604505539, 0.01776001043... |
2,047 | 2,047 | ['Thomas J. Walsh', 'Sergiu Goschin'] | 1210.4918v1 | We describe theoretical bounds and a practical algorithm for teaching a model
by demonstration in a sequential decision making environment. Unlike previous
efforts that have optimized learners that watch a teacher demonstrate a static
policy, we focus on the teacher as a decision maker who can dynamically choose
differ... | Dynamic Teaching in Sequential Decision Making Environments | 2,012 | http://arxiv.org/pdf/1210.4918v1 | Title Dynamic Teaching Sequential Decision Making Environments Summary describe theoretical bound practical algorithm teaching model demonstration sequential decision making environment Unlike previous effort optimized learner watch teacher demonstrate static policy focus teacher decision maker dynamically choose diffe... | [0.013284357264637947, 0.005704076960682869, -0.03142569214105606, -0.06324663013219833, -0.021392105147242546, -0.03174487501382828, 0.025764698162674904, -0.018547723069787025, -0.04425729811191559, -0.037642017006874084, 0.049486808478832245, 0.025801973417401314, 0.004723363555967808, 0.06774502247571945, -0.019277... |
2,048 | 2,048 | ['Chris Häusler', 'Alex Susemihl'] | 1210.8353v1 | Much work has been done refining and characterizing the receptive fields
learned by deep learning algorithms. A lot of this work has focused on the
development of Gabor-like filters learned when enforcing sparsity constraints
on a natural image dataset. Little work however has investigated how these
filters might expan... | Temporal Autoencoding Restricted Boltzmann Machine | 2,012 | http://arxiv.org/pdf/1210.8353v1 | Title Temporal Autoencoding Restricted Boltzmann Machine Summary Much work done refining characterizing receptive field learned deep learning algorithm lot work focused development Gaborlike filter learned enforcing sparsity constraint natural image dataset Little work however investigated filter might expand temporal ... | [0.005382750649005175, -0.0022551564034074545, -6.478667637566105e-05, 0.03449881821870804, -0.009466838091611862, -0.01197083294391632, 0.031158292666077614, 0.010948246344923973, -0.059459783136844635, 0.011517064645886421, 0.00302535155788064, -0.024759940803050995, 0.01255862694233656, 0.1229717880487442, 0.0056922... |
2,049 | 2,049 | ['Greg Ver Steeg', 'Aram Galstyan'] | 1406.1222v2 | We introduce a method to learn a hierarchy of successively more abstract
representations of complex data based on optimizing an information-theoretic
objective. Intuitively, the optimization searches for a set of latent factors
that best explain the correlations in the data as measured by multivariate
mutual informatio... | Discovering Structure in High-Dimensional Data Through Correlation
Explanation | 2,014 | http://arxiv.org/pdf/1406.1222v2 | Title Discovering Structure HighDimensional Data Correlation Explanation Summary introduce method learn hierarchy successively abstract representation complex data based optimizing informationtheoretic objective Intuitively optimization search set latent factor best explain correlation data measured multivariate mutual... | [-0.004075735807418823, 0.06990640610456467, -0.057022932916879654, 0.02308002859354019, -0.004711890127509832, 0.03467831388115883, -0.0017232026439160109, 0.01790984347462654, 0.019846005365252495, 0.032329827547073364, -0.01767141744494438, 0.01088652666658163, 0.041693441569805145, 0.0285179503262043, -0.0083861863... |
2,050 | 2,050 | ['Fajwel Fogel', "Alexandre d'Aspremont", 'Milan Vojnovic'] | 1406.5370v4 | We describe a seriation algorithm for ranking a set of items given pairwise
comparisons between these items. Intuitively, the algorithm assigns similar
rankings to items that compare similarly with all others. It does so by
constructing a similarity matrix from pairwise comparisons, using seriation
methods to reorder t... | Spectral Ranking using Seriation | 2,014 | http://arxiv.org/pdf/1406.5370v4 | Title Spectral Ranking using Seriation Summary describe seriation algorithm ranking set item given pairwise comparison item Intuitively algorithm assigns similar ranking item compare similarly others constructing similarity matrix pairwise comparison using seriation method reorder matrix construct ranking first show sp... | [-0.019422218203544617, 0.004632118623703718, -0.017455806955695152, 0.028462979942560196, -0.008136026561260223, -0.015131715685129166, -0.0028846978675574064, -0.02642645128071308, 9.103777119889855e-05, -0.06501538306474686, -0.04397757351398468, 0.07360626757144928, 0.030245468020439148, 0.01800582744181156, -0.000... |
2,051 | 2,051 | ['Shiliang Sun', 'John Shawe-Taylor', 'Liang Mao'] | 1406.5614v2 | This paper presents eight PAC-Bayes bounds to analyze the generalization
performance of multi-view classifiers. These bounds adopt data dependent
Gaussian priors which emphasize classifiers with high view agreements. The
center of the prior for the first two bounds is the origin, while the center of
the prior for the t... | PAC-Bayes Analysis of Multi-view Learning | 2,014 | http://arxiv.org/pdf/1406.5614v2 | Title PACBayes Analysis Multiview Learning Summary paper present eight PACBayes bound analyze generalization performance multiview classifier bound adopt data dependent Gaussian prior emphasize classifier high view agreement center prior first two bound origin center prior third fourth bound given data dependent vector... | [-0.055217888206243515, 0.030909419059753418, 0.014141872525215149, 0.026803700253367424, 0.012504120357334614, 0.019316652789711952, 0.056129101663827896, 0.00850064866244793, 0.005768669303506613, -0.018533874303102493, -0.04060405492782593, -0.04427818953990936, 0.02400130219757557, 0.02094162628054619, 0.0215490665... |
2,052 | 2,052 | ['Yuan Shi', 'Yung-Kyun Noh', 'Fei Sha', 'Daniel D. Lee'] | 1109.3940v1 | Metrics specifying distances between data points can be learned in a
discriminative manner or from generative models. In this paper, we show how to
unify generative and discriminative learning of metrics via a kernel learning
framework. Specifically, we learn local metrics optimized from parametric
generative models. T... | Learning Discriminative Metrics via Generative Models and Kernel
Learning | 2,011 | http://arxiv.org/pdf/1109.3940v1 | Title Learning Discriminative Metrics via Generative Models Kernel Learning Summary Metrics specifying distance data point learned discriminative manner generative model paper show unify generative discriminative learning metric via kernel learning framework Specifically learn local metric optimized parametric generati... | [-0.01765531674027443, 0.0027345207054167986, -0.023071138188242912, 0.011719121597707272, -0.007242303807288408, 0.0064403279684484005, 0.024011559784412384, 0.0433473065495491, 0.0208155307918787, 0.014718227088451385, 0.020846717059612274, 0.02354065701365471, -0.006835277657955885, -0.02000403217971325, 0.017873737... |
2,053 | 2,053 | ['Ilya Shpitser', 'Robin J. Evans', 'Thomas S. Richardson', 'James M. Robins'] | 1309.6863v1 | Hidden variables are ubiquitous in practical data analysis, and therefore
modeling marginal densities and doing inference with the resulting models is an
important problem in statistics, machine learning, and causal inference.
Recently, a new type of graphical model, called the nested Markov model, was
developed which ... | Sparse Nested Markov models with Log-linear Parameters | 2,013 | http://arxiv.org/pdf/1309.6863v1 | Title Sparse Nested Markov model Loglinear Parameters Summary Hidden variable ubiquitous practical data analysis therefore modeling marginal density inference resulting model important problem statistic machine learning causal inference Recently new type graphical model called nested Markov model developed capture equa... | [-0.007434367667883635, 0.03834656625986099, -0.006951178889721632, -0.04084799438714981, -0.028980109840631485, -0.016734736040234566, 0.046654053032398224, -0.000982991186901927, -0.024022603407502174, -0.06023741140961647, 0.07375533133745193, 0.05632682517170906, 0.019972240552306175, 0.04027143865823746, 0.0024796... |
2,054 | 2,054 | ['Charmgil Hong', 'Milos Hauskrecht'] | 1505.04097v1 | Outlier detection aims to identify unusual data instances that deviate from
expected patterns. The outlier detection is particularly challenging when
outliers are context dependent and when they are defined by unusual
combinations of multiple outcome variable values. In this paper, we develop and
study a new conditiona... | MCODE: Multivariate Conditional Outlier Detection | 2,015 | http://arxiv.org/pdf/1505.04097v1 | Title MCODE Multivariate Conditional Outlier Detection Summary Outlier detection aim identify unusual data instance deviate expected pattern outlier detection particularly challenging outlier context dependent defined unusual combination multiple outcome variable value paper develop study new conditional outlier detect... | [-0.003778250655159354, -0.012936602346599102, -0.01818542368710041, 0.0225718654692173, -0.03248757869005203, 0.026987699791789055, 0.0458931066095829, 0.029318390414118767, -0.03590898588299751, 0.016924424096941948, 0.060610100626945496, 0.05337497219443321, 0.006394877098500729, 0.07202494889497757, -0.025095148012... |
2,055 | 2,055 | ['Stephen H. Bach', 'Matthias Broecheler', 'Bert Huang', 'Lise Getoor'] | 1505.04406v3 | A fundamental challenge in developing high-impact machine learning
technologies is balancing the need to model rich, structured domains with the
ability to scale to big data. Many important problem areas are both richly
structured and large scale, from social and biological networks, to knowledge
graphs and the Web, to... | Hinge-Loss Markov Random Fields and Probabilistic Soft Logic | 2,015 | http://arxiv.org/pdf/1505.04406v3 | Title HingeLoss Markov Random Fields Probabilistic Soft Logic Summary fundamental challenge developing highimpact machine learning technology balancing need model rich structured domain ability scale big data Many important problem area richly structured large scale social biological network knowledge graph Web image v... | [-0.002434662077575922, 0.042974330484867096, 0.006506812758743763, -0.014104757457971573, -0.022258738055825233, -0.008792676962912083, -0.0073624905198812485, 0.0037354619707912207, 0.019013341516256332, -0.07326754182577133, 0.04684040695428848, 0.02311481535434723, 0.008768096566200256, 0.07445517927408218, -0.0009... |
2,056 | 2,056 | ['Stefano Teso', 'Andrea Passerini', 'Paolo Viappiani'] | 1604.06020v1 | In this paper we propose an approach to preference elicitation that is
suitable to large configuration spaces beyond the reach of existing
state-of-the-art approaches. Our setwise max-margin method can be viewed as a
generalization of max-margin learning to sets, and can produce a set of
"diverse" items that can be use... | Constructive Preference Elicitation by Setwise Max-margin Learning | 2,016 | http://arxiv.org/pdf/1604.06020v1 | Title Constructive Preference Elicitation Setwise Maxmargin Learning Summary paper propose approach preference elicitation suitable large configuration space beyond reach existing stateoftheart approach setwise maxmargin method viewed generalization maxmargin learning set produce set diverse item used ask informative q... | [-0.01963682845234871, 0.016260959208011627, -0.012856283225119114, -0.03277156874537468, -0.016206320375204086, -0.022943973541259766, 0.02647341974079609, -0.0019643844570964575, -0.023047098889946938, -0.024210579693317413, 0.04536476731300354, 0.02625657431781292, 0.005023554433137178, 0.07025488466024399, -0.00131... |
2,057 | 2,057 | ['Nils Y. Hammerla', 'Shane Halloran', 'Thomas Ploetz'] | 1604.08880v1 | Human activity recognition (HAR) in ubiquitous computing is beginning to
adopt deep learning to substitute for well-established analysis techniques that
rely on hand-crafted feature extraction and classification techniques. From
these isolated applications of custom deep architectures it is, however,
difficult to gain ... | Deep, Convolutional, and Recurrent Models for Human Activity Recognition
using Wearables | 2,016 | http://arxiv.org/pdf/1604.08880v1 | Title Deep Convolutional Recurrent Models Human Activity Recognition using Wearables Summary Human activity recognition HAR ubiquitous computing beginning adopt deep learning substitute wellestablished analysis technique rely handcrafted feature extraction classification technique isolated application custom deep archi... | [-0.020750150084495544, -0.018264414742588997, -0.00896103959530592, 0.06028631329536438, 0.0240479726344347, 0.011981155723333359, 0.022620057687163353, -0.010791003704071045, -0.009540437720716, -0.038857512176036835, 0.008673683740198612, 0.03051610104739666, 0.006606362760066986, 0.01852465234696865, 0.027634572237... |
2,058 | 2,058 | ['Hoifung Poon', 'Pedro Domingos'] | 1202.3732v1 | The key limiting factor in graphical model inference and learning is the
complexity of the partition function. We thus ask the question: what are
general conditions under which the partition function is tractable? The answer
leads to a new kind of deep architecture, which we call sum-product networks
(SPNs). SPNs are d... | Sum-Product Networks: A New Deep Architecture | 2,012 | http://arxiv.org/pdf/1202.3732v1 | Title SumProduct Networks New Deep Architecture Summary key limiting factor graphical model inference learning complexity partition function thus ask question general condition partition function tractable answer lead new kind deep architecture call sumproduct network SPNs SPNs directed acyclic graph variable leaf sum ... | [-0.025210419669747353, 0.045158471912145615, -0.013366068713366985, 0.030322378501296043, -0.012486553750932217, -0.03760001063346863, 0.005644751712679863, -0.05246814340353012, -0.002126407576724887, 0.04376307502388954, -0.016252299770712852, 0.011440097354352474, 0.03995799645781517, 0.10906574130058289, 0.0525409... |
2,059 | 2,059 | ['Jonathan Huang', 'Ashish Kapoor', 'Carlos E. Guestrin'] | 1202.3734v1 | Distributions over rankings are used to model data in various settings such
as preference analysis and political elections. The factorial size of the space
of rankings, however, typically forces one to make structural assumptions, such
as smoothness, sparsity, or probabilistic independence about these underlying
distri... | Efficient Probabilistic Inference with Partial Ranking Queries | 2,012 | http://arxiv.org/pdf/1202.3734v1 | Title Efficient Probabilistic Inference Partial Ranking Queries Summary Distributions ranking used model data various setting preference analysis political election factorial size space ranking however typically force one make structural assumption smoothness sparsity probabilistic independence underlying distribution ... | [0.03145304694771767, 0.07926204055547714, -0.011685600504279137, -0.005350549705326557, -0.0060411738231778145, -0.018904583528637886, -0.009879871271550655, -0.023667223751544952, 0.03306824341416359, -0.02620006538927555, -0.010179316624999046, -0.0037043076008558273, -0.008397730998694897, 0.03748467564582825, -0.0... |
2,060 | 2,060 | ['Alex Kulesza', 'Ben Taskar'] | 1202.3738v1 | Determinantal point processes (DPPs), which arise in random matrix theory and
quantum physics, are natural models for subset selection problems where
diversity is preferred. Among many remarkable properties, DPPs offer tractable
algorithms for exact inference, including computing marginal probabilities and
sampling; ho... | Learning Determinantal Point Processes | 2,012 | http://arxiv.org/pdf/1202.3738v1 | Title Learning Determinantal Point Processes Summary Determinantal point process DPPs arise random matrix theory quantum physic natural model subset selection problem diversity preferred Among many remarkable property DPPs offer tractable algorithm exact inference including computing marginal probability sampling howev... | [-0.022856617346405983, 0.007346586789935827, -0.013498600572347641, 0.006184756755828857, -0.015354282222688198, 0.013441181741654873, 0.017368970438838005, 0.011634251102805138, -0.021155428141355515, -0.0047661615535616875, 0.03468133509159088, -0.0019081940408796072, 0.024385901167988777, 0.032777294516563416, 0.02... |
2,061 | 2,061 | ['Kareem Amin', 'Michael Kearns', 'Umar Syed'] | 1202.3782v1 | We introduce a rich class of graphical models for multi-armed bandit problems
that permit both the state or context space and the action space to be very
large, yet succinctly specify the payoffs for any context-action pair. Our main
result is an algorithm for such models whose regret is bounded by the number of
parame... | Graphical Models for Bandit Problems | 2,012 | http://arxiv.org/pdf/1202.3782v1 | Title Graphical Models Bandit Problems Summary introduce rich class graphical model multiarmed bandit problem permit state context space action space large yet succinctly specify payoff contextaction pair main result algorithm model whose regret bounded number parameter whose running time depends treewidth graph substr... | [-0.011902510188519955, 0.03232288360595703, -0.022705627605319023, -0.009746667928993702, -0.03318743407726288, -0.02093575708568096, -0.013751477003097534, 0.024013187736272812, 0.014462350867688656, -0.016177771613001823, 0.023976769298315048, 0.03734128177165985, -0.07730644941329956, 0.045788541436195374, 0.021170... |
2,062 | 2,062 | ['Patrik O. Hoyer', 'Aapo Hyvarinen', 'Richard Scheines', 'Peter L. Spirtes', 'Joseph Ramsey', 'Gustavo Lacerda', 'Shohei Shimizu'] | 1206.3260v1 | An important task in data analysis is the discovery of causal relationships
between observed variables. For continuous-valued data, linear acyclic causal
models are commonly used to model the data-generating process, and the
inference of such models is a well-studied problem. However, existing methods
have significant ... | Causal discovery of linear acyclic models with arbitrary distributions | 2,012 | http://arxiv.org/pdf/1206.3260v1 | Title Causal discovery linear acyclic model arbitrary distribution Summary important task data analysis discovery causal relationship observed variable continuousvalued data linear acyclic causal model commonly used model datagenerating process inference model wellstudied problem However existing method significant lim... | [-0.04214766249060631, 0.04806342348456383, -0.03522440418601036, -0.006200891919434071, -0.02446010150015354, -0.02124224416911602, 0.03205832839012146, 0.01020841859281063, 0.023899681866168976, -0.004309259355068207, 0.07627962529659271, 0.0442030243575573, 0.00038116256473585963, 0.029877610504627228, -0.0149927064... |
2,063 | 2,063 | ['Mauricio Araya', 'Olivier Buffet', 'Vincent Thomas'] | 1206.4613v1 | Model-based Bayesian Reinforcement Learning (BRL) allows a found
formalization of the problem of acting optimally while facing an unknown
environment, i.e., avoiding the exploration-exploitation dilemma. However,
algorithms explicitly addressing BRL suffer from such a combinatorial explosion
that a large body of work r... | Near-Optimal BRL using Optimistic Local Transitions | 2,012 | http://arxiv.org/pdf/1206.4613v1 | Title NearOptimal BRL using Optimistic Local Transitions Summary Modelbased Bayesian Reinforcement Learning BRL allows found formalization problem acting optimally facing unknown environment ie avoiding explorationexploitation dilemma However algorithm explicitly addressing BRL suffer combinatorial explosion large body... | [-0.002042601350694895, -0.0235260222107172, 0.013260355219244957, -0.06716302782297134, -0.03190411999821663, -0.039793070405721664, -0.04904940351843834, 0.015630632638931274, -0.0361904501914978, 0.005787061993032694, 0.029591428115963936, 0.033531304448843, -0.057629164308309555, 0.06296005845069885, -0.00664622895... |
2,064 | 2,064 | ['Sergey Levine', 'Vladlen Koltun'] | 1206.4617v1 | Inverse optimal control, also known as inverse reinforcement learning, is the
problem of recovering an unknown reward function in a Markov decision process
from expert demonstrations of the optimal policy. We introduce a probabilistic
inverse optimal control algorithm that scales gracefully with task
dimensionality, an... | Continuous Inverse Optimal Control with Locally Optimal Examples | 2,012 | http://arxiv.org/pdf/1206.4617v1 | Title Continuous Inverse Optimal Control Locally Optimal Examples Summary Inverse optimal control also known inverse reinforcement learning problem recovering unknown reward function Markov decision process expert demonstration optimal policy introduce probabilistic inverse optimal control algorithm scale gracefully ta... | [-0.004409049171954393, 0.010758919641375542, -0.000761316332500428, -0.010187276639044285, -0.03114534728229046, -0.04473568871617317, -0.05191546306014061, 0.05069023743271828, 0.001859494368545711, 0.05494406446814537, -0.009014302864670753, 0.030578630045056343, -0.0327632762491703, 0.061896760016679764, 0.05456208... |
2,065 | 2,065 | ['Kiri Wagstaff'] | 1206.4656v1 | Much of current machine learning (ML) research has lost its connection to
problems of import to the larger world of science and society. From this
perspective, there exist glaring limitations in the data sets we investigate,
the metrics we employ for evaluation, and the degree to which results are
communicated back to ... | Machine Learning that Matters | 2,012 | http://arxiv.org/pdf/1206.4656v1 | Title Machine Learning Matters Summary Much current machine learning ML research lost connection problem import larger world science society perspective exist glaring limitation data set investigate metric employ evaluation degree result communicated back originating domain change needed conduct research increase impac... | [0.026455124840140343, 0.042471930384635925, -0.04690465331077576, -0.009174716658890247, -0.016737869009375572, 0.008635343052446842, 0.037491075694561005, 0.004778948146849871, -0.002938994439318776, -0.03280914947390556, 0.05348948761820793, 0.06884244829416275, 0.0028328460175544024, 0.05786873400211334, 0.03556949... |
2,066 | 2,066 | ['Jose M. Pena'] | 1206.5263v1 | We present a graphical criterion for reading dependencies from the minimal
directed independence map G of a graphoid p when G is a polytree and p
satisfies composition and weak transitivity. We prove that the criterion is
sound and complete. We argue that assuming composition and weak transitivity is
not too restrictiv... | Reading Dependencies from Polytree-Like Bayesian Networks | 2,012 | http://arxiv.org/pdf/1206.5263v1 | Title Reading Dependencies PolytreeLike Bayesian Networks Summary present graphical criterion reading dependency minimal directed independence map G graphoid p G polytree p satisfies composition weak transitivity prove criterion sound complete argue assuming composition weak transitivity restrictive Authors 0 Ahmed Osm... | [-0.018989702686667442, 0.02930963784456253, -0.028671083971858025, -0.021597659215331078, -0.043100304901599884, -0.037443604320287704, -0.021865012124180794, -0.00480780191719532, 0.009940741583704948, -0.0007444592192769051, 0.057859405875205994, 0.03750655800104141, -0.010069064795970917, 0.08158840984106064, -0.04... |
2,067 | 2,067 | ['Marina Meila', 'Kapil Phadnis', 'Arthur Patterson', 'Jeff A. Bilmes'] | 1206.5265v1 | We analyze the generalized Mallows model, a popular exponential model over
rankings. Estimating the central (or consensus) ranking from data is NP-hard.
We obtain the following new results: (1) We show that search methods can
estimate both the central ranking pi0 and the model parameters theta exactly.
The search is n!... | Consensus ranking under the exponential model | 2,012 | http://arxiv.org/pdf/1206.5265v1 | Title Consensus ranking exponential model Summary analyze generalized Mallows model popular exponential model ranking Estimating central consensus ranking data NPhard obtain following new result 1 show search method estimate central ranking pi0 model parameter theta exactly search n worst case tractable true distributi... | [-0.012800532393157482, -0.007486764807254076, 0.0025979948695749044, -0.03712635487318039, 0.005888727493584156, -0.010143857449293137, -0.019638866186141968, -0.03992431238293648, -0.05689305439591408, -0.050429776310920715, 0.010551545768976212, -0.004515605978667736, 0.011392109096050262, 0.02039354480803013, -0.01... |
2,068 | 2,068 | ['Charles Sutton', 'Andrew McCallum'] | 1206.5291v1 | Belief propagation and its variants are popular methods for approximate
inference, but their running time and even their convergence depend greatly on
the schedule used to send the messages. Recently, dynamic update schedules have
been shown to converge much faster on hard networks than static schedules,
namely the res... | Improved Dynamic Schedules for Belief Propagation | 2,012 | http://arxiv.org/pdf/1206.5291v1 | Title Improved Dynamic Schedules Belief Propagation Summary Belief propagation variant popular method approximate inference running time even convergence depend greatly schedule used send message Recently dynamic update schedule shown converge much faster hard network static schedule namely residual BP schedule Elidan ... | [-0.04018958657979965, 0.049813032150268555, -0.006502141710370779, 0.0052742669358849525, -0.06654002517461777, -0.04669348523020744, -0.007517792750149965, -0.03089580498635769, -0.034723296761512756, -0.02761940099298954, 0.08158739656209946, 0.006321096792817116, -0.01582762598991394, 0.06419410556554794, -0.038462... |
2,069 | 2,069 | ['Yoram Bachrach', 'Thore Graepel', 'Tom Minka', 'John Guiver'] | 1206.6386v1 | We propose a new probabilistic graphical model that jointly models the
difficulties of questions, the abilities of participants and the correct
answers to questions in aptitude testing and crowdsourcing settings. We devise
an active learning/adaptive testing scheme based on a greedy minimization of
expected model entro... | How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical
Model for Adaptive Crowdsourcing and Aptitude Testing | 2,012 | http://arxiv.org/pdf/1206.6386v1 | Title Grade Test Without Knowing Answers Bayesian Graphical Model Adaptive Crowdsourcing Aptitude Testing Summary propose new probabilistic graphical model jointly model difficulty question ability participant correct answer question aptitude testing crowdsourcing setting devise active learningadaptive testing scheme b... | [0.03895771503448486, 0.027394037693738937, -0.04047328606247902, 0.020326748490333557, -0.01933411881327629, -0.010498049668967724, 0.003794716438278556, 0.022761430591344833, 0.013361758552491665, -0.0040906453505158424, 0.05624779313802719, 0.017590133473277092, 0.0004087270062882453, 0.017273826524615288, 0.0102360... |
2,070 | 2,070 | ['Jesse Davis', 'Vitor Santos Costa', 'Peggy Peissig', 'Michael Caldwell', 'Elizabeth Berg', 'David Page'] | 1206.6399v1 | Learning from electronic medical records (EMR) is challenging due to their
relational nature and the uncertain dependence between a patient's past and
future health status. Statistical relational learning is a natural fit for
analyzing EMRs but is less adept at handling their inherent latent structure,
such as connecti... | Demand-Driven Clustering in Relational Domains for Predicting Adverse
Drug Events | 2,012 | http://arxiv.org/pdf/1206.6399v1 | Title DemandDriven Clustering Relational Domains Predicting Adverse Drug Events Summary Learning electronic medical record EMR challenging due relational nature uncertain dependence patient past future health status Statistical relational learning natural fit analyzing EMRs le adept handling inherent latent structure c... | [0.015047644264996052, 0.0331401564180851, -0.020464535802602768, -0.026753058657050133, 0.020620664581656456, 0.01917680911719799, -0.003713468788191676, 0.02898753061890602, 0.06859450042247772, -0.011191675439476967, 0.024885453283786774, -0.01737702079117298, -0.02034551277756691, 0.04123033583164215, -0.0031242575... |
2,071 | 2,071 | ['Roy Fox', 'Naftali Tishby'] | 1206.6405v1 | In Passive POMDPs actions do not affect the world state, but still incur
costs. When the agent is bounded by information-processing constraints, it can
only keep an approximation of the belief. We present a variational principle
for the problem of maintaining the information which is most useful for
minimizing the cost... | Bounded Planning in Passive POMDPs | 2,012 | http://arxiv.org/pdf/1206.6405v1 | Title Bounded Planning Passive POMDPs Summary Passive POMDPs action affect world state still incur cost agent bounded informationprocessing constraint keep approximation belief present variational principle problem maintaining information useful minimizing cost introduce efficient simple algorithm finding optimum Autho... | [0.0005232300027273595, 0.07107508182525635, -0.0029118326492607594, -0.03031974285840988, -0.03622957691550255, -0.05987828969955444, -0.03612849488854408, 0.009806076996028423, -0.02121003530919552, -0.018403448164463043, 0.0689646452665329, 0.05900009348988533, -0.020579461008310318, 0.077720507979393, -0.0126318708... |
2,072 | 2,072 | ['Takaki Makino', 'Johane Takeuchi'] | 1206.6484v1 | We consider apprenticeship learning, i.e., having an agent learn a task by
observing an expert demonstrating the task in a partially observable
environment when the model of the environment is uncertain. This setting is
useful in applications where the explicit modeling of the environment is
difficult, such as a dialog... | Apprenticeship Learning for Model Parameters of Partially Observable
Environments | 2,012 | http://arxiv.org/pdf/1206.6484v1 | Title Apprenticeship Learning Model Parameters Partially Observable Environments Summary consider apprenticeship learning ie agent learn task observing expert demonstrating task partially observable environment model environment uncertain setting useful application explicit modeling environment difficult dialogue syste... | [0.017660528421401978, 0.03783974424004555, 0.025048546493053436, -0.021631993353366852, -0.02032013237476349, -0.0010384846245869994, 0.01563578099012375, 0.023083779960870743, 0.01029928121715784, -0.02693767286837101, 0.022142786532640457, 0.039510831236839294, -0.047637321054935455, 0.04460849612951279, 0.011416876... |
2,073 | 2,073 | ['Avi Pfeffer'] | 1206.6846v1 | One approach to monitoring a dynamic system relies on decomposition of the
system into weakly interacting subsystems. An earlier paper introduced a notion
of weak interaction called separability, and showed that it leads to exact
propagation of marginals for prediction. This paper addresses two questions
left open by t... | Approximate Separability for Weak Interaction in Dynamic Systems | 2,012 | http://arxiv.org/pdf/1206.6846v1 | Title Approximate Separability Weak Interaction Dynamic Systems Summary One approach monitoring dynamic system relies decomposition system weakly interacting subsystem earlier paper introduced notion weak interaction called separability showed lead exact propagation marginals prediction paper address two question left ... | [-0.04331379011273384, 0.031162461265921593, -0.013738010078668594, 0.03769551217556, -0.0006120861507952213, -0.01829351671040058, 0.012994579039514065, 0.016228143125772476, 0.006418230477720499, 0.03202848881483078, 0.009688075631856918, -0.015597258694469929, -0.024493789300322533, 0.0728551521897316, -0.0210857298... |
2,074 | 2,074 | ['Vikash Mansinghka', 'Charles Kemp', 'Thomas Griffiths', 'Joshua Tenenbaum'] | 1206.6852v1 | Traditional approaches to Bayes net structure learning typically assume
little regularity in graph structure other than sparseness. However, in many
cases, we expect more systematicity: variables in real-world systems often
group into classes that predict the kinds of probabilistic dependencies they
participate in. Her... | Structured Priors for Structure Learning | 2,012 | http://arxiv.org/pdf/1206.6852v1 | Title Structured Priors Structure Learning Summary Traditional approach Bayes net structure learning typically assume little regularity graph structure sparseness However many case expect systematicity variable realworld system often group class predict kind probabilistic dependency participate capture form prior knowl... | [-0.021279804408550262, 0.0687485784292221, 0.001162343891337514, 0.008381414227187634, 0.00936175137758255, 0.001467838534153998, -0.006710390094667673, 0.003440106986090541, -0.04763364419341087, -2.526411844883114e-05, 0.015947775915265083, -0.008826366625726223, 0.01039489172399044, 0.06735170632600784, 0.047891695... |
2,075 | 2,075 | ['Sameer Singh', 'Thore Graepel'] | 1212.0967v1 | Instead of requiring a domain expert to specify the probabilistic
dependencies of the data, in this work we present an approach that uses the
relational DB schema to automatically construct a Bayesian graphical model for
a database. This resulting model contains customized distributions for columns,
latent variables th... | Compiling Relational Database Schemata into Probabilistic Graphical
Models | 2,012 | http://arxiv.org/pdf/1212.0967v1 | Title Compiling Relational Database Schemata Probabilistic Graphical Models Summary Instead requiring domain expert specify probabilistic dependency data work present approach us relational DB schema automatically construct Bayesian graphical model database resulting model contains customized distribution column latent... | [0.014432472176849842, 0.04546206071972847, -0.029537903144955635, -0.024930886924266815, -0.02538476511836052, -0.010074903257191181, 0.005165162496268749, 0.003639871720224619, 0.008448323234915733, -0.01457611471414566, 0.05056304112076759, 0.0011653057299554348, 0.010844264179468155, 0.1135866791009903, 0.009908001... |
2,076 | 2,076 | ['J. E. Smith', 'P. Caleb-Solly', 'M. A. Tahir', 'D. Sannen', 'H. van-Brussel'] | 1212.1100v1 | The accuracy of machine learning systems is a widely studied research topic.
Established techniques such as cross-validation predict the accuracy on unseen
data of the classifier produced by applying a given learning method to a given
training data set. However, they do not predict whether incurring the cost of
obtaini... | Making Early Predictions of the Accuracy of Machine Learning
Applications | 2,012 | http://arxiv.org/pdf/1212.1100v1 | Title Making Early Predictions Accuracy Machine Learning Applications Summary accuracy machine learning system widely studied research topic Established technique crossvalidation predict accuracy unseen data classifier produced applying given learning method given training data set However predict whether incurring cos... | [0.005124268587678671, 0.0031944497022777796, -0.03592777997255325, -0.029026586562395096, 0.004230910912156105, -0.01283400971442461, 0.0392209030687809, 0.0011758188484236598, 0.004601708613336086, -0.0029514299239963293, 0.022366909310221672, 0.01232985407114029, 0.0341201089322567, 0.055009160190820694, -0.00224649... |
2,077 | 2,077 | ['David Maxwell Chickering', 'David Heckerman'] | 1302.3567v2 | We discuss Bayesian methods for learning Bayesian networks when data sets are
incomplete. In particular, we examine asymptotic approximations for the
marginal likelihood of incomplete data given a Bayesian network. We consider
the Laplace approximation and the less accurate but more efficient BIC/MDL
approximation. We ... | Efficient Approximations for the Marginal Likelihood of Incomplete Data
Given a Bayesian Network | 2,013 | http://arxiv.org/pdf/1302.3567v2 | Title Efficient Approximations Marginal Likelihood Incomplete Data Given Bayesian Network Summary discus Bayesian method learning Bayesian network data set incomplete particular examine asymptotic approximation marginal likelihood incomplete data given Bayesian network consider Laplace approximation le accurate efficie... | [-0.05530932545661926, -0.008555022068321705, -0.012237011454999447, 0.014796613715589046, -0.02330068126320839, -0.04140667989850044, 0.01419099047780037, -0.02040012553334236, -0.017738278955221176, 0.038557425141334534, 0.0631520226597786, -0.005930772051215172, 0.023651497438549995, 0.011331220157444477, -0.0231454... |
2,078 | 2,078 | ['Dan Geiger', 'David Heckerman', 'Christopher Meek'] | 1302.3580v2 | We extend the Bayesian Information Criterion (BIC), an asymptotic
approximation for the marginal likelihood, to Bayesian networks with hidden
variables. This approximation can be used to select models given large samples
of data. The standard BIC as well as our extension punishes the complexity of a
model according to ... | Asymptotic Model Selection for Directed Networks with Hidden Variables | 2,013 | http://arxiv.org/pdf/1302.3580v2 | Title Asymptotic Model Selection Directed Networks Hidden Variables Summary extend Bayesian Information Criterion BIC asymptotic approximation marginal likelihood Bayesian network hidden variable approximation used select model given large sample data standard BIC well extension punishes complexity model according dime... | [-0.05903637781739235, 0.02550484612584114, -0.01943427324295044, -0.011078529991209507, 0.0019408465595915914, 0.0022101118229329586, 0.05782820284366608, 0.01247506681829691, 0.01597348228096962, 0.021665949374437332, 0.04662551358342171, 0.05153018236160278, 0.008386794477701187, 0.0787314921617508, 0.01366748195141... |
2,079 | 2,079 | ['Hado van Hasselt'] | 1302.7175v2 | We investigate the accuracy of the two most common estimators for the maximum
expected value of a general set of random variables: a generalization of the
maximum sample average, and cross validation. No unbiased estimator exists and
we show that it is non-trivial to select a good estimator without knowledge
about the ... | Estimating the Maximum Expected Value: An Analysis of (Nested) Cross
Validation and the Maximum Sample Average | 2,013 | http://arxiv.org/pdf/1302.7175v2 | Title Estimating Maximum Expected Value Analysis Nested Cross Validation Maximum Sample Average Summary investigate accuracy two common estimator maximum expected value general set random variable generalization maximum sample average cross validation unbiased estimator exists show nontrivial select good estimator with... | [-0.037611596286296844, -0.03296448290348053, -0.0270608589053154, -0.008480421267449856, 0.017933005467057228, 6.520982424262911e-05, 0.04055718705058098, -0.011817376129329205, 0.03460663557052612, 0.015888717025518417, 0.014674766920506954, 0.04608674719929695, 0.04094269499182701, 0.047166649252176285, 0.0286987815... |
2,080 | 2,080 | ['Somayeh Danafar', 'Paola M. V. Rancoita', 'Tobias Glasmachers', 'Kevin Whittingstall', 'Juergen Schmidhuber'] | 1305.0423v1 | Do two data samples come from different distributions? Recent studies of this
fundamental problem focused on embedding probability distributions into
sufficiently rich characteristic Reproducing Kernel Hilbert Spaces (RKHSs), to
compare distributions by the distance between their embeddings. We show that
Regularized Ma... | Testing Hypotheses by Regularized Maximum Mean Discrepancy | 2,013 | http://arxiv.org/pdf/1305.0423v1 | Title Testing Hypotheses Regularized Maximum Mean Discrepancy Summary two data sample come different distribution Recent study fundamental problem focused embedding probability distribution sufficiently rich characteristic Reproducing Kernel Hilbert Spaces RKHSs compare distribution distance embeddings show Regularized... | [-0.03356059268116951, -0.015980107709765434, -0.03890058025717735, 0.0005031739128753543, -0.01752518303692341, 0.006541328504681587, -0.01033998467028141, 0.03200575336813927, 0.02324211224913597, 0.05152469500899315, -0.0009081657626666129, -0.015500600449740887, 0.062250811606645584, 0.011886447668075562, 0.0583701... |
2,081 | 2,081 | ['Aaron Karper'] | 1402.2300v1 | The amount of information in the form of features and variables avail- able
to machine learning algorithms is ever increasing. This can lead to classifiers
that are prone to overfitting in high dimensions, high di- mensional models do
not lend themselves to interpretable results, and the CPU and memory resources
necess... | Feature and Variable Selection in Classification | 2,014 | http://arxiv.org/pdf/1402.2300v1 | Title Feature Variable Selection Classification Summary amount information form feature variable avail able machine learning algorithm ever increasing lead classifier prone overfitting high dimension high di mensional model lend interpretable result CPU memory resource necessary run highdimensional datasets severly lim... | [-0.01503723580390215, 0.03310861065983772, -0.05072430521249771, 0.01964566856622696, 0.004890934564173222, 0.00293405307456851, 0.05864860862493515, 0.028686795383691788, 0.005625756457448006, 0.005444481503218412, 0.06475509703159332, 0.04742700979113579, 0.00784587673842907, 0.05869772285223007, -0.0156638063490390... |
2,082 | 2,082 | ['Lihong Li', 'Shunbao Chen', 'Jim Kleban', 'Ankur Gupta'] | 1403.1891v2 | Optimizing an interactive system against a predefined online metric is
particularly challenging, when the metric is computed from user feedback such
as clicks and payments. The key challenge is the counterfactual nature: in the
case of Web search, any change to a component of the search engine may result
in a different... | Counterfactual Estimation and Optimization of Click Metrics for Search
Engines | 2,014 | http://arxiv.org/pdf/1403.1891v2 | Title Counterfactual Estimation Optimization Click Metrics Search Engines Summary Optimizing interactive system predefined online metric particularly challenging metric computed user feedback click payment key challenge counterfactual nature case Web search change component search engine may result different search res... | [0.026970619335770607, 0.057925522327423096, -0.038612689822912216, -0.011467079631984234, -0.02481050044298172, -0.03715641051530838, 0.03110860101878643, 0.03522871434688568, 0.006067683920264244, -0.06256449967622757, -0.03252210095524788, -0.0003582848294172436, -0.012323129922151566, 0.09273400902748108, 0.0200419... |
2,083 | 2,083 | ['Zhana Bao'] | 1405.1027v1 | Finding rare information hidden in a huge amount of data from the Internet is
a necessary but complex issue. Many researchers have studied this issue and
have found effective methods to detect anomaly data in low dimensional space.
However, as the dimension increases, most of these existing methods perform
poorly in de... | K-NS: Section-Based Outlier Detection in High Dimensional Space | 2,014 | http://arxiv.org/pdf/1405.1027v1 | Title KNS SectionBased Outlier Detection High Dimensional Space Summary Finding rare information hidden huge amount data Internet necessary complex issue Many researcher studied issue found effective method detect anomaly data low dimensional space However dimension increase existing method perform poorly detecting out... | [0.009748214855790138, -0.018245546147227287, -0.03769798204302788, 0.041060637682676315, -0.018476534634828568, 0.01318865641951561, 0.028490252792835236, 0.06297449767589569, -0.024636516347527504, 0.041691116988658905, 0.0538514107465744, 0.04608277231454849, -0.007786982227116823, 0.056568119674921036, -0.031496793... |
2,084 | 2,084 | ['Ji Zhao', 'Deyu Meng'] | 1405.2664v2 | The maximum mean discrepancy (MMD) is a recently proposed test statistic for
two-sample test. Its quadratic time complexity, however, greatly hampers its
availability to large-scale applications. To accelerate the MMD calculation, in
this study we propose an efficient method called FastMMD. The core idea of
FastMMD is ... | FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test | 2,014 | http://arxiv.org/pdf/1405.2664v2 | Title FastMMD Ensemble Circular Discrepancy Efficient TwoSample Test Summary maximum mean discrepancy MMD recently proposed test statistic twosample test quadratic time complexity however greatly hamper availability largescale application accelerate MMD calculation study propose efficient method called FastMMD core ide... | [-0.05340200290083885, -0.004506088327616453, -0.05934504047036171, 0.05318877100944519, -0.008252672851085663, -0.032670196145772934, 0.06480226665735245, -0.02573218196630478, -0.024504218250513077, 0.027538031339645386, 0.014620550908148289, -0.018866494297981262, 0.05498983711004257, -0.029056239873170853, -0.00863... |
2,085 | 2,085 | ['Hongwei Li', 'Qiang Liu'] | 1502.00725v1 | Crowdsourcing provides a popular paradigm for data collection at scale. We
study the problem of selecting subsets of workers from a given worker pool to
maximize the accuracy under a budget constraint. One natural question is
whether we should hire as many workers as the budget allows, or restrict on a
small number of ... | Cheaper and Better: Selecting Good Workers for Crowdsourcing | 2,015 | http://arxiv.org/pdf/1502.00725v1 | Title Cheaper Better Selecting Good Workers Crowdsourcing Summary Crowdsourcing provides popular paradigm data collection scale study problem selecting subset worker given worker pool maximize accuracy budget constraint One natural question whether hire many worker budget allows restrict small number topquality worker ... | [0.016991762444376945, 0.06856611371040344, -0.03477822244167328, -0.03048105351626873, -0.029157651588320732, -0.059212084859609604, 0.0466863252222538, 0.04394686967134476, -0.015075954608619213, -0.04038483649492264, 0.04378972575068474, 0.04919582977890968, -0.003046088619157672, -0.0033984200563281775, 0.022043546... |
2,086 | 2,086 | ['Shuai Li', 'Alexandros Karatzoglou', 'Claudio Gentile'] | 1502.03473v7 | Classical collaborative filtering, and content-based filtering methods try to
learn a static recommendation model given training data. These approaches are
far from ideal in highly dynamic recommendation domains such as news
recommendation and computational advertisement, where the set of items and
users is very fluid.... | Collaborative Filtering Bandits | 2,015 | http://arxiv.org/pdf/1502.03473v7 | Title Collaborative Filtering Bandits Summary Classical collaborative filtering contentbased filtering method try learn static recommendation model given training data approach far ideal highly dynamic recommendation domain news recommendation computational advertisement set item user fluid work investigate adaptive cl... | [0.043313391506671906, 0.004283931106328964, -0.01708991266787052, -0.019534491002559662, 0.0005997842526994646, -0.011954969726502895, 0.042941562831401825, 0.021548865363001823, 0.01073745172470808, 0.006185795646160841, -0.053907983005046844, 0.00370569690130651, -0.02590211294591427, 0.03241090476512909, -0.0068773... |
2,087 | 2,087 | ['Jacob Abernethy', 'Yiling Chen', 'Chien-Ju Ho', 'Bo Waggoner'] | 1502.05774v2 | We design mechanisms for online procurement of data held by strategic agents
for machine learning tasks. The challenge is to use past data to actively price
future data and give learning guarantees even when an agent's cost for
revealing her data may depend arbitrarily on the data itself. We achieve this
goal by showin... | Low-Cost Learning via Active Data Procurement | 2,015 | http://arxiv.org/pdf/1502.05774v2 | Title LowCost Learning via Active Data Procurement Summary design mechanism online procurement data held strategic agent machine learning task challenge use past data actively price future data give learning guarantee even agent cost revealing data may depend arbitrarily data achieve goal showing convert large class no... | [-0.023585449904203415, 0.08519540727138519, -0.017772499471902847, -0.05586612597107887, -0.006209905259311199, -0.03100406937301159, 0.06546367704868317, 0.021294724196195602, -0.022671116515994072, -0.01102172490209341, 0.015225880779325962, 0.04702921584248543, -0.017882371321320534, 0.13014262914657593, -0.0144842... |
2,088 | 2,088 | ['Md Amran Siddiqui', 'Alan Fern', 'Thomas G. Dietterich', 'Weng-Keen Wong'] | 1503.00038v1 | In many applications, an anomaly detection system presents the most anomalous
data instance to a human analyst, who then must determine whether the instance
is truly of interest (e.g. a threat in a security setting). Unfortunately, most
anomaly detectors provide no explanation about why an instance was considered
anoma... | Sequential Feature Explanations for Anomaly Detection | 2,015 | http://arxiv.org/pdf/1503.00038v1 | Title Sequential Feature Explanations Anomaly Detection Summary many application anomaly detection system present anomalous data instance human analyst must determine whether instance truly interest eg threat security setting Unfortunately anomaly detector provide explanation instance considered anomalous leaving analy... | [0.03856004774570465, -0.007319788448512554, -0.012067083269357681, 0.03410965949296951, -0.04685444384813309, -0.01119364146143198, 0.04849904403090477, 0.008157940581440926, -0.02130657434463501, -0.02270738035440445, 0.1022520586848259, -0.0008323031943291426, -0.011948407627642155, 0.12237460911273956, -0.021566396... |
2,089 | 2,089 | ['Andrew Emmott', 'Shubhomoy Das', 'Thomas Dietterich', 'Alan Fern', 'Weng-Keen Wong'] | 1503.01158v2 | This article provides a thorough meta-analysis of the anomaly detection
problem. To accomplish this we first identify approaches to benchmarking
anomaly detection algorithms across the literature and produce a large corpus
of anomaly detection benchmarks that vary in their construction across several
dimensions we deem... | A Meta-Analysis of the Anomaly Detection Problem | 2,015 | http://arxiv.org/pdf/1503.01158v2 | Title MetaAnalysis Anomaly Detection Problem Summary article provides thorough metaanalysis anomaly detection problem accomplish first identify approach benchmarking anomaly detection algorithm across literature produce large corpus anomaly detection benchmark vary construction across several dimension deem important r... | [0.06328872591257095, -0.020624376833438873, -0.032865215092897415, 0.01918272115290165, -0.03468652069568634, 0.00162624625954777, 0.04972092807292938, 0.01620400696992874, -0.04520917311310768, -0.02940533310174942, 0.048684753477573395, 0.0182701013982296, -0.003590296721085906, 0.05678805708885193, -0.0417051911354... |
2,090 | 2,090 | ['Qingming Tang', 'Siqi Sun', 'Jinbo Xu'] | 1503.02129v3 | Learning the network structure underlying data is an important problem in
machine learning. This paper introduces a novel prior to study the inference of
scale-free networks, which are widely used to model social and biological
networks. The prior not only favors a desirable global node degree
distribution, but also ta... | Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior | 2,015 | http://arxiv.org/pdf/1503.02129v3 | Title Learning ScaleFree Networks Dynamic NodeSpecific Degree Prior Summary Learning network structure underlying data important problem machine learning paper introduces novel prior study inference scalefree network widely used model social biological network prior favor desirable global node degree distribution also ... | [-0.017744367942214012, -0.00891782809048891, -0.03865165263414383, -0.018091997131705284, -0.009269659407436848, -0.017756953835487366, 0.03190317004919052, 0.006770459935069084, 0.05484851449728012, 0.024441951885819435, 0.01901293732225895, 0.04002838954329491, -0.00866029504686594, 0.10840190947055817, 0.0022172452... |
2,091 | 2,091 | ['Qinqing Zheng', 'Ryota Tomioka'] | 1503.05479v2 | We consider the problem of recovering a low-rank tensor from its noisy
observation. Previous work has shown a recovery guarantee with signal to noise
ratio $O(n^{\lceil K/2 \rceil /2})$ for recovering a $K$th order rank one
tensor of size $n\times \cdots \times n$ by recursive unfolding. In this paper,
we first improve... | Interpolating Convex and Non-Convex Tensor Decompositions via the
Subspace Norm | 2,015 | http://arxiv.org/pdf/1503.05479v2 | Title Interpolating Convex NonConvex Tensor Decompositions via Subspace Norm Summary consider problem recovering lowrank tensor noisy observation Previous work shown recovery guarantee signal noise ratio Onlceil K2 rceil 2 recovering Kth order rank one tensor size ntimes cdots time n recursive unfolding paper first imp... | [-0.025488197803497314, 0.07685485482215881, 0.005058417096734047, 0.03756030648946762, -0.0010564718395471573, 0.02929343655705452, 0.005151380784809589, 0.07240483164787292, -0.030262667685747147, 0.013362454250454903, 0.004248038865625858, -0.04480091854929924, -0.0028084793593734503, -0.02914496324956417, 0.0015379... |
2,092 | 2,092 | ['Jinye Zhang', 'Zhijian Ou'] | 1503.06239v1 | Existing MAP inference algorithms for determinantal point processes (DPPs)
need to calculate determinants or conduct eigenvalue decomposition generally at
the scale of the full kernel, which presents a great challenge for real-world
applications. In this paper, we introduce a class of DPPs, called BwDPPs, that
are char... | Block-Wise MAP Inference for Determinantal Point Processes with
Application to Change-Point Detection | 2,015 | http://arxiv.org/pdf/1503.06239v1 | Title BlockWise MAP Inference Determinantal Point Processes Application ChangePoint Detection Summary Existing MAP inference algorithm determinantal point process DPPs need calculate determinant conduct eigenvalue decomposition generally scale full kernel present great challenge realworld application paper introduce cl... | [-0.07240423560142517, 0.000522443326190114, -0.04835313931107521, -0.007268576882779598, -0.015433134511113167, -0.00039502704748883843, 0.03038032166659832, 0.024285001680254936, 0.04154610261321068, 0.02257649227976799, 0.07686968147754669, 0.05603582784533501, 0.029130972921848297, 0.01931862160563469, -0.006954280... |
2,093 | 2,093 | ['Taylor Killian', 'George Konidaris', 'Finale Doshi-Velez'] | 1612.00475v1 | Due to physiological variation, patients diagnosed with the same condition
may exhibit divergent, but related, responses to the same treatments. Hidden
Parameter Markov Decision Processes (HiP-MDPs) tackle this transfer-learning
problem by embedding these tasks into a low-dimensional space. However, the
original formul... | Transfer Learning Across Patient Variations with Hidden Parameter Markov
Decision Processes | 2,016 | http://arxiv.org/pdf/1612.00475v1 | Title Transfer Learning Across Patient Variations Hidden Parameter Markov Decision Processes Summary Due physiological variation patient diagnosed condition may exhibit divergent related response treatment Hidden Parameter Markov Decision Processes HiPMDPs tackle transferlearning problem embedding task lowdimensional s... | [0.014823011122643948, 0.03425592929124832, -0.01384636852890253, -0.05035480856895447, 0.02991417795419693, 0.01956239901483059, 0.019398363307118416, 0.0203845277428627, -0.012039663270115852, 0.013737629167735577, 0.006257784087210894, -0.011194316670298576, 0.013564607128500938, 0.033341530710458755, -0.00585546065... |
2,094 | 2,094 | ['James Kirkpatrick', 'Razvan Pascanu', 'Neil Rabinowitz', 'Joel Veness', 'Guillaume Desjardins', 'Andrei A. Rusu', 'Kieran Milan', 'John Quan', 'Tiago Ramalho', 'Agnieszka Grabska-Barwinska', 'Demis Hassabis', 'Claudia Clopath', 'Dharshan Kumaran', 'Raia Hadsell'] | 1612.00796v2 | The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this lim... | Overcoming catastrophic forgetting in neural networks | 2,016 | http://arxiv.org/pdf/1612.00796v2 | Title Overcoming catastrophic forgetting neural network Summary ability learn task sequential fashion crucial development artificial intelligence Neural network general capable widely thought catastrophic forgetting inevitable feature connectionist model show possible overcome limitation train network maintain expertis... | [-0.013627736829221249, 0.06784623861312866, -0.0245034322142601, 0.0006046528578735888, 0.02747356705367565, 0.017595097422599792, -0.0224929042160511, -0.018583137542009354, -0.01327467244118452, 0.030320804566144943, 0.013708880171179771, 0.010989083908498287, -0.023444807156920433, 0.02875937707722187, 0.0185731071... |
2,095 | 2,095 | ['Peter Karkus', 'Andras Kupcsik', 'David Hsu', 'Wee Sun Lee'] | 1612.01746v1 | Scarce data is a major challenge to scaling robot learning to truly complex
tasks, as we need to generalize locally learned policies over different
"contexts". Bayesian optimization approaches to contextual policy search (CPS)
offer data-efficient policy learning that generalize over a context space. We
propose to impr... | Factored Contextual Policy Search with Bayesian Optimization | 2,016 | http://arxiv.org/pdf/1612.01746v1 | Title Factored Contextual Policy Search Bayesian Optimization Summary Scarce data major challenge scaling robot learning truly complex task need generalize locally learned policy different context Bayesian optimization approach contextual policy search CPS offer dataefficient policy learning generalize context space pr... | [-0.021594161167740822, -0.0009851943468675017, 0.03450219705700874, -0.0039422414265573025, 0.005831927061080933, -0.015931300818920135, 0.013361268676817417, 0.0013885862426832318, -0.051722656935453415, -0.017539044842123985, -0.01869313232600689, 0.00790545903146267, 0.0035008389968425035, 0.03543585538864136, -0.0... |
2,096 | 2,096 | ['Ting Chen', 'Yizhou Sun'] | 1612.02814v2 | In this paper, we study the problem of author identification under
double-blind review setting, which is to identify potential authors given
information of an anonymized paper. Different from existing approaches that
rely heavily on feature engineering, we propose to use network embedding
approach to address the proble... | Task-Guided and Path-Augmented Heterogeneous Network Embedding for
Author Identification | 2,016 | http://arxiv.org/pdf/1612.02814v2 | Title TaskGuided PathAugmented Heterogeneous Network Embedding Author Identification Summary paper study problem author identification doubleblind review setting identify potential author given information anonymized paper Different existing approach rely heavily feature engineering propose use network embedding approa... | [0.006971737835556269, -0.022771546617150307, -0.023734131827950478, 0.02401071973145008, 0.017898106947541237, -0.018154677003622055, 0.08214766532182693, -0.03914443403482437, 0.040595944970846176, -0.021216291934251785, -0.01025584526360035, 0.08158876746892929, -0.030711006373167038, 0.04144924879074097, 0.03559555... |
2,097 | 2,097 | ['Vivek Veeriah', 'Shangtong Zhang', 'Richard S. Sutton'] | 1612.02879v2 | Representations are fundamental to artificial intelligence. The performance
of a learning system depends on the type of representation used for
representing the data. Typically, these representations are hand-engineered
using domain knowledge. More recently, the trend is to learn these
representations through stochasti... | Learning Representations by Stochastic Meta-Gradient Descent in Neural
Networks | 2,016 | http://arxiv.org/pdf/1612.02879v2 | Title Learning Representations Stochastic MetaGradient Descent Neural Networks Summary Representations fundamental artificial intelligence performance learning system depends type representation used representing data Typically representation handengineered using domain knowledge recently trend learn representation sto... | [-0.02671397663652897, -0.007372573018074036, -0.012821019627153873, -0.01619107834994793, 0.0076756603084504604, -0.030694853514432907, 0.033791832625865936, -0.009645365178585052, -0.03539589047431946, -0.008695905096828938, -0.024991288781166077, 0.042916152626276016, 0.008951188065111637, 0.06423050165176392, 0.010... |
2,098 | 2,098 | ['Hanie Sedghi', 'Ashish Sabharwal'] | 1612.03871v2 | Given a knowledge base (KB) rich in facts about common nouns or generics,
such as "all trees produce oxygen" or "some animals live in forests", we
consider the problem of deriving additional such facts at a high precision.
While this problem has received much attention for named entity KBs such as
Freebase, little emph... | Knowledge Completion for Generics using Guided Tensor Factorization | 2,016 | http://arxiv.org/pdf/1612.03871v2 | Title Knowledge Completion Generics using Guided Tensor Factorization Summary Given knowledge base KB rich fact common noun generic tree produce oxygen animal live forest consider problem deriving additional fact high precision problem received much attention named entity KBs Freebase little emphasis placed generic des... | [0.024226149544119835, 0.04627448320388794, -0.0032957098446786404, 0.016702936962246895, 0.0232491884380579, 0.0330202616751194, -0.018531620502471924, 0.046567704528570175, -0.03186938911676407, -0.06299469619989395, 0.015076507814228535, 0.022237233817577362, 0.015674546360969543, 0.028310386463999748, -0.0095683727... |
2,099 | 2,099 | ['Marco F. Cusumano-Towner', 'Vikash K. Mansinghka'] | 1612.04759v2 | This paper introduces the probabilistic module interface, which allows
encapsulation of complex probabilistic models with latent variables alongside
custom stochastic approximate inference machinery, and provides a
platform-agnostic abstraction barrier separating the model internals from the
host probabilistic inferenc... | Encapsulating models and approximate inference programs in probabilistic
modules | 2,016 | http://arxiv.org/pdf/1612.04759v2 | Title Encapsulating model approximate inference program probabilistic module Summary paper introduces probabilistic module interface allows encapsulation complex probabilistic model latent variable alongside custom stochastic approximate inference machinery provides platformagnostic abstraction barrier separating model... | [-0.026385894045233727, 0.06399696320295334, -0.0004334723053034395, -0.009615359827876091, -0.031692229211330414, -0.033143073320388794, 0.030327208340168, -0.06841228157281876, -0.0075864423997700214, 0.025383543223142624, 0.02685406617820263, 0.02124253660440445, -0.004541034810245037, 0.10739387571811676, 0.0463104... |
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