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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700 | 700 | ['A. J. Tallón-Ballesteros', 'P. A. Gutiérrez-Peña', 'C. Hervás-Martínez'] | 1205.3336v1 | This paper deals with the distributed processing in the search for an optimum
classification model using evolutionary product unit neural networks. For this
distributed search we used a cluster of computers. Our objective is to obtain a
more efficient design than those net architectures which do not use a
distributed p... | Distribution of the search of evolutionary product unit neural networks
for classification | 2,012 | http://arxiv.org/pdf/1205.3336v1 | Title Distribution search evolutionary product unit neural network classification Summary paper deal distributed processing search optimum classification model using evolutionary product unit neural network distributed search used cluster computer objective obtain efficient design net architecture use distributed proce... | [-0.017015954479575157, 0.04218994826078415, -0.0480910949409008, 0.035894256085157394, -0.011669846251606941, -0.019090872257947922, 0.07465261965990067, 0.0159330852329731, -0.03907737880945206, -0.002233824459835887, 0.02942030131816864, -0.011982315219938755, 0.005564785096794367, 0.051176197826862335, 0.0086611984... |
701 | 701 | ['Baochen Sun', 'Jiashi Feng', 'Kate Saenko'] | 1612.01939v1 | In this chapter, we present CORrelation ALignment (CORAL), a simple yet
effective method for unsupervised domain adaptation. CORAL minimizes domain
shift by aligning the second-order statistics of source and target
distributions, without requiring any target labels. In contrast to subspace
manifold methods, it aligns t... | Correlation Alignment for Unsupervised Domain Adaptation | 2,016 | http://arxiv.org/pdf/1612.01939v1 | Title Correlation Alignment Unsupervised Domain Adaptation Summary chapter present CORrelation ALignment CORAL simple yet effective method unsupervised domain adaptation CORAL minimizes domain shift aligning secondorder statistic source target distribution without requiring target label contrast subspace manifold metho... | [-0.003157225204631686, 0.055424828082323074, -0.01645541936159134, 0.009064463898539543, -0.008572671562433243, 0.014969167299568653, 0.03380617871880531, -0.011032774113118649, -0.016263090074062347, 0.012759760022163391, -0.0734294205904007, 0.04532918706536293, 0.027217239141464233, 0.01444198563694954, -0.04497967... |
702 | 702 | ['Gundram Leifert', 'Tobias Strauß', 'Tobias Grüning', 'Roger Labahn'] | 1605.08412v1 | We describe CITlab's recognition system for the HTRtS competition attached to
the 13. International Conference on Document Analysis and Recognition, ICDAR
2015. The task comprises the recognition of historical handwritten documents.
The core algorithms of our system are based on multi-dimensional recurrent
neural netwo... | CITlab ARGUS for historical handwritten documents | 2,016 | http://arxiv.org/pdf/1605.08412v1 | Title CITlab ARGUS historical handwritten document Summary describe CITlabs recognition system HTRtS competition attached 13 International Conference Document Analysis Recognition ICDAR 2015 task comprises recognition historical handwritten document core algorithm system based multidimensional recurrent neural network ... | [0.006485320162028074, 0.011991418898105621, 0.03605543076992035, 0.10382351279258728, -0.054830402135849, 0.0312416423112154, 0.047403573989868164, 0.05970511958003044, 0.031199734658002853, -0.02386263944208622, 0.013471421785652637, -0.02181967720389366, 0.04529687762260437, -0.0016658701933920383, -0.03147829324007... |
703 | 703 | ['Keyu Lu', 'Jian Li', 'Xiangjing An', 'Hangen He'] | 1610.09609v1 | Vision-based object detection is one of the fundamental functions in numerous
traffic scene applications such as self-driving vehicle systems and advance
driver assistance systems (ADAS). However, it is also a challenging task due to
the diversity of traffic scene and the storage, power and computing source
limitations... | Generalized Haar Filter based Deep Networks for Real-Time Object
Detection in Traffic Scene | 2,016 | http://arxiv.org/pdf/1610.09609v1 | Title Generalized Haar Filter based Deep Networks RealTime Object Detection Traffic Scene Summary Visionbased object detection one fundamental function numerous traffic scene application selfdriving vehicle system advance driver assistance system ADAS However also challenging task due diversity traffic scene storage po... | [-0.026212334632873535, 0.0003751225012820214, -0.013209350407123566, 0.05791609361767769, -0.042799949645996094, 0.0005910324980504811, 0.03361477330327034, -0.002968650311231613, -0.0026382768992334604, -0.003895937232300639, 0.03201752156019211, -0.004890859127044678, -0.023898780345916748, 0.08317878097295761, 0.01... |
704 | 704 | ['Weishan Dong', 'Ting Yuan', 'Kai Yang', 'Changsheng Li', 'Shilei Zhang'] | 1701.01272v1 | In this paper, we study learning generalized driving style representations
from automobile GPS trip data. We propose a novel Autoencoder Regularized deep
neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers'
driving styles directly from GPS records, by combining supervised and
unsupervised fea... | Autoencoder Regularized Network For Driving Style Representation
Learning | 2,017 | http://arxiv.org/pdf/1701.01272v1 | Title Autoencoder Regularized Network Driving Style Representation Learning Summary paper study learning generalized driving style representation automobile GPS trip data propose novel Autoencoder Regularized deep neural Network ARNet trip encoding framework trip2vec learn driver driving style directly GPS record combi... | [-0.03630707785487175, 0.038288988173007965, -0.03017522022128105, 0.04267675429582596, 0.026386911049485207, 0.012322071008384228, 0.02979334257543087, -0.004941744729876518, -0.04290768876671791, -0.02354791760444641, 0.02713371440768242, 0.01855294406414032, 0.0042855404317379, 0.04404690861701965, 0.058824878185987... |
705 | 705 | ['Prutha Date', 'Ashwinkumar Ganesan', 'Tim Oates'] | 1707.09899v1 | Convolutional Neural Networks have been highly successful in performing a
host of computer vision tasks such as object recognition, object detection,
image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how
the style of a painter can be extracted from an image of the painting and
applied to another ... | Fashioning with Networks: Neural Style Transfer to Design Clothes | 2,017 | http://arxiv.org/pdf/1707.09899v1 | Title Fashioning Networks Neural Style Transfer Design Clothes Summary Convolutional Neural Networks highly successful performing host computer vision task object recognition object detection image segmentation texture synthesis 2015 Gatys et al 7 show style painter extracted image painting applied another normal photo... | [-0.00823851116001606, 0.062297869473695755, 0.004127266816794872, 0.0646948516368866, 0.0053047193214297295, -0.0337434858083725, 0.020034287124872208, 0.00061299919616431, -0.0553683266043663, 0.0040498278103768826, -0.006196638569235802, 0.02744789980351925, 0.00698753260076046, 0.06729361414909363, 0.04759786650538... |
706 | 706 | ['Seungkyun Hong', 'Seongchan Kim', 'Minsu Joh', 'Sa-kwang Song'] | 1708.03417v1 | Advances in remote sensing technologies have made it possible to use
high-resolution visual data for weather observation and forecasting tasks. We
propose the use of multi-layer neural networks for understanding complex
atmospheric dynamics based on multichannel satellite images. The capability of
our model was evaluat... | GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from
Remote Sensing Imagery | 2,017 | http://arxiv.org/pdf/1708.03417v1 | Title GlobeNet Convolutional Neural Networks Typhoon Eye Tracking Remote Sensing Imagery Summary Advances remote sensing technology made possible use highresolution visual data weather observation forecasting task propose use multilayer neural network understanding complex atmospheric dynamic based multichannel satelli... | [0.011978700757026672, 0.03270982578396797, 0.0042501394636929035, 0.0399932935833931, 0.03708725795149803, 0.030548449605703354, 0.015155835077166557, -0.09495334327220917, -0.023419389501214027, 0.05626843869686127, -0.002228537807241082, -0.05608472228050232, 0.02461966685950756, 0.04351809248328209, 0.0705731287598... |
707 | 707 | ['Dat Thanh Tran', 'Alexandros Iosifidis', 'Moncef Gabbouj'] | 1709.09902v3 | The excellent performance of deep neural networks has enabled us to solve
several automatization problems, opening an era of autonomous devices. However,
current deep net architectures are heavy with millions of parameters and
require billions of floating point operations. Several works have been
developed to compress ... | Improving Efficiency in Convolutional Neural Network with Multilinear
Filters | 2,017 | http://arxiv.org/pdf/1709.09902v3 | Title Improving Efficiency Convolutional Neural Network Multilinear Filters Summary excellent performance deep neural network enabled u solve several automatization problem opening era autonomous device However current deep net architecture heavy million parameter require billion floating point operation Several work d... | [-0.0230800062417984, 0.06493087112903595, -0.007753004319965839, 0.052645500749349594, 0.00351553107611835, -0.03316081687808037, 0.06656789034605026, -0.014636198990046978, -0.04129568859934807, -0.00214259116910398, -0.017294492572546005, 0.03075144626200199, 4.964257823303342e-05, 0.03679436445236206, 0.03370646387... |
708 | 708 | ['Devinder Kumar', 'Graham W. Taylor', 'Alexander Wong'] | 1710.10675v1 | Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown
considerable promise in recent years as a potential tool for improving clinical
decision support in medical oncology, particularly those based around the
concept of Discovery Radiomics, where radiomic sequencers are discovered
through the analysis of ... | Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided
Diagnosis of Diabetic Retinopathy | 2,017 | http://arxiv.org/pdf/1710.10675v1 | Title Discovery Radiomics CLEARDR Interpretable Computer Aided Diagnosis Diabetic Retinopathy Summary Objective Radiomicsdriven Computer Aided Diagnosis CAD shown considerable promise recent year potential tool improving clinical decision support medical oncology particularly based around concept Discovery Radiomics ra... | [0.004513891413807869, -0.01456287782639265, -0.0058048442006111145, -0.010569184087216854, 0.026145469397306442, 0.04538270831108093, 0.014838271774351597, 0.07058724015951157, -0.007606544066220522, 0.05095557123422623, 0.0695001408457756, 0.0015230110147967935, 0.026726379990577698, 0.07343531399965286, -0.019839175... |
709 | 709 | ['Emad Barsoum', 'John Kender', 'Zicheng Liu'] | 1711.09561v1 | Predicting and understanding human motion dynamics has many applications,
such as motion synthesis, augmented reality, security, and autonomous vehicles.
Due to the recent success of generative adversarial networks (GAN), there has
been much interest in probabilistic estimation and synthetic data generation
using deep ... | HP-GAN: Probabilistic 3D human motion prediction via GAN | 2,017 | http://arxiv.org/pdf/1711.09561v1 | Title HPGAN Probabilistic 3D human motion prediction via GAN Summary Predicting understanding human motion dynamic many application motion synthesis augmented reality security autonomous vehicle Due recent success generative adversarial network GAN much interest probabilistic estimation synthetic data generation using ... | [-0.0058366525918245316, 0.06477829813957214, -0.04030698537826538, 0.006125688087195158, 0.0358552560210228, 0.0020677705761045218, -0.002153107663616538, 0.018649939447641373, -0.009829223155975342, 0.004496832378208637, -0.009524628520011902, -0.0560121014714241, 0.005378892179578543, 0.010715723037719727, 0.0897707... |
710 | 710 | ['Qiangeng Xu', 'John Kender'] | 1712.09709v2 | In the research of the impact of gestures using by a lecturer, one
challenging task is to infer the attention of a group of audiences. Two
important measurements that can help infer the level of attention are eye
movement data and Electroencephalography (EEG) data. Under the fundamental
assumption that a group of peopl... | Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro
Gesture | 2,017 | http://arxiv.org/pdf/1712.09709v2 | Title Report Dynamic Eye Movement Matching Visualization Tool Neuro Gesture Summary research impact gesture using lecturer one challenging task infer attention group audience Two important measurement help infer level attention eye movement data Electroencephalography EEG data fundamental assumption group people would ... | [0.02106880210340023, 0.0004780478775501251, -0.03516867756843567, 0.017498597502708435, 0.004785249475389719, 0.04110534489154816, 0.011310875415802002, 0.06284011155366898, -0.003938641864806414, -0.024829421192407608, -0.026670614257454872, 0.013832620345056057, 0.061912354081869125, 0.044799890369176865, 0.06801147... |
711 | 711 | ['Audrey G. Chung', 'Paul Fieguth', 'Alexander Wong'] | 1802.03318v1 | Evolutionary deep intelligence synthesizes highly efficient deep neural
networks architectures over successive generations. Inspired by the nature
versus nurture debate, we propose a study to examine the role of external
factors on the network synthesis process by varying the availability of
simulated environmental res... | Nature vs. Nurture: The Role of Environmental Resources in Evolutionary
Deep Intelligence | 2,018 | http://arxiv.org/pdf/1802.03318v1 | Title Nature v Nurture Role Environmental Resources Evolutionary Deep Intelligence Summary Evolutionary deep intelligence synthesizes highly efficient deep neural network architecture successive generation Inspired nature versus nurture debate propose study examine role external factor network synthesis process varying... | [-0.024060428142547607, 0.05777814984321594, -0.057195376604795456, 0.0167063120752573, -0.006461993791162968, -0.015112362802028656, 0.032274022698402405, -0.02153223194181919, -0.040376462042331696, 0.020567379891872406, -0.03807233273983002, 0.006133691407740116, -0.021327001973986626, 0.050032876431941986, 0.036894... |
712 | 712 | ['Akisato kimura', 'Derek Pang', 'Tatsuto Takeuchi', 'Kouji Miyazato', 'Junji Yamato', 'Kunio Kashino'] | 1004.0085v1 | Recent studies in the field of human vision science suggest that the human
responses to the stimuli on a visual display are non-deterministic. People may
attend to different locations on the same visual input at the same time. Based
on this knowledge, we propose a new stochastic model of visual attention by
introducing... | A stochastic model of human visual attention with a dynamic Bayesian
network | 2,010 | http://arxiv.org/pdf/1004.0085v1 | Title stochastic model human visual attention dynamic Bayesian network Summary Recent study field human vision science suggest human response stimulus visual display nondeterministic People may attend different location visual input time Based knowledge propose new stochastic model visual attention introducing dynamic ... | [-0.010042090900242329, -0.02305353246629238, -0.019794777035713196, -0.0253529641777277, 0.010284334421157837, -0.005860269069671631, 0.004688510671257973, 0.0102235684171319, -0.007809461560100317, -0.035747282207012177, 0.025163181126117706, -0.04932325333356857, 0.047812964767217636, 0.06632932275533676, 0.05536041... |
713 | 713 | ['Tee Connie', 'Mundher Al-Shabi', 'Michael Goh'] | 1612.09506v2 | With rapid development of the Internet, web contents become huge. Most of the
websites are publicly available, and anyone can access the contents from
anywhere such as workplace, home and even schools. Nevertheless, not all the
web contents are appropriate for all users, especially children. An example of
these content... | Smart Content Recognition from Images Using a Mixture of Convolutional
Neural Networks | 2,016 | http://arxiv.org/pdf/1612.09506v2 | Title Smart Content Recognition Images Using Mixture Convolutional Neural Networks Summary rapid development Internet web content become huge website publicly available anyone access content anywhere workplace home even school Nevertheless web content appropriate user especially child example content pornography image ... | [0.053268421441316605, 0.04292959347367287, -0.025858022272586823, 0.04832489788532257, -0.04007880762219429, 0.05105172470211983, 0.07456593215465546, 0.04035274311900139, 0.017997123301029205, -0.048988133668899536, -0.028000997379422188, -0.020716283470392227, -0.01718912459909916, 0.04956079646945, -0.0219766721129... |
714 | 714 | ['Giacomo Cocci', 'Davide Barbieri', 'Giovanna Citti', 'Alessandro Sarti'] | 1407.0733v2 | The visual systems of many mammals, including humans, is able to integrate
the geometric information of visual stimuli and to perform cognitive tasks
already at the first stages of the cortical processing. This is thought to be
the result of a combination of mechanisms, which include feature extraction at
single cell l... | Cortical spatio-temporal dimensionality reduction for visual grouping | 2,014 | http://arxiv.org/pdf/1407.0733v2 | Title Cortical spatiotemporal dimensionality reduction visual grouping Summary visual system many mammal including human able integrate geometric information visual stimulus perform cognitive task already first stage cortical processing thought result combination mechanism include feature extraction single cell level g... | [-0.029008204117417336, -0.04715174436569214, -0.0460982471704483, 0.024282919242978096, -0.018221618607640266, 0.03414541482925415, 0.05285128951072693, -0.008611221797764301, 0.0069997296668589115, 0.018568407744169235, -0.02713211253285408, -0.014310279861092567, 0.057770781219005585, 0.057154033333063126, 0.0658645... |
715 | 715 | ['Can Xu', 'Suleyman Cetintas', 'Kuang-Chih Lee', 'Li-Jia Li'] | 1411.5731v1 | Images have become one of the most popular types of media through which users
convey their emotions within online social networks. Although vast amount of
research is devoted to sentiment analysis of textual data, there has been very
limited work that focuses on analyzing sentiment of image data. In this work,
we propo... | Visual Sentiment Prediction with Deep Convolutional Neural Networks | 2,014 | http://arxiv.org/pdf/1411.5731v1 | Title Visual Sentiment Prediction Deep Convolutional Neural Networks Summary Images become one popular type medium user convey emotion within online social network Although vast amount research devoted sentiment analysis textual data limited work focus analyzing sentiment image data work propose novel visual sentiment ... | [0.026351680979132652, 0.06894616782665253, -0.00021954286785330623, 0.04812313988804817, -0.02541949413716793, 0.028199469670653343, -0.009609222412109375, 0.01943696103990078, 0.014297880232334137, -0.02676563896238804, -0.011985491029918194, 0.015088360756635666, -0.03671792522072792, 0.05788110941648483, 0.01268449... |
716 | 716 | ['Fei Zhu', 'Abderrahim Halimi', 'Paul Honeine', 'Badong Chen', 'Nanning Zheng'] | 1602.01729v1 | In hyperspectral images, some spectral bands suffer from low signal-to-noise
ratio due to noisy acquisition and atmospheric effects, thus requiring robust
techniques for the unmixing problem. This paper presents a robust supervised
spectral unmixing approach for hyperspectral images. The robustness is achieved
by writi... | Correntropy Maximization via ADMM - Application to Robust Hyperspectral
Unmixing | 2,016 | http://arxiv.org/pdf/1602.01729v1 | Title Correntropy Maximization via ADMM Application Robust Hyperspectral Unmixing Summary hyperspectral image spectral band suffer low signaltonoise ratio due noisy acquisition atmospheric effect thus requiring robust technique unmixing problem paper present robust supervised spectral unmixing approach hyperspectral im... | [-0.01128374319523573, 0.008524861186742783, 0.006219339091330767, 0.035685356706380844, 0.011910916306078434, 0.012478083372116089, -0.0077212220057845116, -0.007883419282734394, -0.004385383799672127, 0.031793300062417984, -0.019009126350283623, 0.05858924612402916, 0.016866693273186684, 0.026558484882116318, -0.0159... |
717 | 717 | ['Farhad Arbabzadah', 'Grégoire Montavon', 'Klaus-Robert Müller', 'Wojciech Samek'] | 1606.07285v2 | This paper focuses on the problem of explaining predictions of psychological
attributes such as attractiveness, happiness, confidence and intelligence from
face photographs using deep neural networks. Since psychological attribute
datasets typically suffer from small sample sizes, we apply transfer learning
with two ba... | Identifying individual facial expressions by deconstructing a neural
network | 2,016 | http://arxiv.org/pdf/1606.07285v2 | Title Identifying individual facial expression deconstructing neural network Summary paper focus problem explaining prediction psychological attribute attractiveness happiness confidence intelligence face photograph using deep neural network Since psychological attribute datasets typically suffer small sample size appl... | [0.01171519048511982, 0.10384302586317062, -0.03140038624405861, 0.006184595637023449, -0.0005059328977949917, 0.08105877041816711, 0.04882337898015976, -0.01604016311466694, 0.012622016482055187, 0.036673445254564285, -0.04585159569978714, -0.0045763589441776276, 0.02939782291650772, 0.027108021080493927, 0.0313397124... |
718 | 718 | ['Jing Yu Koh', 'Wojciech Samek', 'Klaus-Robert Müller', 'Alexander Binder'] | 1606.09187v3 | Semantic boundary and edge detection aims at simultaneously detecting object
edge pixels in images and assigning class labels to them. Systematic training
of predictors for this task requires the labeling of edges in images which is a
particularly tedious task. We propose a novel strategy for solving this task,
when pi... | Object Boundary Detection and Classification with Image-level Labels | 2,016 | http://arxiv.org/pdf/1606.09187v3 | Title Object Boundary Detection Classification Imagelevel Labels Summary Semantic boundary edge detection aim simultaneously detecting object edge pixel image assigning class label Systematic training predictor task requires labeling edge image particularly tedious task propose novel strategy solving task pixellevel an... | [0.014435085467994213, -0.009498577564954758, 0.016130737960338593, 0.0587863065302372, -0.027480414137244225, -0.04759363457560539, 0.057943981140851974, -0.020749565213918686, 0.0026499656960368156, -0.03737781569361687, -0.010966208763420582, 0.05082504823803902, -0.005800250452011824, 0.05133979022502899, -0.026573... |
719 | 719 | ['Sam Kriegman', 'Marcin Szubert', 'Josh C. Bongard', 'Christian Skalka'] | 1706.07888v2 | Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine lea... | Evolving Spatially Aggregated Features from Satellite Imagery for
Regional Modeling | 2,017 | http://arxiv.org/pdf/1706.07888v2 | Title Evolving Spatially Aggregated Features Satellite Imagery Regional Modeling Summary Satellite imagery remote sensing provide explanatory variable relatively high resolution modeling geospatial phenomenon yet regional summary often desirable analysis actionable insight paper propose novel method inducing spatial ag... | [-0.015096725896000862, 0.0686153993010521, -0.03705476596951485, -0.02326796017587185, -0.019166309386491776, 0.039359673857688904, 0.018243933096528053, -0.07028821110725403, 0.026447806507349014, 0.025008250027894974, 0.07410223037004471, 0.004483410157263279, 0.04304669424891472, 0.07307964563369751, 0.047447010874... |
720 | 720 | ['Biswa Sengupta', 'Yu Qian'] | 1708.06250v1 | In recent work, it was shown that combining multi-kernel based support vector
machines (SVMs) can lead to near state-of-the-art performance on an action
recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks
that used hand-crafted features in addition to the deep convolutional feature
extractors. I... | Pillar Networks++: Distributed non-parametric deep and wide networks | 2,017 | http://arxiv.org/pdf/1708.06250v1 | Title Pillar Networks Distributed nonparametric deep wide network Summary recent work shown combining multikernel based support vector machine SVMs lead near stateoftheart performance action recognition dataset HMDB51 dataset 04 lower framework used handcrafted feature addition deep convolutional feature extractor pres... | [-0.02186121791601181, 0.019998127594590187, -0.011713789775967598, 0.04303362965583801, 0.006795182358473539, 0.023230986669659615, 0.08459728956222534, -0.02022259682416916, -0.06779662519693375, 0.017613446339964867, -0.04614480212330818, -0.015191325917840004, 0.03995179757475853, 0.07688998430967331, 0.01177845895... |
721 | 721 | ['Ivo Kwee', 'Marcus Hutter', 'Juergen Schmidhuber'] | cs/0105025v1 | Unlike traditional reinforcement learning (RL), market-based RL is in
principle applicable to worlds described by partially observable Markov
Decision Processes (POMDPs), where an agent needs to learn short-term memories
of relevant previous events in order to execute optimal actions. Most previous
work, however, has f... | Market-Based Reinforcement Learning in Partially Observable Worlds | 2,001 | http://arxiv.org/pdf/cs/0105025v1 | Title MarketBased Reinforcement Learning Partially Observable Worlds Summary Unlike traditional reinforcement learning RL marketbased RL principle applicable world described partially observable Markov Decision Processes POMDPs agent need learn shortterm memory relevant previous event order execute optimal action previ... | [0.014959203079342842, 0.03265559300780296, 0.0059007154777646065, -0.07738395780324936, -0.013619828969240189, -0.009802755899727345, -0.031781792640686035, -0.009771967306733131, -0.024042461067438126, -0.006411755457520485, 0.009179960936307907, 0.054150912910699844, -0.0539899580180645, 0.08083557337522507, -0.0036... |
722 | 722 | ['Andras Lorincz'] | cs/0308025v1 | A model of sensory information processing is presented. The model assumes
that learning of internal (hidden) generative models, which can predict the
future and evaluate the precision of that prediction, is of central importance
for information extraction. Furthermore, the model makes a bridge to
goal-oriented systems ... | Controlled hierarchical filtering: Model of neocortical sensory
processing | 2,003 | http://arxiv.org/pdf/cs/0308025v1 | Title Controlled hierarchical filtering Model neocortical sensory processing Summary model sensory information processing presented model assumes learning internal hidden generative model predict future evaluate precision prediction central importance information extraction Furthermore model make bridge goaloriented sy... | [0.0038238430861383677, -0.016976026818156242, -0.030160972848534584, -0.007917379960417747, 0.0030014747753739357, 0.012925976887345314, -0.014209343120455742, 0.021481959149241447, 0.046543776988983154, -0.013778923079371452, -0.05295243486762047, 0.015117663890123367, -0.01441823597997427, 0.06966102868318558, 0.047... |
723 | 723 | ['David J. Finton'] | cs/0404032v1 | For an intelligent agent to be truly autonomous, it must be able to adapt its
representation to the requirements of its task as it interacts with the world.
Most current approaches to on-line feature extraction are ad hoc; in contrast,
this paper presents an algorithm that bases judgments of state compatibility
and sta... | When Do Differences Matter? On-Line Feature Extraction Through Cognitive
Economy | 2,004 | http://arxiv.org/pdf/cs/0404032v1 | Title Differences Matter OnLine Feature Extraction Cognitive Economy Summary intelligent agent truly autonomous must able adapt representation requirement task interacts world current approach online feature extraction ad hoc contrast paper present algorithm base judgment state compatibility statespace abstraction prin... | [-0.0025872213300317526, 0.04461510851979256, -0.04202467203140259, -0.0055188341066241264, -0.031070062890648842, 0.015614354982972145, 0.06376229971647263, 0.0013927760301157832, 0.05910900607705116, -0.03941641002893448, 0.0278855599462986, 0.018414955586194992, 0.0176840890198946, 0.10981688648462296, 0.00545070739... |
724 | 724 | ['I. Szita', 'A. Lorincz'] | cs/0410004v1 | Recurrent neural networks are often used for learning time-series data. Based
on a few assumptions we model this learning task as a minimization problem of a
nonlinear least-squares cost function. The special structure of the cost
function allows us to build a connection to reinforcement learning. We exploit
this conne... | Applying Policy Iteration for Training Recurrent Neural Networks | 2,004 | http://arxiv.org/pdf/cs/0410004v1 | Title Applying Policy Iteration Training Recurrent Neural Networks Summary Recurrent neural network often used learning timeseries data Based assumption model learning task minimization problem nonlinear leastsquares cost function special structure cost function allows u build connection reinforcement learning exploit ... | [0.0023810474667698145, 0.009202794171869755, 0.004990331828594208, 0.008443190716207027, -0.00026223910390399396, -0.02158651500940323, -0.021275408565998077, -0.02535850554704666, -0.020347122102975845, 0.012646910734474659, 0.006972047965973616, 0.015723707154393196, 0.014062496833503246, 0.05185173824429512, -0.001... |
725 | 725 | ['Vitaly Schetinin', 'Joachim Schult'] | cs/0504069v1 | A new technique is presented developed to learn multi-class concepts from
clinical electroencephalograms. A desired concept is represented as a neuronal
computational model consisting of the input, hidden, and output neurons. In
this model the hidden neurons learn independently to classify the
electroencephalogram segm... | A Neural-Network Technique to Learn Concepts from Electroencephalograms | 2,005 | http://arxiv.org/pdf/cs/0504069v1 | Title NeuralNetwork Technique Learn Concepts Electroencephalograms Summary new technique presented developed learn multiclass concept clinical electroencephalogram desired concept represented neuronal computational model consisting input hidden output neuron model hidden neuron learn independently classify electroencep... | [-0.026561444625258446, 0.02696835808455944, -0.013183407485485077, -0.007017889525741339, -0.026515575125813484, 0.021268894895911217, 0.06517685949802399, 0.012261785566806793, -0.013266931287944317, -0.014424309134483337, 0.003456740640103817, -0.02011762000620365, 0.04236244410276413, 0.02565435692667961, 0.0446575... |
726 | 726 | ['Ridwan Al Iqbal'] | 1005.5556v2 | Standard hybrid learners that use domain knowledge require stronger knowledge
that is hard and expensive to acquire. However, weaker domain knowledge can
benefit from prior knowledge while being cost effective. Weak knowledge in the
form of feature relative importance (FRI) is presented and explained. Feature
relative ... | Empirical learning aided by weak domain knowledge in the form of feature
importance | 2,010 | http://arxiv.org/pdf/1005.5556v2 | Title Empirical learning aided weak domain knowledge form feature importance Summary Standard hybrid learner use domain knowledge require stronger knowledge hard expensive acquire However weaker domain knowledge benefit prior knowledge cost effective Weak knowledge form feature relative importance FRI presented explain... | [-0.014742983505129814, -0.013229891657829285, -0.019791970029473305, -0.030785612761974335, -0.028199950233101845, -0.02570103108882904, 0.08041764795780182, 0.024752169847488403, 0.032797474414110184, 0.01318944338709116, 0.05035974830389023, -0.00034811056684702635, 0.01610279083251953, 0.08712884038686752, 0.032612... |
727 | 727 | ['J. J. Grefenstette', 'D. E. Moriarty', 'A. C. Schultz'] | 1106.0221v1 | There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey... | Evolutionary Algorithms for Reinforcement Learning | 2,011 | http://arxiv.org/pdf/1106.0221v1 | Title Evolutionary Algorithms Reinforcement Learning Summary two distinct approach solving reinforcement learning problem namely searching value function space searching policy space Temporal difference method evolutionary algorithm wellknown example approach Kaelbling Littman Moore recently provided informative survey... | [0.03982983157038689, 0.02611774392426014, -0.03365381062030792, -0.0257871076464653, -0.042755842208862305, -0.027617596089839935, -0.026217561215162277, 0.020619787275791168, -0.0354771688580513, 0.01724240370094776, 0.04367851838469505, 0.032135769724845886, -0.010958706960082054, 0.03356756642460823, -0.03311513364... |
728 | 728 | ['Guillaume Desjardins', 'Aaron Courville', 'Yoshua Bengio'] | 1203.4416v1 | The deep Boltzmann machine (DBM) has been an important development in the
quest for powerful "deep" probabilistic models. To date, simultaneous or joint
training of all layers of the DBM has been largely unsuccessful with existing
training methods. We introduce a simple regularization scheme that encourages
the weight ... | On Training Deep Boltzmann Machines | 2,012 | http://arxiv.org/pdf/1203.4416v1 | Title Training Deep Boltzmann Machines Summary deep Boltzmann machine DBM important development quest powerful deep probabilistic model date simultaneous joint training layer DBM largely unsuccessful existing training method introduce simple regularization scheme encourages weight vector associated hidden unit similar ... | [-0.013285397551953793, -0.009640747681260109, -0.02858475036919117, 0.04183497279882431, 0.017518483102321625, 0.011517945677042007, 0.07407122850418091, -0.015777211636304855, -0.015969205647706985, 0.02935321442782879, -0.010993031784892082, -0.012566630728542805, -0.0056161535903811455, 0.06052934005856514, 0.03048... |
729 | 729 | ['Farnood Merrikh-Bayat', 'Saeed Bagheri Shouraki'] | 1109.4609v1 | Fuzzy inference systems always suffer from the lack of efficient structures
or platforms for their hardware implementation. In this paper, we tried to
overcome this problem by proposing new method for the implementation of those
fuzzy inference systems which use fuzzy rule base to make inference. To achieve
this goal, ... | Memristive fuzzy edge detector | 2,011 | http://arxiv.org/pdf/1109.4609v1 | Title Memristive fuzzy edge detector Summary Fuzzy inference system always suffer lack efficient structure platform hardware implementation paper tried overcome problem proposing new method implementation fuzzy inference system use fuzzy rule base make inference achieve goal designed multilayer neurofuzzy computing sys... | [-0.054506924003362656, -0.008144539780914783, -0.07892411202192307, 0.0748184472322464, -0.011441108770668507, -0.013800231739878654, 0.02545432560145855, -0.0005565573810599744, 0.028147999197244644, 0.008172736503183842, 0.004141718149185181, 0.01474747620522976, -0.03236301615834236, 0.07134664803743362, 0.01824261... |
730 | 730 | ['Sebastián Basterrech', 'Gerardo Rubino'] | 1212.6276v1 | In the last decade, a new computational paradigm was introduced in the field
of Machine Learning, under the name of Reservoir Computing (RC). RC models are
neural networks which a recurrent part (the reservoir) that does not
participate in the learning process, and the rest of the system where no
recurrence (no neural ... | Echo State Queueing Network: a new reservoir computing learning tool | 2,012 | http://arxiv.org/pdf/1212.6276v1 | Title Echo State Queueing Network new reservoir computing learning tool Summary last decade new computational paradigm introduced field Machine Learning name Reservoir Computing RC RC model neural network recurrent part reservoir participate learning process rest system recurrence neural circuit occurs approach grown r... | [-0.04387815669178963, -0.0168597511947155, -0.046949636191129684, -0.007753667421638966, -0.04462617263197899, -0.01876181922852993, 0.012994497083127499, -0.04813118651509285, -0.04522783309221268, 0.0007096731569617987, 0.00988808274269104, -0.04099180921912193, 0.003933105152100325, 0.05911489576101303, 0.006672496... |
731 | 731 | ['David Silver', 'Hado van Hasselt', 'Matteo Hessel', 'Tom Schaul', 'Arthur Guez', 'Tim Harley', 'Gabriel Dulac-Arnold', 'David Reichert', 'Neil Rabinowitz', 'Andre Barreto', 'Thomas Degris'] | 1612.08810v3 | One of the key challenges of artificial intelligence is to learn models that
are effective in the context of planning. In this document we introduce the
predictron architecture. The predictron consists of a fully abstract model,
represented by a Markov reward process, that can be rolled forward multiple
"imagined" plan... | The Predictron: End-To-End Learning and Planning | 2,016 | http://arxiv.org/pdf/1612.08810v3 | Title Predictron EndToEnd Learning Planning Summary One key challenge artificial intelligence learn model effective context planning document introduce predictron architecture predictron consists fully abstract model represented Markov reward process rolled forward multiple imagined planning step forward pas predictron... | [0.0056665921583771706, -0.006368445698171854, -0.021463168784976006, -0.013271157629787922, -0.01797153428196907, -0.03033430315554142, -0.02080725133419037, -0.024465681985020638, -0.019865550100803375, 0.025212226435542107, -0.001936144195497036, 0.07284921407699585, -0.028282826766371727, 0.07988160848617554, -0.00... |
732 | 732 | ['Deepak Kumar', 'A G Ramakrishnan'] | 1407.6315v1 | Particle swarm optimization is used in several combinatorial optimization
problems. In this work, particle swarms are used to solve quadratic programming
problems with quadratic constraints. The approach of particle swarms is an
example for interior point methods in optimization as an iterative technique.
This approach... | Quadratically constrained quadratic programming for classification using
particle swarms and applications | 2,014 | http://arxiv.org/pdf/1407.6315v1 | Title Quadratically constrained quadratic programming classification using particle swarm application Summary Particle swarm optimization used several combinatorial optimization problem work particle swarm used solve quadratic programming problem quadratic constraint approach particle swarm example interior point metho... | [-0.03507625684142113, 0.029305221512913704, -0.02904825657606125, -0.016544800251722336, 0.057384174317121506, 0.010084465146064758, 0.005685581360012293, 0.02721221186220646, 0.016257494688034058, -0.00974691566079855, -0.018319960683584213, 0.04222463443875313, 0.012938722968101501, 0.008114258758723736, -0.02714114... |
733 | 733 | ['Wojciech Zaremba', 'Ilya Sutskever'] | 1410.4615v3 | Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are
widely used because they are expressive and are easy to train. Our interest
lies in empirically evaluating the expressiveness and the learnability of LSTMs
in the sequence-to-sequence regime by training them to evaluate short computer
program... | Learning to Execute | 2,014 | http://arxiv.org/pdf/1410.4615v3 | Title Learning Execute Summary Recurrent Neural Networks RNNs Long ShortTerm Memory unit LSTM widely used expressive easy train interest lie empirically evaluating expressiveness learnability LSTMs sequencetosequence regime training evaluate short computer program domain traditionally seen complex neural network consid... | [0.005658090114593506, 0.008094490505754948, -0.02637835592031479, 0.01725480705499649, -0.021237267181277275, -0.009866801090538502, 0.02591564506292343, -0.02976294979453087, -0.025311168283224106, -0.038631077855825424, 0.006735583301633596, -0.03363580256700516, 0.016377123072743416, 0.032064393162727356, 0.0411731... |
734 | 734 | ['Minje Kim', 'Paris Smaragdis'] | 1601.06071v1 | Based on the assumption that there exists a neural network that efficiently
represents a set of Boolean functions between all binary inputs and outputs, we
propose a process for developing and deploying neural networks whose weight
parameters, bias terms, input, and intermediate hidden layer output signals,
are all bin... | Bitwise Neural Networks | 2,016 | http://arxiv.org/pdf/1601.06071v1 | Title Bitwise Neural Networks Summary Based assumption exists neural network efficiently represents set Boolean function binary input output propose process developing deploying neural network whose weight parameter bias term input intermediate hidden layer output signal binaryvalued require basic bit logic feedforward... | [-0.033424027264118195, 0.03408483415842056, -0.03259963542222977, -0.00326509028673172, -0.0326678566634655, -0.017755355685949326, 0.07308824360370636, -0.0013033131835982203, -0.04563900828361511, -0.01738762855529785, 0.05179448798298836, 0.014192993752658367, -0.015645496547222137, 0.09992236644029617, 0.038498960... |
735 | 735 | ['Tom Zahavy', 'Nir Ben Zrihem', 'Shie Mannor'] | 1602.02658v4 | In recent years there is a growing interest in using deep representations for
reinforcement learning. In this paper, we present a methodology and tools to
analyze Deep Q-networks (DQNs) in a non-blind matter. Moreover, we propose a
new model, the Semi Aggregated Markov Decision Process (SAMDP), and an
algorithm that le... | Graying the black box: Understanding DQNs | 2,016 | http://arxiv.org/pdf/1602.02658v4 | Title Graying black box Understanding DQNs Summary recent year growing interest using deep representation reinforcement learning paper present methodology tool analyze Deep Qnetworks DQNs nonblind matter Moreover propose new model Semi Aggregated Markov Decision Process SAMDP algorithm learns automatically SAMDP model ... | [-0.04897117614746094, 0.006604126654565334, -0.014694291166961193, -0.0004318010760471225, 0.018252242356538773, 0.004054898861795664, 0.014945746399462223, -0.024500226601958275, -0.05785233899950981, 0.018399419263005257, 0.0006828439072705805, 0.02430298924446106, -0.012191477231681347, 0.08019841462373734, 0.00979... |
736 | 736 | ['Randal S. Olson', 'Nathan Bartley', 'Ryan J. Urbanowicz', 'Jason H. Moore'] | 1603.06212v1 | As the field of data science continues to grow, there will be an
ever-increasing demand for tools that make machine learning accessible to
non-experts. In this paper, we introduce the concept of tree-based pipeline
optimization for automating one of the most tedious parts of machine
learning---pipeline design. We imple... | Evaluation of a Tree-based Pipeline Optimization Tool for Automating
Data Science | 2,016 | http://arxiv.org/pdf/1603.06212v1 | Title Evaluation Treebased Pipeline Optimization Tool Automating Data Science Summary field data science continues grow everincreasing demand tool make machine learning accessible nonexperts paper introduce concept treebased pipeline optimization automating one tedious part machine learningpipeline design implement ope... | [-0.021703533828258514, 0.04981401935219765, -0.04987595975399017, -0.022133897989988327, -0.020270589739084244, 0.0028025531210005283, 0.02102573961019516, 0.005663945805281401, 0.00994651485234499, -0.029387181624770164, 0.0325920432806015, 0.0328822024166584, 0.014238768257200718, 0.11373019218444824, -0.01991215907... |
737 | 737 | ['Quan Liu', 'Hui Jiang', 'Andrew Evdokimov', 'Zhen-Hua Ling', 'Xiaodan Zhu', 'Si Wei', 'Yu Hu'] | 1603.07704v2 | In this paper, we propose a new deep learning approach, called neural
association model (NAM), for probabilistic reasoning in artificial
intelligence. We propose to use neural networks to model association between
any two events in a domain. Neural networks take one event as input and compute
a conditional probability ... | Probabilistic Reasoning via Deep Learning: Neural Association Models | 2,016 | http://arxiv.org/pdf/1603.07704v2 | Title Probabilistic Reasoning via Deep Learning Neural Association Models Summary paper propose new deep learning approach called neural association model NAM probabilistic reasoning artificial intelligence propose use neural network model association two event domain Neural network take one event input compute conditi... | [0.018449990078806877, 0.017635352909564972, -0.001346656703390181, 0.05619872361421585, -0.046915724873542786, 0.01549064926803112, 0.014106840826570988, -0.01580650359392166, 0.02446252852678299, 0.002544377464801073, 0.02468935213983059, 0.0049185920506715775, 0.004400919657200575, 0.04806472733616829, -0.0129656419... |
738 | 738 | ['Ishan P. Durugkar', 'Clemens Rosenbaum', 'Stefan Dernbach', 'Sridhar Mahadevan'] | 1606.04615v1 | Deep reinforcement learning has been shown to be a powerful framework for
learning policies from complex high-dimensional sensory inputs to actions in
complex tasks, such as the Atari domain. In this paper, we explore output
representation modeling in the form of temporal abstraction to improve
convergence and reliabil... | Deep Reinforcement Learning With Macro-Actions | 2,016 | http://arxiv.org/pdf/1606.04615v1 | Title Deep Reinforcement Learning MacroActions Summary Deep reinforcement learning shown powerful framework learning policy complex highdimensional sensory input action complex task Atari domain paper explore output representation modeling form temporal abstraction improve convergence reliability deep reinforcement lea... | [-0.02120278775691986, 0.005359957925975323, -0.047610633075237274, -0.005514223128557205, -0.008647213689982891, -0.0020072441548109055, 0.01049203984439373, -0.026103924959897995, -0.04826332628726959, 0.010998442769050598, -0.033669933676719666, 0.000630987691693008, -0.009201161563396454, 0.07648267596960068, 0.021... |
739 | 739 | ['Edward Choi', 'Mohammad Taha Bahadori', 'Joshua A. Kulas', 'Andy Schuetz', 'Walter F. Stewart', 'Jimeng Sun'] | 1608.05745v4 | Accuracy and interpretability are two dominant features of successful
predictive models. Typically, a choice must be made in favor of complex black
box models such as recurrent neural networks (RNN) for accuracy versus less
accurate but more interpretable traditional models such as logistic regression.
This tradeoff po... | RETAIN: An Interpretable Predictive Model for Healthcare using Reverse
Time Attention Mechanism | 2,016 | http://arxiv.org/pdf/1608.05745v4 | Title RETAIN Interpretable Predictive Model Healthcare using Reverse Time Attention Mechanism Summary Accuracy interpretability two dominant feature successful predictive model Typically choice must made favor complex black box model recurrent neural network RNN accuracy versus le accurate interpretable traditional mod... | [0.013573057949543, 0.05023243650794029, -0.022835073992609978, -0.03899867832660675, -0.005059984978288412, 0.01578308641910553, -0.024581685662269592, 0.020174631848931313, 0.01758205145597458, 0.020548513159155846, 0.03386103734374046, -0.06458643823862076, 0.03096551075577736, 0.07153351604938507, -0.00131754297763... |
740 | 740 | ['Meng Joo Er', 'Rajasekar Venkatesan', 'Ning Wang'] | 1608.08898v1 | In this paper a high speed neural network classifier based on extreme
learning machines for multi-label classification problem is proposed and
dis-cussed. Multi-label classification is a superset of traditional binary and
multi-class classification problems. The proposed work extends the extreme
learning machine techni... | A High Speed Multi-label Classifier based on Extreme Learning Machines | 2,016 | http://arxiv.org/pdf/1608.08898v1 | Title High Speed Multilabel Classifier based Extreme Learning Machines Summary paper high speed neural network classifier based extreme learning machine multilabel classification problem proposed discussed Multilabel classification superset traditional binary multiclass classification problem proposed work extends extr... | [0.011641849763691425, -0.019172636792063713, -0.013563303276896477, -0.0055283233523368835, 0.01633145846426487, 0.04251297563314438, 0.01093616709113121, 0.0065765297040343285, 0.02636253461241722, -0.032061949372291565, -0.018313191831111908, 0.05077899247407913, -0.03119768761098385, 0.03670485317707062, 0.01005120... |
741 | 741 | ['Meng Joo Er', 'Rajasekar Venkatesan', 'Ning Wang'] | 1609.00843v1 | Classification involves the learning of the mapping function that associates
input samples to corresponding target label. There are two major categories of
classification problems: Single-label classification and Multi-label
classification. Traditional binary and multi-class classifications are
sub-categories of single... | An Online Universal Classifier for Binary, Multi-class and Multi-label
Classification | 2,016 | http://arxiv.org/pdf/1609.00843v1 | Title Online Universal Classifier Binary Multiclass Multilabel Classification Summary Classification involves learning mapping function associate input sample corresponding target label two major category classification problem Singlelabel classification Multilabel classification Traditional binary multiclass classific... | [0.00635999022051692, 0.026650086045265198, -0.005068789701908827, -0.023504823446273804, 0.010311434976756573, 0.033111415803432465, 0.06899004429578781, 0.015605117194354534, 0.028564417734742165, -0.06869341433048248, 0.0148243959993124, 0.009656363166868687, 0.0006846588221378624, 0.05772661790251732, 0.00129340554... |
742 | 742 | ['Arif Budiman', 'Mohamad Ivan Fanany', 'Chan Basaruddin'] | 1610.01922v1 | A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive ca... | Adaptive Online Sequential ELM for Concept Drift Tackling | 2,016 | http://arxiv.org/pdf/1610.01922v1 | Title Adaptive Online Sequential ELM Concept Drift Tackling Summary machine learning method need adapt time change environment change known concept drift paper propose concept drift tackling method enhancement Online Sequential Extreme Learning Machine OSELM Constructive Enhancement OSELM CEOSELM adding adaptive capabi... | [-0.018714556470513344, 0.01274622417986393, -0.026175880804657936, -0.039601970463991165, 0.021359985694289207, 0.005881111603230238, -0.005135837011039257, 0.016426902264356613, -0.029276644811034203, -0.01450412068516016, 0.03420611843466759, 0.05599755793809891, 0.005857973359525204, 0.08668451756238937, -0.0149632... |
743 | 743 | ['Arif Budiman', 'Mohamad Ivan Fanany', 'Chan Basaruddin'] | 1610.02348v1 | In big data era, the data continuously generated and its distribution may
keep changes overtime. These challenges in online stream of data are known as
concept drift. In this paper, we proposed the Adaptive Convolutional ELM method
(ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid
Extreme Lea... | Adaptive Convolutional ELM For Concept Drift Handling in Online Stream
Data | 2,016 | http://arxiv.org/pdf/1610.02348v1 | Title Adaptive Convolutional ELM Concept Drift Handling Online Stream Data Summary big data era data continuously generated distribution may keep change overtime challenge online stream data known concept drift paper proposed Adaptive Convolutional ELM method ACNNELM enhancement Convolutional Neural Network CNN hybrid ... | [-0.023038430139422417, 0.016558583825826645, -0.04026084765791893, 0.013589044101536274, -0.015497859567403793, 0.009319085627794266, -0.005719159264117479, -0.011203566566109657, -0.046573903411626816, -0.005502530373632908, 0.06766276806592941, 0.019047144800424576, -0.02096056006848812, 0.0857652947306633, 0.008196... |
744 | 744 | ['Daniel Hein', 'Alexander Hentschel', 'Thomas Runkler', 'Steffen Udluft'] | 1610.05984v5 | Fuzzy controllers are efficient and interpretable system controllers for
continuous state and action spaces. To date, such controllers have been
constructed manually or trained automatically either using expert-generated
problem-specific cost functions or incorporating detailed knowledge about the
optimal control strat... | Particle Swarm Optimization for Generating Interpretable Fuzzy
Reinforcement Learning Policies | 2,016 | http://arxiv.org/pdf/1610.05984v5 | Title Particle Swarm Optimization Generating Interpretable Fuzzy Reinforcement Learning Policies Summary Fuzzy controller efficient interpretable system controller continuous state action space date controller constructed manually trained automatically either using expertgenerated problemspecific cost function incorpor... | [-0.023040346801280975, 0.003841106314212084, -0.029586870223283768, -0.017008721828460693, 0.061029527336359024, -0.024846505373716354, 0.006034351885318756, 0.02768389694392681, -0.03447912633419037, -0.001576033653691411, 0.015887467190623283, 0.06297402083873749, 0.001110281446017325, 0.06841270625591278, 0.0019515... |
745 | 745 | ['Marc Pickett', 'Rami Al-Rfou', 'Louis Shao', 'Chris Tar'] | 1610.06402v1 | The long-term memory of most connectionist systems lies entirely in the
weights of the system. Since the number of weights is typically fixed, this
bounds the total amount of knowledge that can be learned and stored. Though
this is not normally a problem for a neural network designed for a specific
task, such a bound i... | A Growing Long-term Episodic & Semantic Memory | 2,016 | http://arxiv.org/pdf/1610.06402v1 | Title Growing Longterm Episodic Semantic Memory Summary longterm memory connectionist system lie entirely weight system Since number weight typically fixed bound total amount knowledge learned stored Though normally problem neural network designed specific task bound undesirable system continually learns open range dom... | [0.017698826268315315, 0.06042559817433357, -0.028343917801976204, 0.01917206309735775, 0.008936609141528606, 0.006127943750470877, 0.003440682077780366, -0.022848647087812424, -0.03195356950163841, -0.009028660133481026, 0.033631667494773865, -0.0035851760767400265, -0.034466084092855453, 0.0609319843351841, -0.008430... |
746 | 746 | ['Wen-Chieh Fang', 'Yi-ting Chiang'] | 1611.02512v1 | Humans can learn concepts or recognize items from just a handful of examples,
while machines require many more samples to perform the same task. In this
paper, we build a computational model to investigate the possibility of this
kind of rapid learning. The proposed method aims to improve the learning task
of input fro... | Cognitive Discriminative Mappings for Rapid Learning | 2,016 | http://arxiv.org/pdf/1611.02512v1 | Title Cognitive Discriminative Mappings Rapid Learning Summary Humans learn concept recognize item handful example machine require many sample perform task paper build computational model investigate possibility kind rapid learning proposed method aim improve learning task input sensory memory leveraging information re... | [-0.029868775978684425, -0.07687529176473618, -0.04442603141069412, 0.012023000977933407, 0.0004023114452138543, 0.03359444439411163, 0.012297326698899269, 0.0009574511204846203, 0.05177688226103783, -0.030614037066698074, 0.005810556001961231, 0.010457903146743774, 0.018516918644309044, 0.001411361270584166, -0.002868... |
747 | 747 | ['Hao Shen'] | 1611.05827v3 | Training deep neural networks for solving machine learning problems is one
great challenge in the field, mainly due to its associated optimisation problem
being highly non-convex. Recent developments have suggested that many training
algorithms do not suffer from undesired local minima under certain scenario,
and conse... | Towards a Mathematical Understanding of the Difficulty in Learning with
Feedforward Neural Networks | 2,016 | http://arxiv.org/pdf/1611.05827v3 | Title Towards Mathematical Understanding Difficulty Learning Feedforward Neural Networks Summary Training deep neural network solving machine learning problem one great challenge field mainly due associated optimisation problem highly nonconvex Recent development suggested many training algorithm suffer undesired local... | [0.001356947934255004, 0.0023668515495955944, -0.0050458284094929695, 0.02698616497218609, -0.017796842381358147, 0.006771114654839039, 0.002815478714182973, 0.002154191955924034, -0.032144974917173386, 0.029983893036842346, 0.01813541166484356, -0.012416559271514416, 0.013703123666346073, 0.03948216885328293, 0.041411... |
748 | 748 | ['Gonzalo Diaz', 'Achille Fokoue', 'Giacomo Nannicini', 'Horst Samulowitz'] | 1705.08520v1 | A major challenge in designing neural network (NN) systems is to determine
the best structure and parameters for the network given the data for the
machine learning problem at hand. Examples of parameters are the number of
layers and nodes, the learning rates, and the dropout rates. Typically, these
parameters are chos... | An effective algorithm for hyperparameter optimization of neural
networks | 2,017 | http://arxiv.org/pdf/1705.08520v1 | Title effective algorithm hyperparameter optimization neural network Summary major challenge designing neural network NN system determine best structure parameter network given data machine learning problem hand Examples parameter number layer node learning rate dropout rate Typically parameter chosen based heuristic r... | [-0.0006385190645232797, -0.001494747120887041, -0.02385726384818554, -0.02010945975780487, 0.033184126019477844, -0.02971462346613407, 0.015958281233906746, -0.00976563710719347, 0.0197676420211792, 0.017336972057819366, -0.014380546286702156, 0.046589069068431854, 0.002216424560174346, 0.07431766390800476, 0.01886870... |
749 | 749 | ['Decebal Constantin Mocanu', 'Elena Mocanu', 'Peter Stone', 'Phuong H. Nguyen', 'Madeleine Gibescu', 'Antonio Liotta'] | 1707.04780v1 | Through the success of deep learning, Artificial Neural Networks (ANNs) are
among the most used artificial intelligence methods nowadays. ANNs have led to
major breakthroughs in various domains, such as particle physics, reinforcement
learning, speech recognition, computer vision, and so on. Taking inspiration
from the... | Evolutionary Training of Sparse Artificial Neural Networks: A Network
Science Perspective | 2,017 | http://arxiv.org/pdf/1707.04780v1 | Title Evolutionary Training Sparse Artificial Neural Networks Network Science Perspective Summary success deep learning Artificial Neural Networks ANNs among used artificial intelligence method nowadays ANNs led major breakthrough various domain particle physic reinforcement learning speech recognition computer vision ... | [-0.027887728065252304, 0.0174647718667984, -0.032964251935482025, 0.02248745784163475, -0.00527630839496851, -0.034026794135570526, 0.012799931690096855, 0.011461532674729824, -0.03405437991023064, 0.028860636055469513, -0.020940473303198814, 0.04646703973412514, -0.020626455545425415, 0.03567114844918251, 0.033183451... |
750 | 750 | ['Ritambhara Singh', 'Jack Lanchantin', 'Arshdeep Sekhon', 'Yanjun Qi'] | 1708.00339v3 | The past decade has seen a revolution in genomic technologies that enable a
flood of genome-wide profiling of chromatin marks. Recent literature tried to
understand gene regulation by predicting gene expression from large-scale
chromatin measurements. Two fundamental challenges exist for such learning
tasks: (1) genome... | Attend and Predict: Understanding Gene Regulation by Selective Attention
on Chromatin | 2,017 | http://arxiv.org/pdf/1708.00339v3 | Title Attend Predict Understanding Gene Regulation Selective Attention Chromatin Summary past decade seen revolution genomic technology enable flood genomewide profiling chromatin mark Recent literature tried understand gene regulation predicting gene expression largescale chromatin measurement Two fundamental challeng... | [-0.004478110931813717, -0.001050654798746109, -0.033037297427654266, -0.04848727956414223, 0.021697215735912323, 0.007799375336617231, 0.06353030353784561, 0.007420167792588472, 0.04580581933259964, 0.058938346803188324, -0.02583427168428898, 0.0028135734610259533, -0.005884683690965176, 0.051225434988737106, -0.01807... |
751 | 751 | ['Eric Martin', 'Chris Cundy'] | 1709.04057v2 | Recurrent neural networks (RNNs) are widely used to model sequential data but
their non-linear dependencies between sequence elements prevent parallelizing
training over sequence length. We show the training of RNNs with only linear
sequential dependencies can be parallelized over the sequence length using the
parallel... | Parallelizing Linear Recurrent Neural Nets Over Sequence Length | 2,017 | http://arxiv.org/pdf/1709.04057v2 | Title Parallelizing Linear Recurrent Neural Nets Sequence Length Summary Recurrent neural network RNNs widely used model sequential data nonlinear dependency sequence element prevent parallelizing training sequence length show training RNNs linear sequential dependency parallelized sequence length using parallel scan a... | [0.02666763961315155, 0.032647982239723206, -0.01742280274629593, 0.04196585342288017, -0.020144179463386536, 0.006452351342886686, 0.03652665764093399, -0.009637504816055298, -0.022059965878725052, -0.01910150796175003, 0.024840177968144417, -0.03230572119355202, 0.033829059451818466, 0.011337578296661377, -0.00257403... |
752 | 752 | ['Filipe Alves Neto Verri', 'Renato Tinós', 'Liang Zhao'] | 1710.09300v1 | Data and knowledge representation are fundamental concepts in machine
learning. The quality of the representation impacts the performance of the
learning model directly. Feature learning transforms or enhances raw data to
structures that are effectively exploited by those models. In recent years,
several works have bee... | Feature learning in feature-sample networks using multi-objective
optimization | 2,017 | http://arxiv.org/pdf/1710.09300v1 | Title Feature learning featuresample network using multiobjective optimization Summary Data knowledge representation fundamental concept machine learning quality representation impact performance learning model directly Feature learning transforms enhances raw data structure effectively exploited model recent year seve... | [-0.0015126197831705213, 0.02810029499232769, -0.0386347770690918, -0.0009802425047382712, -0.003071610815823078, -0.02640114165842533, 0.022930234670639038, 0.006832682061940432, -0.020855717360973358, -0.02538805827498436, -9.471612429479137e-05, 0.011832165531814098, 0.0013946352992206812, 0.07586498558521271, -0.00... |
753 | 753 | ['Chelsea Finn', 'Sergey Levine'] | 1710.11622v3 | Learning to learn is a powerful paradigm for enabling models to learn from
data more effectively and efficiently. A popular approach to meta-learning is
to train a recurrent model to read in a training dataset as input and output
the parameters of a learned model, or output predictions for new test inputs.
Alternativel... | Meta-Learning and Universality: Deep Representations and Gradient
Descent can Approximate any Learning Algorithm | 2,017 | http://arxiv.org/pdf/1710.11622v3 | Title MetaLearning Universality Deep Representations Gradient Descent Approximate Learning Algorithm Summary Learning learn powerful paradigm enabling model learn data effectively efficiently popular approach metalearning train recurrent model read training dataset input output parameter learned model output prediction... | [-0.02336861379444599, 0.014397252351045609, -0.011752142570912838, 0.02348981611430645, 0.025858798995614052, 0.0008374102762900293, 0.006655561272054911, -0.01857181079685688, -0.06292036175727844, -0.00812552496790886, -0.02127445861697197, 0.0339849479496479, -0.01750265806913376, 0.030898740515112877, 0.0466424003... |
754 | 754 | ['Paulo Rauber', 'Filipe Mutz', 'Juergen Schmidhuber'] | 1711.06006v1 | Goal-conditional policies allow reinforcement learning agents to pursue
specific goals during different episodes. In addition to their potential to
generalize desired behavior to unseen goals, such policies may also help in
defining options for arbitrary subgoals, enabling higher-level planning. While
trying to achieve... | Hindsight policy gradients | 2,017 | http://arxiv.org/pdf/1711.06006v1 | Title Hindsight policy gradient Summary Goalconditional policy allow reinforcement learning agent pursue specific goal different episode addition potential generalize desired behavior unseen goal policy may also help defining option arbitrary subgoals enabling higherlevel planning trying achieve specific goal agent may... | [0.03146608918905258, 0.06408703327178955, 0.010849918238818645, -0.061614830046892166, 0.009132280945777893, -0.0132643086835742, -0.0090391980484128, 0.02972756326198578, -0.03456856682896614, 0.016909440979361534, 0.0024515341501682997, 0.02041325531899929, -0.04846867918968201, 0.0543789304792881, 0.005326449871063... |
755 | 755 | ['Mohammad Javad Shafiee', 'Francis Li', 'Brendan Chwyl', 'Alexander Wong'] | 1711.07459v1 | While deep neural networks have been shown in recent years to outperform
other machine learning methods in a wide range of applications, one of the
biggest challenges with enabling deep neural networks for widespread deployment
on edge devices such as mobile and other consumer devices is high computational
and memory r... | SquishedNets: Squishing SqueezeNet further for edge device scenarios via
deep evolutionary synthesis | 2,017 | http://arxiv.org/pdf/1711.07459v1 | Title SquishedNets Squishing SqueezeNet edge device scenario via deep evolutionary synthesis Summary deep neural network shown recent year outperform machine learning method wide range application one biggest challenge enabling deep neural network widespread deployment edge device mobile consumer device high computatio... | [-0.02499484270811081, 0.024122700095176697, -0.04396184906363487, 0.05038096755743027, 0.010828804224729538, -0.0407930389046669, 0.08757714927196503, -0.017255116254091263, -0.043878424912691116, 0.005290680099278688, 0.004355383105576038, 0.004438853822648525, -0.018252452835440636, 0.08484749495983124, 0.0234755445... |
756 | 756 | ['Pierre-Yves Oudeyer'] | 1712.01626v1 | Autonomous lifelong development and learning is a fundamental capability of
humans, differentiating them from current deep learning systems. However, other
branches of artificial intelligence have designed crucial ingredients towards
autonomous learning: curiosity and intrinsic motivation, social learning and
natural i... | Autonomous development and learning in artificial intelligence and
robotics: Scaling up deep learning to human--like learning | 2,017 | http://arxiv.org/pdf/1712.01626v1 | Title Autonomous development learning artificial intelligence robotics Scaling deep learning humanlike learning Summary Autonomous lifelong development learning fundamental capability human differentiating current deep learning system However branch artificial intelligence designed crucial ingredient towards autonomous... | [0.04060013219714165, 0.03074970282614231, -0.05631304904818535, -0.047551605850458145, 0.00949346274137497, 0.03383752331137657, 0.05041634663939476, -0.04431520029902458, -0.01887732557952404, 0.005613050889223814, 0.006764853838831186, 0.03701455146074295, 0.012814365327358246, 0.05520724132657051, -0.00501590594649... |
757 | 757 | ['Leonid Peshkin', 'Christian R. Shelton'] | cs/0204043v1 | Searching the space of policies directly for the optimal policy has been one
popular method for solving partially observable reinforcement learning
problems. Typically, with each change of the target policy, its value is
estimated from the results of following that very policy. This requires a large
number of interacti... | Learning from Scarce Experience | 2,002 | http://arxiv.org/pdf/cs/0204043v1 | Title Learning Scarce Experience Summary Searching space policy directly optimal policy one popular method solving partially observable reinforcement learning problem Typically change target policy value estimated result following policy requires large number interaction environment different police considered present ... | [0.03234167769551277, 0.05274500325322151, 0.0061468263156712055, -0.060232844203710556, -0.016274088993668556, -0.03032250888645649, 0.027484552934765816, -0.0017381637590005994, -0.05202985182404518, 0.03140789270401001, -0.005477289669215679, 0.027840979397296906, -0.033138565719127655, 0.04808039218187332, -0.01369... |
758 | 758 | ['Martin Pelikan', 'Kumara Sastry'] | cs/0402032v1 | This paper describes how fitness inheritance can be used to estimate fitness
for a proportion of newly sampled candidate solutions in the Bayesian
optimization algorithm (BOA). The goal of estimating fitness for some candidate
solutions is to reduce the number of fitness evaluations for problems where
fitness evaluatio... | Fitness inheritance in the Bayesian optimization algorithm | 2,004 | http://arxiv.org/pdf/cs/0402032v1 | Title Fitness inheritance Bayesian optimization algorithm Summary paper describes fitness inheritance used estimate fitness proportion newly sampled candidate solution Bayesian optimization algorithm BOA goal estimating fitness candidate solution reduce number fitness evaluation problem fitness evaluation expensive Bay... | [-0.009404526092112064, 0.02924974448978901, -0.029009466990828514, -0.008554755710065365, 0.0031043144408613443, -0.04595917835831642, 0.0030008931644260883, 0.029038438573479652, -0.02064596489071846, -0.01607423461973667, 0.0007445363444276154, 0.02656448818743229, 0.011375466361641884, 0.008967547677457333, -0.0278... |
759 | 759 | ['Vitaly Schetinin', 'Joachim Schult'] | cs/0504070v1 | In this paper we describe a new method combining the polynomial neural
network and decision tree techniques in order to derive comprehensible
classification rules from clinical electroencephalograms (EEGs) recorded from
sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement,
muscle and noise artif... | The Combined Technique for Detection of Artifacts in Clinical
Electroencephalograms of Sleeping Newborns | 2,005 | http://arxiv.org/pdf/cs/0504070v1 | Title Combined Technique Detection Artifacts Clinical Electroencephalograms Sleeping Newborns Summary paper describe new method combining polynomial neural network decision tree technique order derive comprehensible classification rule clinical electroencephalogram EEGs recorded sleeping newborn EEGs heavily corrupted ... | [-0.031679943203926086, 0.015686405822634697, -0.003519346471875906, 0.0034423188772052526, -0.005845001898705959, 0.054155103862285614, -0.01054330449551344, 0.019419606775045395, -0.05232229083776474, -0.016257239505648613, 0.0851726233959198, 0.014769934117794037, 0.05361296236515045, 0.017282292246818542, 0.0267698... |
760 | 760 | ['Greg Hulley', 'Tshilidzi Marwala'] | 0709.3965v2 | The ability of a classifier to take on new information and classes by
evolving the classifier without it having to be fully retrained is known as
incremental learning. Incremental learning has been successfully applied to
many classification problems, where the data is changing and is not all
available at once. In this... | Evolving Classifiers: Methods for Incremental Learning | 2,007 | http://arxiv.org/pdf/0709.3965v2 | Title Evolving Classifiers Methods Incremental Learning Summary ability classifier take new information class evolving classifier without fully retrained known incremental learning Incremental learning successfully applied many classification problem data changing available paper comparison Learn one recent incremental... | [0.021815409883856773, 0.10014214366674423, 0.0016821863828226924, 0.010092966258525848, -0.029712988063693047, 0.003038417547941208, -0.020706139504909515, 0.06440241634845734, 0.017087634652853012, -0.03599880263209343, 0.04391868785023689, 0.04782071337103844, -0.016808969900012016, 0.04410529509186745, -0.020251888... |
761 | 761 | ['Dasika Ratna Deepthi', 'G. R. Aditya Krishna', 'K. Eswaran'] | 0712.0938v1 | This paper proposes an unsupervised learning technique by using Multi-layer
Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer
Mirroring Neural Network is a neural network that can be trained with
generalized data inputs (different categories of image patterns) to perform
non-linear dimensionality r... | Automatic Pattern Classification by Unsupervised Learning Using
Dimensionality Reduction of Data with Mirroring Neural Networks | 2,007 | http://arxiv.org/pdf/0712.0938v1 | Title Automatic Pattern Classification Unsupervised Learning Using Dimensionality Reduction Data Mirroring Neural Networks Summary paper proposes unsupervised learning technique using Multilayer Mirroring Neural Network Forgys clustering algorithm Multilayer Mirroring Neural Network neural network trained generalized d... | [-0.02508358657360077, 0.015034792013466358, -0.05080763250589371, 0.07208844274282455, -0.007659881375730038, 0.021959535777568817, 0.08361513912677765, 0.01171223632991314, -0.0028858822770416737, 0.006447590421885252, -0.026457296684384346, 0.038594167679548264, 0.007938126102089882, 0.019436946138739586, 0.01855116... |
762 | 762 | ['Alejandro Chinea Manrique De Lara', 'Juan Manuel Moreno', 'Arostegui Jordi Madrenas', 'Joan Cabestany'] | 0712.3654v1 | In this paper we shall review the common problems associated with Piecewise
Linear Separation incremental algorithms. This kind of neural models yield poor
performances when dealing with some classification problems, due to the
evolving schemes used to construct the resulting networks. So as to avoid this
undesirable b... | Improving the Performance of PieceWise Linear Separation Incremental
Algorithms for Practical Hardware Implementations | 2,007 | http://arxiv.org/pdf/0712.3654v1 | Title Improving Performance PieceWise Linear Separation Incremental Algorithms Practical Hardware Implementations Summary paper shall review common problem associated Piecewise Linear Separation incremental algorithm kind neural model yield poor performance dealing classification problem due evolving scheme used constr... | [0.0001494176685810089, 0.07189055532217026, -0.022032974287867546, 0.0388922281563282, -0.040404386818408966, -0.03054353967308998, 0.07194789499044418, 0.032455530017614365, -0.017937183380126953, -0.04106805846095085, 0.06806177645921707, 0.013983677141368389, -0.03122500143945217, 0.07794579118490219, -0.0069475700... |
763 | 763 | ['N. Suguna', 'K. Thanushkodi'] | 1006.4540v1 | Feature selection refers to the problem of selecting relevant features which
produce the most predictive outcome. In particular, feature selection task is
involved in datasets containing huge number of features. Rough set theory has
been one of the most successful methods used for feature selection. However,
this metho... | A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee
Colony Optimization | 2,010 | http://arxiv.org/pdf/1006.4540v1 | Title Novel Rough Set Reduct Algorithm Medical Domain Based Bee Colony Optimization Summary Feature selection refers problem selecting relevant feature produce predictive outcome particular feature selection task involved datasets containing huge number feature Rough set theory one successful method used feature select... | [-0.01260025892406702, -0.04312650486826897, -0.026841871440410614, -0.061229243874549866, -0.031140973791480064, 0.008109571412205696, -0.01014422532171011, 0.06688329577445984, 0.028713781386613846, 0.0020852144807577133, 0.07218075543642044, -0.006249288097023964, 0.018859602510929108, 0.031781069934368134, -0.04068... |
764 | 764 | ['Pekka Malo', 'Pyry Siitari', 'Ankur Sinha'] | 1012.0841v1 | Most of the existing information retrieval systems are based on bag of words
model and are not equipped with common world knowledge. Work has been done
towards improving the efficiency of such systems by using intelligent
algorithms to generate search queries, however, not much research has been done
in the direction o... | Automated Query Learning with Wikipedia and Genetic Programming | 2,010 | http://arxiv.org/pdf/1012.0841v1 | Title Automated Query Learning Wikipedia Genetic Programming Summary existing information retrieval system based bag word model equipped common world knowledge Work done towards improving efficiency system using intelligent algorithm generate search query however much research done direction incorporating humanandsocie... | [0.07866102457046509, 0.035998765379190445, -0.037776295095682144, 0.02672942355275154, -0.009266302920877934, 0.009866967797279358, -0.04097622632980347, 0.06424432247877121, -0.012354201637208462, -0.04645919427275658, 0.0025595563929528, 0.03770696371793747, -0.03312350809574127, 0.040822792798280716, 0.003726591821... |
765 | 765 | ['Weishan Dong', 'Tianshi Chen', 'Peter Tino', 'Xin Yao'] | 1111.2221v1 | Since Estimation of Distribution Algorithms (EDA) were proposed, many
attempts have been made to improve EDAs' performance in the context of global
optimization. So far, the studies or applications of multivariate probabilistic
model based continuous EDAs are still restricted to rather low dimensional
problems (smaller... | Scaling Up Estimation of Distribution Algorithms For Continuous
Optimization | 2,011 | http://arxiv.org/pdf/1111.2221v1 | Title Scaling Estimation Distribution Algorithms Continuous Optimization Summary Since Estimation Distribution Algorithms EDA proposed many attempt made improve EDAs performance context global optimization far study application multivariate probabilistic model based continuous EDAs still restricted rather low dimension... | [0.002503087744116783, 0.016965016722679138, -0.03229774162173271, -0.0029946565628051758, 0.03892260789871216, -0.020755266770720482, 0.022248180583119392, 0.0043020108714699745, -0.0246600154787302, 0.03191452473402023, -0.0027542049065232277, 0.014080332592129707, 0.015541508793830872, 0.049390487372875214, 0.003658... |
766 | 766 | ['Martin Pelikan', 'Mark W. Hauschild', 'Pier Luca Lanzi'] | 1203.5443v2 | An automated technique has recently been proposed to transfer learning in the
hierarchical Bayesian optimization algorithm (hBOA) based on distance-based
statistics. The technique enables practitioners to improve hBOA efficiency by
collecting statistics from probabilistic models obtained in previous hBOA runs
and using... | Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA | 2,012 | http://arxiv.org/pdf/1203.5443v2 | Title Transfer Learning Soft DistanceBased Bias Hierarchical BOA Summary automated technique recently proposed transfer learning hierarchical Bayesian optimization algorithm hBOA based distancebased statistic technique enables practitioner improve hBOA efficiency collecting statistic probabilistic model obtained previo... | [-0.014708032831549644, 0.009262935258448124, 0.012727460823953152, -0.007349533028900623, -0.016176505014300346, -0.023754090070724487, 0.04161599278450012, 0.02191346138715744, -0.0016984455287456512, -0.012812601402401924, -0.01368795521557331, 0.025898709893226624, 0.04316175729036331, 0.06633852422237396, -0.00330... |
767 | 767 | ['Richard J. Preen', 'Larry Bull'] | 1204.4200v2 | A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using a discrete
dynamical system representation within the XCS Learning Classifier System. In
particular, asynchron... | Discrete Dynamical Genetic Programming in XCS | 2,012 | http://arxiv.org/pdf/1204.4200v2 | Title Discrete Dynamical Genetic Programming XCS Summary number representation scheme presented use within Learning Classifier Systems ranging binary encoding neural network paper present result investigation using discrete dynamical system representation within XCS Learning Classifier System particular asynchronous ra... | [-0.034023359417915344, 0.030810607597231865, -0.06012888252735138, -0.0337800532579422, -0.040345024317502975, -0.03351465240120888, 0.0017823297530412674, 0.011603031307458878, -0.003092412371188402, 0.0024450630880892277, 0.10172656178474426, 0.020060474053025246, -0.02859484776854515, 0.09362750500440598, -0.029521... |
768 | 768 | ['Richard J. Preen', 'Larry Bull'] | 1204.4202v1 | A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to Neural Networks,
and more recently Dynamical Genetic Programming (DGP). This paper presents
results from an investigation into using a fuzzy DGP representation within the
XCSF Learning Cla... | Fuzzy Dynamical Genetic Programming in XCSF | 2,012 | http://arxiv.org/pdf/1204.4202v1 | Title Fuzzy Dynamical Genetic Programming XCSF Summary number representation scheme presented use within Learning Classifier Systems ranging binary encoding Neural Networks recently Dynamical Genetic Programming DGP paper present result investigation using fuzzy DGP representation within XCSF Learning Classifier System... | [-0.03398854285478592, 0.04416368901729584, -0.06830047070980072, -0.02871222421526909, -0.008317552506923676, -0.01767830178141594, -0.018961185589432716, 0.02694653533399105, -0.019403336569666862, -0.0214847344905138, 0.07777810096740723, -0.002548235235735774, -0.011316522024571896, 0.07732318341732025, -0.02466365... |
769 | 769 | ['Jonathan Roberts', 'Ke Chen'] | 1308.6415v2 | Procedural content generation (PCG) has recently become one of the hottest
topics in computational intelligence and AI game researches. Among a variety of
PCG techniques, search-based approaches overwhelmingly dominate PCG development
at present. While SBPCG leads to promising results and successful applications,
it po... | Learning-Based Procedural Content Generation | 2,013 | http://arxiv.org/pdf/1308.6415v2 | Title LearningBased Procedural Content Generation Summary Procedural content generation PCG recently become one hottest topic computational intelligence AI game research Among variety PCG technique searchbased approach overwhelmingly dominate PCG development present SBPCG lead promising result successful application po... | [0.006704776082187891, 0.028883595019578934, -0.03291783481836319, 0.02905902825295925, 0.002205322030931711, -0.006693611852824688, -0.01848847232758999, -0.00043214860488660634, -0.01983494684100151, 0.04324449598789215, -0.0035593716893345118, 0.016182001680135727, -0.025613220408558846, 0.05025029927492142, -0.0205... |
770 | 770 | ['Wojciech Jaśkowski'] | 1406.1509v3 | N-tuple networks have been successfully used as position evaluation functions
for board games such as Othello or Connect Four. The effectiveness of such
networks depends on their architecture, which is determined by the placement of
constituent n-tuples, sequences of board locations, providing input to the
network. The... | Systematic N-tuple Networks for Position Evaluation: Exceeding 90% in
the Othello League | 2,014 | http://arxiv.org/pdf/1406.1509v3 | Title Systematic Ntuple Networks Position Evaluation Exceeding 90 Othello League Summary Ntuple network successfully used position evaluation function board game Othello Connect Four effectiveness network depends architecture determined placement constituent ntuples sequence board location providing input network popul... | [-0.00758735416457057, -0.0052129290997982025, -0.035774797201156616, 0.021313093602657318, -0.02567540854215622, -0.06571634858846664, 0.05021866038441658, -0.01986871287226677, -0.03476303815841675, 0.0057578133419156075, -0.0013465570518746972, -0.009042971767485142, 0.01685352623462677, 0.05219406262040138, 0.06060... |
771 | 771 | ['Önder Gürcan', 'Carole Bernon', 'Kemal S. Türker'] | 1207.3760v1 | In this paper, the early design of our self-organized agent-based simulation
model for exploration of synaptic connections that faithfully generates what is
observed in natural situation is given. While we take inspiration from
neuroscience, our intent is not to create a veridical model of processes in
neurodevelopment... | Towards a Self-Organized Agent-Based Simulation Model for Exploration of
Human Synaptic Connections | 2,012 | http://arxiv.org/pdf/1207.3760v1 | Title Towards SelfOrganized AgentBased Simulation Model Exploration Human Synaptic Connections Summary paper early design selforganized agentbased simulation model exploration synaptic connection faithfully generates observed natural situation given take inspiration neuroscience intent create veridical model process ne... | [0.009691315703094006, -0.0249745212495327, -0.028372108936309814, -0.020776573568582535, -0.012043382041156292, -0.0023995819501578808, 0.03426482900977135, -0.026169540360569954, 0.06224702298641205, -0.021491480991244316, -0.011645463295280933, 0.02812385931611061, -0.00671433238312602, 0.022176727652549744, 0.05902... |
772 | 772 | ['G. N. Tripathi', 'V. Rihani'] | 1207.4931v1 | The paper presents the electronic design and motion planning of a robot based
on decision making regarding its straight motion and precise turn using
Artificial Neural Network (ANN). The ANN helps in learning of robot so that it
performs motion autonomously. The weights calculated are implemented in
microcontroller. Th... | Motion Planning Of an Autonomous Mobile Robot Using Artificial Neural
Network | 2,012 | http://arxiv.org/pdf/1207.4931v1 | Title Motion Planning Autonomous Mobile Robot Using Artificial Neural Network Summary paper present electronic design motion planning robot based decision making regarding straight motion precise turn using Artificial Neural Network ANN ANN help learning robot performs motion autonomously weight calculated implemented ... | [0.02890363149344921, -0.04013105854392052, 0.0036974656395614147, -0.025588860735297203, 0.00411352701485157, -0.03195500746369362, 0.010114934295415878, -0.032883137464523315, -0.026571722701191902, 0.003150728764012456, 0.02828584797680378, 0.08020555227994919, -0.015967482700943947, 0.026520198211073875, 0.03149738... |
773 | 773 | ['Rónán Daly', 'Qiang Shen'] | 1401.3464v1 | Bayesian networks are a useful tool in the representation of uncertain
knowledge. This paper proposes a new algorithm called ACO-E, to learn the
structure of a Bayesian network. It does this by conducting a search through
the space of equivalence classes of Bayesian networks using Ant Colony
Optimization (ACO). To this... | Learning Bayesian Network Equivalence Classes with Ant Colony
Optimization | 2,014 | http://arxiv.org/pdf/1401.3464v1 | Title Learning Bayesian Network Equivalence Classes Ant Colony Optimization Summary Bayesian network useful tool representation uncertain knowledge paper proposes new algorithm called ACOE learn structure Bayesian network conducting search space equivalence class Bayesian network using Ant Colony Optimization ACO end t... | [-0.021568967029452324, -0.0019393136026337743, -0.020777583122253418, -0.00777273578569293, -0.025497393682599068, -0.01368641946464777, -0.0033678885083645582, 0.03526681289076805, 0.02410024218261242, -0.04581594094634056, 0.0031160584185272455, 0.04981612786650658, -0.012527765706181526, 0.03706486523151398, -0.036... |
774 | 774 | ['Kenton W. Murray', 'Jayant Krishnamurthy'] | 1612.00712v1 | We present probabilistic neural programs, a framework for program induction
that permits flexible specification of both a computational model and inference
algorithm while simultaneously enabling the use of deep neural networks.
Probabilistic neural programs combine a computation graph for specifying a
neural network w... | Probabilistic Neural Programs | 2,016 | http://arxiv.org/pdf/1612.00712v1 | Title Probabilistic Neural Programs Summary present probabilistic neural program framework program induction permit flexible specification computational model inference algorithm simultaneously enabling use deep neural network Probabilistic neural program combine computation graph specifying neural network operator wei... | [-0.02065419964492321, 0.05876593291759491, -0.023386411368846893, 0.005874189548194408, -0.036246638745069504, -0.03405193239450455, -0.015628725290298462, -0.06682796776294708, -0.027826080098748207, 0.016735929995775223, 0.06017959490418434, 0.018140411004424095, 0.03910701349377632, 0.08217211067676544, 0.014991165... |
775 | 775 | ['András Lőrincz', 'Máté Csákvári', 'Áron Fóthi', 'Zoltán Ádám Milacski', 'András Sárkány', 'Zoltán Tősér'] | 1612.00745v1 | Machine learning is making substantial progress in diverse applications. The
success is mostly due to advances in deep learning. However, deep learning can
make mistakes and its generalization abilities to new tasks are questionable.
We ask when and how one can combine network outputs, when (i) details of the
observati... | Cognitive Deep Machine Can Train Itself | 2,016 | http://arxiv.org/pdf/1612.00745v1 | Title Cognitive Deep Machine Train Summary Machine learning making substantial progress diverse application success mostly due advance deep learning However deep learning make mistake generalization ability new task questionable ask one combine network output detail observation evaluated learned deep component ii fact ... | [-0.01585247926414013, 0.006229399237781763, -0.011902273632586002, -0.011448919773101807, 0.01015924196690321, 0.02907133474946022, 0.04583369567990303, -0.030547264963388443, -0.0053973812609910965, -0.013417177833616734, 0.016290709376335144, 0.040741998702287674, -0.01556606125086546, 0.04691661149263382, 0.0208981... |
776 | 776 | ['Alexander L. Gaunt', 'Marc Brockschmidt', 'Rishabh Singh', 'Nate Kushman', 'Pushmeet Kohli', 'Jonathan Taylor', 'Daniel Tarlow'] | 1612.00817v1 | We study machine learning formulations of inductive program synthesis; that
is, given input-output examples, synthesize source code that maps inputs to
corresponding outputs. Our key contribution is TerpreT, a domain-specific
language for expressing program synthesis problems. A TerpreT model is composed
of a specifica... | Summary - TerpreT: A Probabilistic Programming Language for Program
Induction | 2,016 | http://arxiv.org/pdf/1612.00817v1 | Title Summary TerpreT Probabilistic Programming Language Program Induction Summary study machine learning formulation inductive program synthesis given inputoutput example synthesize source code map input corresponding output key contribution TerpreT domainspecific language expressing program synthesis problem TerpreT ... | [-0.019659869372844696, -0.004071400035172701, -0.02797825261950493, 0.0072052963078022, -0.022938398644328117, -0.0064613064751029015, -0.029354672878980637, -0.013239962980151176, -0.013619085773825645, -0.025967827066779137, 0.06879919022321701, 0.08410071581602097, 0.014550857245922089, 0.0999143049120903, 0.028349... |
777 | 777 | ['Pierre Baldi', 'Peter Sadowski', 'Zhiqin Lu'] | 1612.02734v2 | Random backpropagation (RBP) is a variant of the backpropagation algorithm
for training neural networks, where the transpose of the forward matrices are
replaced by fixed random matrices in the calculation of the weight updates. It
is remarkable both because of its effectiveness, in spite of using random
matrices to co... | Learning in the Machine: Random Backpropagation and the Deep Learning
Channel | 2,016 | http://arxiv.org/pdf/1612.02734v2 | Title Learning Machine Random Backpropagation Deep Learning Channel Summary Random backpropagation RBP variant backpropagation algorithm training neural network transpose forward matrix replaced fixed random matrix calculation weight update remarkable effectiveness spite using random matrix communicate error informatio... | [0.010330100543797016, -0.0180104598402977, -0.01898561604321003, 0.0033646111842244864, -0.006797737441956997, -0.028400084003806114, 0.017507245764136314, -0.012958348728716373, -0.032400455325841904, -0.015578096732497215, 0.00574925122782588, 0.010047715157270432, 0.03639937564730644, 0.04428138956427574, 0.0104406... |
778 | 778 | ['Klaus Greff', 'Rupesh K. Srivastava', 'Jürgen Schmidhuber'] | 1612.07771v3 | The past year saw the introduction of new architectures such as Highway
networks and Residual networks which, for the first time, enabled the training
of feedforward networks with dozens to hundreds of layers using simple gradient
descent. While depth of representation has been posited as a primary reason for
their suc... | Highway and Residual Networks learn Unrolled Iterative Estimation | 2,016 | http://arxiv.org/pdf/1612.07771v3 | Title Highway Residual Networks learn Unrolled Iterative Estimation Summary past year saw introduction new architecture Highway network Residual network first time enabled training feedforward network dozen hundred layer using simple gradient descent depth representation posited primary reason success indication archit... | [-0.025647025555372238, 0.08940903842449188, -0.008388910442590714, 0.05896079167723656, -0.006155331153422594, 0.015333243645727634, 0.01831926964223385, -0.005297107622027397, -0.03869413956999779, 0.006378860678523779, 0.011321782134473324, 0.01754014752805233, 0.0028352364897727966, -0.0004428575630299747, -0.00309... |
779 | 779 | ['Tamas Madl'] | 1612.09205v1 | Despite of the pain and limited accuracy of blood tests for early recognition
of cardiovascular disease, they dominate risk screening and triage. On the
other hand, heart rate variability is non-invasive and cheap, but not
considered accurate enough for clinical practice. Here, we tackle heart beat
interval based class... | Deep neural heart rate variability analysis | 2,016 | http://arxiv.org/pdf/1612.09205v1 | Title Deep neural heart rate variability analysis Summary Despite pain limited accuracy blood test early recognition cardiovascular disease dominate risk screening triage hand heart rate variability noninvasive cheap considered accurate enough clinical practice tackle heart beat interval based classification deep learn... | [-0.0301542766392231, -0.0011170697398483753, -0.015254385769367218, -0.007004126440733671, 0.02874237298965454, -0.004598874598741531, 0.032159462571144104, 0.0011184316826984286, -0.008572089485824108, 0.05171731859445572, 0.04561648145318031, -0.019412219524383545, 0.03170312941074371, 0.07260346412658691, -0.014522... |
780 | 780 | ['Jianlin Cheng'] | 0704.1028v1 | Ordinal regression is an important type of learning, which has properties of
both classification and regression. Here we describe a simple and effective
approach to adapt a traditional neural network to learn ordinal categories. Our
approach is a generalization of the perceptron method for ordinal regression.
On severa... | A neural network approach to ordinal regression | 2,007 | http://arxiv.org/pdf/0704.1028v1 | Title neural network approach ordinal regression Summary Ordinal regression important type learning property classification regression describe simple effective approach adapt traditional neural network learn ordinal category approach generalization perceptron method ordinal regression several benchmark datasets method... | [1.9890934709110297e-05, 0.08679355680942535, -0.03430856764316559, -0.006286998745054007, 0.024378515779972076, -0.0016859039897099137, 0.03369252011179924, 0.021915830671787262, -0.0006870360812172294, -0.01151077076792717, 0.046184342354536057, 0.02408537268638611, 0.009612297639250755, 0.04741339012980461, 0.010345... |
781 | 781 | ['Keyvan Yahya', 'Pouyan Rafiei Fard'] | 1007.0546v4 | Although the Music Sight Reading process has been studied from the cognitive
psychology view points, but the computational learning methods like the
Reinforcement Learning have not yet been used to modeling of such processes. In
this paper, with regards to essential properties of our specific problem, we
consider the v... | Computational Model of Music Sight Reading: A Reinforcement Learning
Approach | 2,010 | http://arxiv.org/pdf/1007.0546v4 | Title Computational Model Music Sight Reading Reinforcement Learning Approach Summary Although Music Sight Reading process studied cognitive psychology view point computational learning method like Reinforcement Learning yet used modeling process paper regard essential property specific problem consider value function ... | [-0.015092303976416588, -0.036629535257816315, -0.02216220460832119, 0.012726135551929474, -0.027104927226901054, -0.028467558324337006, 0.0231417715549469, 0.02002018131315708, -0.007233802694827318, 0.039952173829078674, -0.04206623509526253, 0.05796084553003311, 0.015421158634126186, 0.03981016203761101, -0.01185084... |
782 | 782 | ['Habib Shah', 'Rozaida Ghazali', 'Nazri Mohd Nawi'] | 1112.4628v1 | Nowadays, computer scientists have shown the interest in the study of social
insect's behaviour in neural networks area for solving different combinatorial
and statistical problems. Chief among these is the Artificial Bee Colony (ABC)
algorithm. This paper investigates the use of ABC algorithm that simulates the
intell... | Using Artificial Bee Colony Algorithm for MLP Training on Earthquake
Time Series Data Prediction | 2,011 | http://arxiv.org/pdf/1112.4628v1 | Title Using Artificial Bee Colony Algorithm MLP Training Earthquake Time Series Data Prediction Summary Nowadays computer scientist shown interest study social insect behaviour neural network area solving different combinatorial statistical problem Chief among Artificial Bee Colony ABC algorithm paper investigates use ... | [-0.022705523297190666, 0.0008704561623744667, -0.029974015429615974, -0.005475393030792475, -0.026523591950535774, -0.024368036538362503, -0.022687533870339394, -0.02391619049012661, 0.04949353635311127, 0.0014225913910195231, 0.03828252851963043, 0.03995503857731819, -0.01710430718958378, -0.003329209517687559, 0.027... |
783 | 783 | ['Guanjiao Ren', 'Weihai Chen', 'Sakyasingha Dasgupta', 'Christoph Kolodziejski', 'Florentin Wörgötter', 'Poramate Manoonpong'] | 1407.3269v1 | An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller ha... | Multiple chaotic central pattern generators with learning for legged
locomotion and malfunction compensation | 2,014 | http://arxiv.org/pdf/1407.3269v1 | Title Multiple chaotic central pattern generator learning legged locomotion malfunction compensation Summary originally chaotic system controlled various periodic dynamic implemented legged robot locomotion control central pattern generator CPG sophisticated gait pattern arise robot perform various walking behavior How... | [0.011876724660396576, -0.0229293555021286, -0.029470782727003098, -0.01031036488711834, 0.024149449542164803, 0.01428877841681242, -0.00956327747553587, -0.004203005228191614, -0.06127917021512985, -0.006363043561577797, -0.040662676095962524, -0.04354863986372948, -0.028669357299804688, -0.008590728044509888, 0.04298... |
784 | 784 | ['Christopher Clark', 'Amos Storkey'] | 1412.3409v2 | Mastering the game of Go has remained a long standing challenge to the field
of AI. Modern computer Go systems rely on processing millions of possible
future positions to play well, but intuitively a stronger and more 'humanlike'
way to play the game would be to rely on pattern recognition abilities rather
then brute f... | Teaching Deep Convolutional Neural Networks to Play Go | 2,014 | http://arxiv.org/pdf/1412.3409v2 | Title Teaching Deep Convolutional Neural Networks Play Go Summary Mastering game Go remained long standing challenge field AI Modern computer Go system rely processing million possible future position play well intuitively stronger humanlike way play game would rely pattern recognition ability rather brute force comput... | [0.006599697284400463, 0.04295464977622032, -0.040187060832977295, 0.0573764406144619, 0.023335492238402367, -0.04744700714945793, 0.028163326904177666, -0.0036823363043367863, -0.064762644469738, -0.00047546590212732553, -0.02353096939623356, 0.016629284247756004, -0.0037019795272499323, 0.0871199443936348, 0.05315583... |
785 | 785 | ['Kratarth Goel', 'Raunaq Vohra', 'J. K. Sahoo'] | 1412.7927v1 | In this paper, we propose a generic technique to model temporal dependencies
and sequences using a combination of a recurrent neural network and a Deep
Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state
of the RNN that allows it to provide temporal information and a multi-layer DBN
that help... | Polyphonic Music Generation by Modeling Temporal Dependencies Using a
RNN-DBN | 2,014 | http://arxiv.org/pdf/1412.7927v1 | Title Polyphonic Music Generation Modeling Temporal Dependencies Using RNNDBN Summary paper propose generic technique model temporal dependency sequence using combination recurrent neural network Deep Belief Network technique RNNDBN amalgamation memory state RNN allows provide temporal information multilayer DBN help h... | [-0.012904885224997997, 0.0404168963432312, -0.0016407378716394305, 0.03014226071536541, -0.04600048065185547, -0.043499480932950974, 0.03317447751760483, -0.027902347967028618, -0.029836535453796387, 0.01135867927223444, 0.012194815091788769, -0.028288602828979492, 0.02435942180454731, 0.03377550467848778, -0.00176268... |
786 | 786 | ['Arun Nair', 'Praveen Srinivasan', 'Sam Blackwell', 'Cagdas Alcicek', 'Rory Fearon', 'Alessandro De Maria', 'Vedavyas Panneershelvam', 'Mustafa Suleyman', 'Charles Beattie', 'Stig Petersen', 'Shane Legg', 'Volodymyr Mnih', 'Koray Kavukcuoglu', 'David Silver'] | 1507.04296v2 | We present the first massively distributed architecture for deep
reinforcement learning. This architecture uses four main components: parallel
actors that generate new behaviour; parallel learners that are trained from
stored experience; a distributed neural network to represent the value function
or behaviour policy; ... | Massively Parallel Methods for Deep Reinforcement Learning | 2,015 | http://arxiv.org/pdf/1507.04296v2 | Title Massively Parallel Methods Deep Reinforcement Learning Summary present first massively distributed architecture deep reinforcement learning architecture us four main component parallel actor generate new behaviour parallel learner trained stored experience distributed neural network represent value function behav... | [0.01950511336326599, 0.04397379606962204, -0.035955168306827545, 0.02042825147509575, 0.0004354389675427228, 0.025455953553318977, 0.01727382279932499, -0.02691436931490898, -0.06118806079030037, 0.022097965702414513, -0.06944891065359116, 0.0007897754549048841, -0.034655291587114334, 0.017840558663010597, -0.01142743... |
787 | 787 | ['Maxim Borisyak', 'Andrey Ustyuzhanin'] | 1507.07374v1 | The problem of autonomous navigation is one of the basic problems for
robotics. Although, in general, it may be challenging when an autonomous
vehicle is placed into partially observable domain. In this paper we consider
simplistic environment model and introduce a navigation algorithm based on
Learning Classifier Syst... | A genetic algorithm for autonomous navigation in partially observable
domain | 2,015 | http://arxiv.org/pdf/1507.07374v1 | Title genetic algorithm autonomous navigation partially observable domain Summary problem autonomous navigation one basic problem robotics Although general may challenging autonomous vehicle placed partially observable domain paper consider simplistic environment model introduce navigation algorithm based Learning Clas... | [0.04368383437395096, 0.008930462412536144, -0.019828274846076965, -0.0644092857837677, -0.02506658434867859, 0.009054241701960564, -0.013630657456815243, 0.013399327173829079, 0.042633675038814545, -0.007903601974248886, 0.06910116970539093, 0.034307751804590225, -0.014951305463910103, 0.02001459151506424, -0.01071042... |
788 | 788 | ['Hao Yi Ong', 'Kevin Chavez', 'Augustus Hong'] | 1508.04186v2 | We propose a distributed deep learning model to successfully learn control
policies directly from high-dimensional sensory input using reinforcement
learning. The model is based on the deep Q-network, a convolutional neural
network trained with a variant of Q-learning. Its input is raw pixels and its
output is a value ... | Distributed Deep Q-Learning | 2,015 | http://arxiv.org/pdf/1508.04186v2 | Title Distributed Deep QLearning Summary propose distributed deep learning model successfully learn control policy directly highdimensional sensory input using reinforcement learning model based deep Qnetwork convolutional neural network trained variant Qlearning input raw pixel output value function estimating future ... | [-0.016913609579205513, 0.04123321920633316, -0.0237545408308506, 0.03703076392412186, -0.009450439363718033, -0.019529834389686584, 0.03755539655685425, -0.007258306257426739, -0.03266405686736107, 0.030578289180994034, -0.04520018398761749, 0.02052122727036476, -0.03057701885700226, 0.08222495764493942, 0.02227124571... |
789 | 789 | ['Gustav Sourek', 'Vojtech Aschenbrenner', 'Filip Zelezny', 'Ondrej Kuzelka'] | 1508.05128v2 | We propose a method combining relational-logic representations with neural
network learning. A general lifted architecture, possibly reflecting some
background domain knowledge, is described through relational rules which may be
handcrafted or learned. The relational rule-set serves as a template for
unfolding possibly... | Lifted Relational Neural Networks | 2,015 | http://arxiv.org/pdf/1508.05128v2 | Title Lifted Relational Neural Networks Summary propose method combining relationallogic representation neural network learning general lifted architecture possibly reflecting background domain knowledge described relational rule may handcrafted learned relational ruleset serf template unfolding possibly deep neural ne... | [0.01719510741531849, 0.06408540159463882, -0.04534464329481125, 0.05606064200401306, -0.03824929520487785, -0.011926489882171154, -0.0183150302618742, 0.03267604112625122, -0.009359707124531269, -0.02553440071642399, -0.007110713981091976, 0.004581031855195761, -0.008772253058850765, 0.03764282166957855, -0.0138381076... |
790 | 790 | ['Matthew Lai'] | 1509.01549v2 | This report presents Giraffe, a chess engine that uses self-play to discover
all its domain-specific knowledge, with minimal hand-crafted knowledge given by
the programmer. Unlike previous attempts using machine learning only to perform
parameter-tuning on hand-crafted evaluation functions, Giraffe's learning
system al... | Giraffe: Using Deep Reinforcement Learning to Play Chess | 2,015 | http://arxiv.org/pdf/1509.01549v2 | Title Giraffe Using Deep Reinforcement Learning Play Chess Summary report present Giraffe chess engine us selfplay discover domainspecific knowledge minimal handcrafted knowledge given programmer Unlike previous attempt using machine learning perform parametertuning handcrafted evaluation function Giraffes learning sys... | [0.005608752369880676, 0.017788715660572052, -0.03463148698210716, 0.036784302443265915, -0.0033812965266406536, -0.019422052428126335, -0.005180541425943375, 0.014505052007734776, -0.04271160066127777, -0.012004423886537552, -0.0019216561922803521, 0.0015307688154280186, -0.014542761258780956, 0.07205724716186523, 0.0... |
791 | 791 | ['Kaisheng Yao', 'Geoffrey Zweig', 'Baolin Peng'] | 1510.08565v3 | In a conversation or a dialogue process, attention and intention play
intrinsic roles. This paper proposes a neural network based approach that
models the attention and intention processes. It essentially consists of three
recurrent networks. The encoder network is a word-level model representing
source side sentences.... | Attention with Intention for a Neural Network Conversation Model | 2,015 | http://arxiv.org/pdf/1510.08565v3 | Title Attention Intention Neural Network Conversation Model Summary conversation dialogue process attention intention play intrinsic role paper proposes neural network based approach model attention intention process essentially consists three recurrent network encoder network wordlevel model representing source side s... | [0.07418721914291382, 0.028101658448576927, -0.016625864431262016, 0.03261515498161316, -0.005677464418113232, -0.022973161190748215, -0.031870037317276, -0.021354587748646736, -0.006044243928045034, -0.048517849296331406, -0.03193512558937073, 0.004044013097882271, 0.012984806671738625, 0.08076771348714828, 0.00123000... |
792 | 792 | ['Matthew Hausknecht', 'Peter Stone'] | 1511.04143v4 | Recent work has shown that deep neural networks are capable of approximating
both value functions and policies in reinforcement learning domains featuring
continuous state and action spaces. However, to the best of our knowledge no
previous work has succeeded at using deep neural networks in structured
(parameterized) ... | Deep Reinforcement Learning in Parameterized Action Space | 2,015 | http://arxiv.org/pdf/1511.04143v4 | Title Deep Reinforcement Learning Parameterized Action Space Summary Recent work shown deep neural network capable approximating value function policy reinforcement learning domain featuring continuous state action space However best knowledge previous work succeeded using deep neural network structured parameterized c... | [-0.012675650417804718, 0.007990937680006027, -0.026146497577428818, -0.019109152257442474, 0.046831220388412476, -0.029667936265468597, 0.0076109240762889385, -0.06154318153858185, -0.06295673549175262, 0.019057054072618484, -0.030551210045814514, 0.007014790084213018, -0.048812828958034515, 0.08638209104537964, 0.028... |
793 | 793 | ['Sainbayar Sukhbaatar', 'Arthur Szlam', 'Gabriel Synnaeve', 'Soumith Chintala', 'Rob Fergus'] | 1511.07401v2 | This paper introduces MazeBase: an environment for simple 2D games, designed
as a sandbox for machine learning approaches to reasoning and planning. Within
it, we create 10 simple games embodying a range of algorithmic tasks (e.g.
if-then statements or set negation). A variety of neural models (fully
connected, convolu... | MazeBase: A Sandbox for Learning from Games | 2,015 | http://arxiv.org/pdf/1511.07401v2 | Title MazeBase Sandbox Learning Games Summary paper introduces MazeBase environment simple 2D game designed sandbox machine learning approach reasoning planning Within create 10 simple game embodying range algorithmic task eg ifthen statement set negation variety neural model fully connected convolutional network memor... | [0.01864539086818695, -0.02072683721780777, -0.03355177491903305, -0.014497590251266956, -0.017775719985365868, -0.008028334937989712, -0.004133883398026228, -0.010758710093796253, 0.022005965933203697, 0.02145436592400074, -0.01544943731278181, 0.06511269509792328, -0.057799044996500015, 0.05978481471538544, 0.0185393... |
794 | 794 | ['Juergen Schmidhuber'] | 1511.09249v1 | This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In 2013, our large RL recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they le... | On Learning to Think: Algorithmic Information Theory for Novel
Combinations of Reinforcement Learning Controllers and Recurrent Neural World
Models | 2,015 | http://arxiv.org/pdf/1511.09249v1 | Title Learning Think Algorithmic Information Theory Novel Combinations Reinforcement Learning Controllers Recurrent Neural World Models Summary paper address general problem reinforcement learning RL partially observable environment 2013 large RL recurrent neural network RNNs learned scratch drive simulated car highdim... | [0.040364764630794525, 0.039489295333623886, -0.017605502158403397, 0.01730210706591606, -0.053249262273311615, -0.0025828455109149218, -0.0014882119139656425, -0.010194088332355022, -0.05190899223089218, -0.013389719650149345, -0.0011533999349921942, 0.008059129118919373, -0.007386452052742243, 0.0474996343255043, 0.0... |
795 | 795 | ['Denis Steckelmacher', 'Peter Vrancx'] | 1512.05509v1 | This paper explores the performance of fitted neural Q iteration for
reinforcement learning in several partially observable environments, using
three recurrent neural network architectures: Long Short-Term Memory, Gated
Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of
several thousands ca... | An Empirical Comparison of Neural Architectures for Reinforcement
Learning in Partially Observable Environments | 2,015 | http://arxiv.org/pdf/1512.05509v1 | Title Empirical Comparison Neural Architectures Reinforcement Learning Partially Observable Environments Summary paper explores performance fitted neural Q iteration reinforcement learning several partially observable environment using three recurrent neural network architecture Long ShortTerm Memory Gated Recurrent Un... | [0.031508006155490875, 0.000662361504510045, -0.008813418447971344, -0.013668354600667953, -0.0028510328847914934, -0.019623108208179474, 0.0009291676105931401, -0.005711337551474571, -0.025212733075022697, 0.019281769171357155, -0.040371086448431015, -0.012081305496394634, -0.04073917865753174, 0.053953949362039566, 0... |
796 | 796 | ['Cristóbal Esteban', 'Oliver Staeck', 'Yinchong Yang', 'Volker Tresp'] | 1602.02685v2 | In clinical data sets we often find static information (e.g. patient gender,
blood type, etc.) combined with sequences of data that are recorded during
multiple hospital visits (e.g. medications prescribed, tests performed, etc.).
Recurrent Neural Networks (RNNs) have proven to be very successful for
modelling sequence... | Predicting Clinical Events by Combining Static and Dynamic Information
Using Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1602.02685v2 | Title Predicting Clinical Events Combining Static Dynamic Information Using Recurrent Neural Networks Summary clinical data set often find static information eg patient gender blood type etc combined sequence data recorded multiple hospital visit eg medication prescribed test performed etc Recurrent Neural Networks RNN... | [0.013261804357171059, 0.04503307119011879, -0.008894164115190506, -0.035063933581113815, -0.015218491666018963, 0.01147483941167593, -0.010656564496457577, 0.016265563666820526, -0.028623173013329506, 0.0058285086415708065, 0.04173915088176727, -0.06631138175725937, 0.039380863308906555, 0.0908694937825203, 0.00199571... |
797 | 797 | ['Tim Salimans', 'Diederik P. Kingma'] | 1602.07868v3 | We present weight normalization: a reparameterization of the weight vectors
in a neural network that decouples the length of those weight vectors from
their direction. By reparameterizing the weights in this way we improve the
conditioning of the optimization problem and we speed up convergence of
stochastic gradient d... | Weight Normalization: A Simple Reparameterization to Accelerate Training
of Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1602.07868v3 | Title Weight Normalization Simple Reparameterization Accelerate Training Deep Neural Networks Summary present weight normalization reparameterization weight vector neural network decouples length weight vector direction reparameterizing weight way improve conditioning optimization problem speed convergence stochastic g... | [-0.020008239895105362, 0.054349448531866074, -0.012255227193236351, 0.04392442852258682, 0.02404322847723961, -0.004250003956258297, 0.035763900727033615, -0.007881603203713894, -0.043586261570453644, 0.043948639184236526, -0.025443583726882935, 0.025280063971877098, 0.0058187455870211124, 0.03038506582379341, -0.0004... |
798 | 798 | ['Felix Leibfried', 'Daniel Alexander Braun'] | 1602.08332v2 | Bounded rational decision-makers transform sensory input into motor output
under limited computational resources. Mathematically, such decision-makers can
be modeled as information-theoretic channels with limited transmission rate.
Here, we apply this formalism for the first time to multilayer feedforward
neural networ... | Bounded Rational Decision-Making in Feedforward Neural Networks | 2,016 | http://arxiv.org/pdf/1602.08332v2 | Title Bounded Rational DecisionMaking Feedforward Neural Networks Summary Bounded rational decisionmakers transform sensory input motor output limited computational resource Mathematically decisionmakers modeled informationtheoretic channel limited transmission rate apply formalism first time multilayer feedforward neu... | [-0.014995863661170006, 0.05422063544392586, -0.01628904789686203, -0.010860799811780453, 0.011774145998060703, -0.008793439716100693, 0.008602979592978954, 0.04339129850268364, 5.646177669405006e-05, 0.008354364894330502, -0.03066168911755085, 0.03283068910241127, 0.016480116173624992, 0.06421598047018051, 0.051957350... |
799 | 799 | ['Greg Yang'] | 1602.08671v3 | Following the recent trend in explicit neural memory structures, we present a
new design of an external memory, wherein memories are stored in an Euclidean
key space $\mathbb R^n$. An LSTM controller performs read and write via
specialized read and write heads. It can move a head by either providing a new
address in th... | Lie Access Neural Turing Machine | 2,016 | http://arxiv.org/pdf/1602.08671v3 | Title Lie Access Neural Turing Machine Summary Following recent trend explicit neural memory structure present new design external memory wherein memory stored Euclidean key space mathbb Rn LSTM controller performs read write via specialized read write head move head either providing new address key space aka random ac... | [0.023067500442266464, -0.03536568954586983, -0.0033056761603802443, 0.035869795829057693, -0.009078841656446457, -0.01579129882156849, 0.026542559266090393, -0.016675584018230438, 0.023853767663240433, -0.026093723252415657, -0.04919576272368431, -0.05345131456851959, -0.006935525219887495, 0.04885188490152359, 0.0583... |
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