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0 | It often difficult predict optimal neural network size particular application Constructive destructive methods add subtract neurons layers connections etc might offer solution problem We prove one method Recurrent Cascade Correlation due topology fundamental limitations representation thus learning capabilities It represent monotone ie sigmoid hardthreshold activation functions certain finite state automata We give preliminary approach get around limitations devising simple constructive training method adds neurons training still preserving powerful fullyrecurrent structure We illustrate approach simulations learn many examples regular grammars |
2 | This paper presents recent developments toward formalism combines useful properties logic probabilities Like logic formalism admits qualitative sentences provides symbolic machinery deriving deductively closed beliefs like probability permits us express ifthen rules different levels firmness retract beliefs response changing observations Rules interpreted orderofmagnitude approximations conditional probabilities impose constraints rankings worlds Inferences supported unique priority ordering rules syntactically derived knowledge base This ordering accounts rule interactions respects specificity considerations facilitates construction coherent states beliefs Practical algorithms developed analyzed testing consistency computing rule ordering answering queries Imprecise observations incorporated using qualitative versions Jeffreys Rule Bayesian updating result coherent belief revision embodied naturally tractably Finally causal rules interpreted imposing Markovian conditions constrain world rankings reflect modularity causal organizations These constraints shown facilitate reasoning causal projections explanations actions change |
5 | We present framework taskdriven knowledge acquisition development design support systems Different types knowledge enter knowledge base design support system defined illustrated formal knowledge acquisition vantage point Special emphasis placed taskstructure used guide acquisition application knowledge Starting knowledge planning steps design augmenting problemsolving knowledge supports design formal integrated model knowledge design constructed Based notion knowledge acquisition incremental process give account possibilities problem solving depending knowledge disposal system Finally depict different kinds knowledge interact design support system This research supported German Ministry Research Technology BMFT within joint project FABEL contract IW Project partners FABEL German National Research Center Computer Science GMD Sankt Augustin BSR Consulting GmbH Munchen Technical University Dresden HTWK Leipzig University Freiburg University Karlsruhe |
4 | An exact model simple genetic algorithm developed permutation based representations Permutation based representations used scheduling problems combinatorial problems Traveling Salesman Problem A remapping function developed remap model permutations search space The mixing matrices various permutation based operators also developed |
1 | In paper bring techniques operations research bear problem choosing optimal actions partially observable stochastic domains We begin introducing theory Markov decision processes mdps partially observable mdps pomdps We outline novel algorithm solving pomdps line show cases finitememory controller extracted solution pomdp We conclude discussion approach relates previous work complexity finding exact solutions pomdps possibilities finding approximate solutions |
0 | This study deals alltoall broadcast CNS We determine lower bound run time present algorithm meeting bound Since study points bottleneck network interface also analyze performance alternative interface designs Our analyses based run time model network |
3 | Evolutionary tree reconstruction important step many biological research problems yet extremely difficult variety computational statistical scientific reasons In particular reconstruction large trees containing significant amounts divergence especially challenging We present paper new tree reconstruction method call DiskCovering Method used recover accurate estimations evolutionary tree otherwise intractable datasets DCM obtains decomposition input dataset small overlapping sets closely related taxa reconstructs trees subsets using base phylogenetic method choice combines subtrees one tree entire set taxa Because subproblems analyzed DCM smaller computationally expensive methods maximum likelihood estimation used without incurring much cost At time taxa within subset closely related even simple methods neighborjoining much likely highly accurate The result DCMboosted methods typically faster accurate compared naive use method In paper describe basic ideas techniques DCM demonstrate advantages DCM experimentally simulating sequence evolution variety trees |
6 | Many ILP systems GOLEM FOIL MIS take advantage user supplied metaknowledge restrict hypothesis space This metaknowledge form type information arguments predicate learned information whether certain argument predicate functionally dependent arguments supplied mode information This meta knowledge explicitly supplied ILP system addition data The present paper argues many cases meta knowledge extracted directly raw data Three algorithms presented learn type mode symmetric metaknowledge data These algorithms incorporated existing ILP systems form preprocessor obviates need user explicitly provide information In many cases algorithms extract meta knowledge user either unaware information used ILP system restrict hypothesis space |
0 | Most known learning algorithms dynamic neural networks nonstationary environments need global computations perform credit assignment These algorithms either local time local space Those algorithms local time space usually deal sensibly hidden units In contrast far judge learning rules biological systems many hidden units local space time In paper propose parallel online learning algorithm performs local computations yet still designed deal hidden units units whose past activations hidden time The approach inspired Hollands idea bucket brigade classifier systems transformed run neural network fixed topology The result feedforward recurrent neural dissipative system consuming weightsubstance permanently trying distribute substance onto connections appropriate way Simple experiments demonstrating feasability algorithm reported |
5 | Tech Report GITCOGSCI Abstract This paper identifies goal handling processes begin account kind processes involved invention We identify new kinds goals special properties mechanisms processing goals well means integrating opportunism deliberation social interaction goalplan processes We focus invention goals address significant enterprises associated inventor Invention goals represent seed goals expert around whole knowledge expert gets reorganized grows less opportunistically Invention goals reflect idiosyncrasy thematic goals among experts They constantly increase sensitivity individuals particular events might contribute satisfaction Our exploration based welldocumented example invention telephone Alexander Graham Bell We propose mechanisms explain Bells early thematic goals gave rise new goals invent multiple telegraph telephone new goals interacted opportunistically Finally describe computational model ALEC accounts role goals invention |
0 | A novel method regression recently proposed V Vapnik et al The technique called Support Vector Machine SVM well founded mathematical point view seems provide new insight function approximation We implemented SVM tested data base chaotic time series used compare performances different approximation techniques including polynomial rational approximation local polynomial techniques Radial Basis Functions Neural Networks The SVM performs better approaches presented We also study particular time series variability performance respect free parameters SVM |
4 | An investigation dynamics Genetic Programming applied chaotic time series prediction reported An interesting characteristic adaptive search techniques ability perform well many problem domains failing others Because Genetic Programmings flexible tree structure particular problem represented myriad forms These representations variegated effects search performance Therefore aspect fundamental engineering significance find representation acted upon Genetic Programming operators optimizes search performance We discover case chaotic time series prediction representation commonly used domain yield optimal solutions Instead find population converges onto one accurately replicating tree trees explored To correct premature convergence make simple modification crossover operator In paper review previous work GP time series prediction pointing anomalous result related overlearning report improvement effected modified crossover operator |
0 | We introduce large family Boltzmann machines trained using standard gradient descent The networks one layers hidden units treelike connectivity We show implement supervised learning algorithm Boltzmann machines exactly without resort simulated meanfield annealing The stochastic averages yield gradients weight space computed technique decimation We present results problems N bit parity detection hidden symmetries |
0 | We propose method decreasing computational complexity selforganising maps The method uses partitioning neurons disjoint clusters Teaching neurons occurs clusterbasis instead neuronbasis For teaching Nneuron network N samples computational complexity decreases ON N ON log N Furthermore introduce measure amount order selforganising map show introduced algorithm behaves well original algorithm |
4 | Traditional machine vision assumes vision system recovers complete labeled description world Marr Recently several researchers criticized model proposed alternative model considers perception distributed collection taskspecific taskdriven visual routines Aloimonos Ullman Some researchers argued natural living systems visual routines product natural selection Ramachandran So far researchers handcoded taskspecific visual routines actual implementations eg Chapman In paper propose alternative approach visual routines simple tasks evolved using artificial evolution approach We present results series runs actual camera images simple routines evolved using Genetic Programming techniques Koza The results obtained promising evolved routines able correctly classify images better best algorithm able write hand |
3 | We examine questions optimality domination repeated stage games one players may draw strategies perhaps different computationally bounded sets We also consider optimality domination bounded convergence rates infinite payoff We develop notion grace period handle problem vengeful strategies |
6 | This paper discusses three techniques useful relaxing constraints imposed control flow parallelism control dependence analysis executing multiple flows control simultaneously speculative execution We evaluate techniques using trace simulations find limits parallelism machines employ different combinations techniques We three major results First local regions code limited parallelism control dependence analysis useful extracting global parallelism different parts program Second superscalar processor fundamentally limited execute independent regions code concurrently Higher performance obtained machines multiprocessors dataflow machines simultaneously follow multiple flows control Finally without speculative execution allow instructions execute control dependences resolved modest amounts parallelism obtained programs complex control flow |
3 | A quantitative practical Bayesian framework described learning mappings feedforward networks The framework makes possible objective comparisons solutions using alternative network architectures objective stopping rules network pruning growing procedures objective choice magnitude type weight decay terms additive regularisers penalising large weights etc measure effective number welldetermined parameters model quantified estimates error bars network parameters network output objective comparisons alternative learning interpolation models splines radial basis functions The Bayesian evidence automatically embodies Occams razor penalising overflexible overcomplex models The Bayesian approach helps detect poor underlying assumptions learning models For learning models well matched problem good correlation generalisation ability This paper makes use Bayesian framework regularisation model comparison described companion paper Bayesian interpolation MacKay This framework due Gull Skilling Gull Bayesian evidence obtained |
0 | Although considerable interest shown language inference automata induction using recurrent neural networks success models mostly limited regular languages We previously demonstrated Neural Network Pushdown Automaton NNPDA model capable learning deterministic contextfree languages eg n b n parenthesis languages examples However learning task computationally intensive In paper discuss ways priori knowledge task data could used efficient learning We also observe knowledge often experimental prerequisite learning nontrivial languages eg n b n cb |
3 | Theory revision integrates inductive learning background knowledge combining training examples coarse domain theory produce accurate theory There two challenges theory revision theoryguided systems face First representation language appropriate initial theory may inappropriate improved theory While original representation may concisely express initial theory accurate theory forced use representation may bulky cumbersome difficult reach Second theory structure suitable coarse domain theory may insufficient finetuned theory Systems produce small local changes theory limited value accomplishing complex structural alterations may required Consequently advanced theoryguided learning systems require flexible representation flexible structure An analysis various theory revision systems theoryguided learning systems reveals specific strengths weaknesses terms two desired properties Designed capture underlying qualities system new system uses theoryguided constructive induction Experiments three domains show improvement previous theoryguided systems This leads study behavior limitations potential theoryguided constructive induction |
3 | We consider standard problem learning concept random examples Here learning curve defined expected error learners hypotheses function training sample size Haussler Littlestone Warmuth shown distribution free setting smallest expected error learner achieve worst case concept class C converges rationally zero error ie fit training sample size However recently Cohn Tesauro demonstrated exponential convergence often observed experimental settings ie average error decreasing e fit By addressing simple nonuniformity original analysis paper shows dichotomy rational exponential worst case learning curves recovered distribution free theory These results support experimental findings Cohn Tesauro finite concept classes consistent learner achieves exponential convergence even worst case continuous concept classes learner exhibit subrational convergence every target concept domain distribution A precise boundary rational exponential convergence drawn simple concept chains Here show somewhere dense chains always force rational convergence worst case exponential convergence always achieved nowhere dense chains |
0 | We consider recurrent analog neural nets output gate subject Gaussian noise common noise distribution nonzero large set We show many regular languages recognized networks type give precise characterization languages recognized This result implies severe constraints possibilities constructing recurrent analog neural nets robust realistic types analog noise On hand present method constructing feedforward analog neural nets robust regard analog noise type |
0 | We propose assess relevance theories synaptic modification models feature extraction human vision using masks derived synaptic weight patterns occlude parts stimulus images psychophysical experiments In experiment reported found mask derived principal component analysis object images effective reducing generalization performance human subjects mask derived another method feature extraction BCM based higherorder statistics images |
5 | This paper reports project document retrieval industrial setting The objective provide tool helps finding documents related given query answers Frequently Asked Questions databases A CBR approach used develop running prototypical system currently practical evaluation |
2 | This paper analyses recently suggested particle approach filtering time series We suggest algorithm robust outliers two reasons design simulators use discrete support represent sequentially updating prior distribution Both problems tackled paper We believe largely solved first problem reduced order magnitude second In addition introduce idea stratification particle filter allows us perform online Bayesian calculations parameters index models maximum likelihood estimation The new methods illustrated using stochastic volatility model time series model angles |
0 | Results reported application tools synthesizing optimizing analyzing neural networks ECG Patient Monitoring task A neural network synthesized rulebased classifier optimized set normal abnormal heartbeats The classification error rate separate larger test set reduced factor Sensitivity analysis synthesized optimized networks revealed informative differences Analysis weights unit activations optimized network enabled reduction size network factor without loss accuracy |
2 | Alan E Gelfand Professor Department Statistics University Connecticut Storrs CT Sujit K Sahu Lecturer School Mathematics University Wales Cardiff CF YH UK The research first author supported part NSF grant DMS second author supported part EPSRC grant UK The authors thank Brad Carlin Kate Cowles Gareth Roberts anonymous referee valuable comments |
0 | Human visual systems maintain stable internal representation scene even though image retina constantly changing eye movements Such stabilization theoretically effected dynamic shifts receptive field RF neurons visual system This paper examines neural circuit learn generate shifts The shifts controlled eye position signals compensate movement retinal image caused eye movements The development neural shifter circuit Olshausen Anderson Van Essen modeled using triadic connections These connections gated signals indicate direction gaze eye position signals In simulations neural model exposed sequences stimuli paired appropriate eye position signals The initially |
6 | Systems interacting realworld data must address issues raised possible presence errors observations makes In paper first present framework discussing imperfect data resulting problems may cause We distinguish two categories errors data random errors noise systematic errors examine relationship task describing observations way also useful helping future problemsolving learning tasks Secondly proceed examine techniques currently used AI research recognising errors |
3 | In regression context boosting bagging techniques build committee regressors may superior single regressor We use regression trees fundamental building blocks bagging committee machines boosting committee machines Performance analyzed three nonlinear functions Boston housing database In cases boosting least equivalent cases better bagging terms prediction error |
5 | An important aspect creative design concept emergence Though emergence important mechanism either well understood limited domain shapes This deficiency compensated considering definitions emergent behaviour Artificial Life ALife research community With new insights proposed computational technique called evolving representations design genes extended emergent behaviour We demonstrate emergent behaviour coevolutionary model design This coevolutionary approach design allows solution space structure space evolve response problem space behaviour space Since behaviour space active participant behaviour may emerge new structures end design process This paper hypothesizes emergent behaviour identified using technique The floor plan example Gero Schnier extended demonstrate behaviour emerge coevolutionary design process |
0 | A novel approach object recognition scene analysis based neural network representation visual schemas described Given input scene VISOR system focuses attention successively component schema representations cooperate compete match inputs The schema hierarchy learned examples unsupervised adaptation reinforcement learning VISOR learns objects important others identifying scene importance spatial relations varies depending scene As inputs differ increasingly schemas VISORs recognition process remarkably robust automatically generates measure confidence analysis |
4 | In paper develop empirical methodology studying behavior evolutionary algorithms based problem generators We describe three generators used study effects epistasis performance EAs Finally illustrate use ideas preliminary exploration effects epistasis simple GAs |
1 | We present new algorithm solving Markov decision problems extends modified policy iteration algorithm Puterman Shin two important ways The new algorithm asynchronous allows values states updated arbitrary order need consider actions state updating policy The new algorithm converges general initial conditions required modified policy iteration Specifically set initial policyvalue function pairs algorithm guarantees convergence strict superset set modified policy iteration converges This generalization obtained making simple easily implementable change policy evaluation operator used updating value function Both asynchronous nature algorithm convergence general conditions expand range problems algorithm applied |
6 | Many techniques developed learning rules relationships automatically diverse data sets simplify often tedious errorprone process acquiring knowledge empirical data While techniques plausible theoretically wellfounded perform well less artificial test data sets depend ability make sense realworld data This paper describes project applying range machine learning strategies problems agriculture horticulture We briefly survey techniques emerging machine learning research describe software workbench experimenting variety techniques realworld data sets describe case study dairy herd management culling rules inferred mediumsized database herd information |
4 | Traditional evolutionary optimization algorithms assume static evaluation function according solutions evolved Incremental evolution approach dynamic evaluation function scaled time order improve performance evolutionary optimization In paper present empirical results demonstrate effectiveness approach genetic programming Using two domains twoagent pursuitevasion game Tracker trailfollowing task demonstrate incremental evolution successful applied near beginning evolutionary run We also show incremental evolution successful intermediate evaluation functions difficult target evaluation function well easier target function |
2 | We study problem estimating log spectrum stationary Gaussian time series thresholding empirical wavelet coefficients We propose use thresholds jn depending sample size n wavelet basis resolution level j At fine resolution levels j propose The purpose thresholding level make reconstructed logspectrum nearly noisefree possible In addition pleasant visual point view noisefree character leads attractive theoretical properties wide range smoothness assumptions Previous proposals set much smaller thresholds enjoy properties jn ff j log n |
3 | We address problem finding parameter settings result optimal performance given learning algorithm using particular dataset training data We describe wrapper method considering determination best parameters discrete function optimization problem The method uses bestfirst search crossvalidation wrap around basic induction algorithm search explores space parameter values running basic algorithm many times training holdout sets produced crossvalidation get estimate expected error parameter setting Thus final selected parameter settings tuned specific induction algorithm dataset studied We report experiments method datasets selected UCI StatLog collections using C basic induction algorithm At confidence level method improves performance C nine domains degrades performance one statistically indistinguishable C rest On sample datasets used comparison method yields average relative decrease error rate We expect see similar performance improvements using method machine learning al gorithms |
2 | The problem belief changehow agent revise beliefs upon learning new informationhas active area research philosophy artificial intelligence Many approaches belief change proposed literature Our goal introduce yet another approach examine carefully rationale underlying approaches already taken literature highlight view methodological problems literature The main message study belief change carefully must quite explicit ontology scenario underlying belief change process This something missing previous work focus postulates Our analysis shows must pay particular attention two issues often taken granted The first model agents epistemic state Do use set beliefs richer structure ordering worlds And use set beliefs language beliefs expressed The second status observations Are observations known true believed In latter case firm belief For example argue even postulates called beyond controversy unreasonable agents beliefs include beliefs epistemic state well external world Issues status observations arise particularly consider iterated belief revision must confront possibility revising |
5 | Inferences conversational casebased reasoning CCBR approach embedded CBR Content Navigator line products susceptible bias case scoring algorithm In particular shorter cases tend given higher scores assuming factors held constant This report summarizes investigation mediating bias We introduce approach eliminating bias evaluate affects retrieval performance six case libraries We also suggest explanations results note limitations study |
0 | A widely held idea regarding information processing brain cellassembly hypothesis suggested Hebb According hypothesis basic unit information processing brain assembly cells act briefly closed system response specific stimulus This work presents novel method characterizing supposed activity using Hidden Markov Model This model able reveal underlying cortical network activity behavioral processes In study process hand simultaneous activity several cells recorded frontal cortex behaving monkeys Using model able identify behavioral mode animal directly identify corresponding collective network activity Furthermore segmentation data discrete states also provides direct evidence state dependency shorttime correlation functions pair cells Thus crosscorrelation depends network state activity local connectivity alone |
2 | This paper multidisciplinary review empirical statistical learning graphical model perspective Wellknown examples graphical models include Bayesian networks directed graphs representing Markov chain undirected networks representing Markov field These graphical models extended model data analysis empirical learning using notation plates Graphical operations simplifying manipulating problem provided including decomposition differentiation manipulation probability models exponential family Two standard algorithm schemas learning reviewed graphical framework Gibbs sampling expectation maximization algorithm Using operations schemas popular algorithms synthesized graphical specification This includes versions linear regression techniques feedforward networks learning Gaussian discrete Bayesian networks data The paper concludes sketching implications data analysis summarizing popular algorithms fall within framework presented |
0 | Intermediate higher vision processes require selection subset available sensory information processing Usually selection implemented form spatially circumscribed region visual field socalled focus attention scans visual scene dependent input attentional state subject We present model control focus attention primates based saliency map This mechanism expected model functionality biological vision also essential understanding complex scenes machine vision |
6 | Most KDD applications consider databases static objects however many databases inherently temporal ie store evolution object passage time Thus regularities dynamics databases discovered current state might depend way previous states To end preprocessing data needed aimed extracting relationships intimately connected temporal nature data make available discovery algorithm The predicate logic language ILP methods together recent advances ef ficiency makes adequate task |
1 | An accurate simulation heating coil used compare performance PI controller neural network trained predict steadystate output PI controller neural network trained minimize nstep ahead error coil output set point reinforcement learning agent trained minimize sum squared error time Although PI controller works well task neural networks result improved performance |
3 | The coverage learning algorithm number concepts learned algorithm samples given size This paper asks whether good learning algorithms designed maximizing coverage The paper extends previous upper bound coverage Boolean concept learning algorithm describes two algorithmsMultiBalls LargeBallwhose coverage approaches upper bound Experimental measurement coverage ID FRINGE algorithms shows coverage far bound Further analysis LargeBall shows although learns many concepts seem interesting concepts Hence coverage maximization alone appear yield practicallyuseful learning algorithms The paper concludes definition coverage within bias suggests way coverage maximization could applied strengthen weak preference biases |
2 | Survival analysis concerned finding models predict survival patients assess efficacy clinical treatment A key part modelbuilding process selection predictor variables It standard use stepwise procedure guided series significance tests select single model make inference conditionally selected model However ignores model uncertainty substantial We review standard Bayesian model averaging solution problem extend survival analysis introducing partial Bayes factors Cox proportional hazards model In two examples taking account model uncertainty enhances predictive performance extent could clinically useful |
3 | Support Vector Learning Machines SVM finding application pattern recognition regression estimation operator inversion illposed problems Against general backdrop methods improving generalization performance improving speed test phase SVMs increasing interest In paper combine two techniques pattern recognition problem The method improving generalization performance virtual support vector method incorporating known invariances problem This method achieves drop error rate NIST test digit images The method improving speed reduced set method approximating support vector decision surface We apply method achieve factor fifty speedup test phase virtual support vector machine The combined approach yields machine times faster original machine better generalization performance achieving error The virtual support vector method applicable SVM problem known invariances The reduced set method applicable support vector machine |
0 | The DMP Dynamic Multilayer Perceptron network training method based upon divide conquer approach builds networks form binary trees dynamically allocating nodes layers needed This paper introduces DMP method compares preformance DMP using standard delta rule training method training individual nodes performance DMP using genetic algorithm training While basic model require use genetic algorithm training individual nodes results show convergence properties DMP enhanced use genetic algorithm appropriate fitness function |
5 | This research supported National Science Foundation Fellowship awarded Dario Salvucci Office Naval Research grant N awarded John Anderson The views conclusions contained document authors interpreted representing official policies either expressed implied National Science Foundation Office Naval Research United States government |
0 | When delays set A ffi fj k j kg P jffi depends The estimated probabilities become quite noisy number elements set A B small For reason estimate standard deviation P jffi Notice estimate empirical average binomial variable either given couple satisfied conditions ffi The standard deviation estimated easily Generally speaking P jffi increases laxer output test ffi approaches stricter input condition Let us define P maximum ffi P jffi P max ffigt P jffi The dependability index defined P represents much data passes continuity test input information available This dependability index measures much remaining continuity information associated involving input This index averaged respect probability P P It clear therefore average positive quantities Furthermore system deterministic dependability zero certain number inputs sum averages saturates If system also noisefree sum For greater embedding dimension refers results obtained using method Statistical variable selection Statistical variable selection feature selection encompasses number techniques aimed choosing relevant subset input variables regression classification problem As rest document limit considerations related regression problem even though methods discussed apply classification well Variable selection seen part data analysis problem selection discard variable tells us relevance associated measurement modelled system In general setting purely combinatorial problem given V possible variables V possible subsets including empty set full set variables Given performance measure prediction error optimal scheme test subset choose one gives best performance It easy see extensive scheme viable number variables rather low Identifying V models variables requires much computation A number techniques devised overcome combinatorial limit Some use iterative locally optimal technique construct estimate relevant subset number steps We refer stepwise selection methods con fused stepwise regression subset methods address In forward selection start empty set variables At step select candidate variable using selection criteria check whether variable added set iterate given stop condition reached On contrary backward elimination methods start full set input variables At step least significant variable selected according selection criteria If variable irrelevant removed process iterated stop condition reached It easy devise examples inclusion variable causes previously included variable become irrelevant It thus seems appropriate consider running backward elimination time new variable added forward selection This combination ap proaches known stepwise regression linear regression con |
4 | In many optimization problems structure solutions reflects complex relationships different input parameters For example experience may tell us certain parameters closely related explored independently Similarly experience may establish subset parameters must take particular values Any search cost landscape take advantage relationships We present MIMIC framework analyze global structure optimization landscape A novel efficient algorithm estimation structure derived We use knowledge structure guide randomized search solution space turn refine estimate structure Our technique obtains significant speed gains randomized optimization procedures |
0 | We present fast algorithm nonlinear dimension reduction The algorithm builds local linear model data merging PCA clustering based new distortion measure Experiments speech image data indicate local linear algorithm produces encodings lower distortion built five layer autoassociative networks The local linear algorithm also order magnitude faster train |
5 | In paper present probabilistic formalization instancebased learning approach In Bayesian framework moving construction explicit hypothesis datadriven instancebased learning approach equivalent averaging possibly infinitely many individual models The general Bayesian instancebased learning framework described paper applied set assumptions defining parametric model family discrete prediction task number simultaneously predicted attributes small includes example classification tasks prevalent machine learning literature To illustrate use suggested general framework practice show approach implemented special case strong independence assumptions underlying called Naive Bayes classifier The resulting Bayesian instancebased classifier validated empirically public domain data sets results compared performance traditional Naive Bayes classifier The results suggest Bayesian instancebased learning approach yields better results traditional Naive Bayes classifier especially cases amount training data small |
2 | c flMIT Media Lab Perceptual Computing Learning Common Sense Technical Report nov Abstract We present algorithms coupling training hidden Markov models HMMs model interacting processes demonstrate superiority conventional HMMs vision task classifying twohanded actions HMMs perhaps successful framework perceptual computing modeling classifying dynamic behaviors popular offer dynamic time warping training algorithm clear Bayesian semantics However Markovian framework makes strong restrictive assumptions system generating signalthat single process small number states extremely limited state memory The singleprocess model often inappropriate vision speech applications resulting low ceilings model performance Coupled HMMs provide efficient way resolve many problems offer superior training speeds model likelihoods robustness initial conditions |
5 | In article present casebased approach flexible query answering systems two different application areas The ExperienceBook supports technical diagnosis field system administration In FAllQ project use CBR system document retrieval industrial setting The objective systems manage knowledge stored less structured documents The internal case memory implemented Case Retrieval Net This allows handle large case bases efficient retrieval process In order provide multi user access chose client server model combined web interface |
0 | We present two additions hierarchical mixture experts HME architecture We view HME tree structured classifier Firstly applying likelihood splitting criteria expert HME grow tree adaptively training Secondly considering probable path tree may prune branches away either temporarily permanently become redundant We demonstrate results growing pruning algorithms show significant speed ups efficient use parameters conventional algorithms discriminating two interlocking spirals classifying bit parity patterns |
4 | This paper presents fast algorithm provides optimal near optimal solutions minimum perimeter problem rectangular grid The minimum perimeter problem partition grid size M N P equal area regions minimizing total perimeter regions The approach taken divide grid stripes filled completely integer number regions This striping method gives rise knapsack integer program efficiently solved existing codes The solution knapsack problem used generate grid region assignments An implementation algorithm partitioned grid regions provably optimal solution less one second With sufficient memory hold M N grid array extremely large minimum perimeter problems solved easily |
2 | Predictive inference seen process determining predictive distribution discrete variable given data set training examples values problem domain variables We consider three approaches computing predictive distribution assume joint probability distribution variables belongs set distributions determined set parametric models In simplest case predictive distribution computed using model maximum posteriori MAP posterior probability In evidence approach predictive distribution obtained averaging individual models model family In third case define predictive distribution using Rissanens new definition stochastic complexity Our experiments performed family Naive Bayes models suggest using data available stochastic complexity approach produces accurate predictions logscore sense However amount available training data decreased evidence approach clearly outperforms two approaches The MAP predictive distribution clearly inferior logscore sense two sophisticated approaches score MAP approach may still cases produce best results |
6 | We present Resource Spackling framework integrating register allocation instruction scheduling based Measure Reduce paradigm The technique measures resource requirements program uses measurements distribute code better resource allocation The technique applicable allocation different types resources A programs resource requirements register functional unit resources first measured using unified representation These measurements used find areas resources either utilized called resource holes excessive sets respectively Conditions determined increasing resource utilization resource holes These conditions applicable local global code motion |
0 | Statistical decision theory provides principled way estimate amino acid frequencies conserved positions protein family The goal minimize risk function expected squarederror distance estimates true population frequencies The minimumrisk estimates obtained adding optimal number pseudocounts observed data Two formulas presented one pseudocounts based marginal amino acid frequencies one pseudocounts based observed data Experimental results show profiles constructed using minimalrisk estimates discriminating constructed using existing methods |
0 | Neural computation also called connectionism parallel distributed processing neural network modeling brainstyle computation grown rapidly last decade Despite explosion ultimately impressive applications dire need concise introduction theoretical perspective analyzing strengths weaknesses connectionist approaches establishing links disciplines statistics control theory The Introduction Theory Neural Computation Hertz Krogh Palmer subsequently referred HKP written perspective physics home discipline authors The book fulfills mission introduction neural network novices provided background calculus linear algebra statistics It covers number models often viewed disjoint Critical analyses fruitful comparisons models |
2 | The problem approximating probability distribution occurs frequently many areas applied mathematics including statistics communication theory machine learning theoretical analysis complex systems neural networks Saul Jordan recently proposed powerful method efficiently approximating probability distributions known structured variational approximations In structured variational approximations exact algorithms probability computation tractable substructures combined variational methods handle interactions substructures make system whole intractable In note I present mathematical result simplify derivation struc tured variational approximations exponential family distributions |
0 | We inv estigate applicability adaptive neural network problems timedependent input demonstrating deterministic parser natural language inputs significant syntactic complexity developed using recurrent connectionist architectures The traditional stacking mechanism known necessary proper treatment contextfree languages symbolic systems absent design subsumed recurrency network |
5 | In casebased planning CBP previously generated plans stored cases memory reused solve similar planning problems future CBP save considerable time planning scratch generative planning thus offering potential heuristic mechanism handling intractable problems One drawback CBP systems need highly structured memory requires significant domain engineering complex memory indexing schemes enable efficient case retrieval In contrast CBP system CaPER based massively parallel framebased AI language extremely fast retrieval complex cases large unindexed memory The ability fast frequent retrievals many advantages indexing unnecessary large casebases used memory probed numerous alternate ways allowing specific retrieval stored plans better fit target problem less adaptation fl Preliminary version article appearing IEEE Expert February pp This paper extended version |
0 | Determining architecture neural network important issue learning task For recurrent neural networks general methods exist permit estimation number layers hidden neurons size layers number weights We present simple pruning heuristic significantly improves generalization performance trained recurrent networks We illustrate heuristic training fully recurrent neural network positive negative strings regular grammar We also show rules extracted networks trained recognize strings rules extracted pruning consistent rules learned This performance improvement obtained pruning retraining networks Simulations shown training pruning recurrent neural net strings generated two regular grammars randomlygenerated state grammar state triple parity grammar Further simulations indicate pruning method gives generalization performance superior obtained training weight decay |
5 | Machine learning knowledge engineering always strongly related introduction new representations knowledge engineering created gap This paper describes research aimed applying machine learning techniques current knowledge engineering representations We propose system redesigns part knowledge based system called control knowledge We claim strong similarity redesign knowledge based systems incremental machine learning Finally relate work existing research |
4 | Genetic algorithms stochastic search optimization techniques used wide range applications This paper addresses application genetic algorithms graph partitioning problem Standard genetic algorithms large populations suffer lack efficiency quite high execution time A massively parallel genetic algorithm proposed implementation SuperNode Transputers results various benchmarks given A comparative analysis approach hillclimbing algorithms simulated annealing also presented The experimental measures show algorithm gives better results concerning quality solution time needed reach |
0 | Incremental Class Learning ICL provides feasible framework development scalable learning systems Instead learning complex problem ICL focuses learning subproblems incrementally one time using results prior learning subsequent learning combining solutions appropriate manner With respect multiclass classification problems ICL approach presented paper summarized follows Initially system focuses one category After learns category tries identify compact subset features nodes hidden layers crucial recognition category The system freezes crucial nodes features fixing incoming weights As result features obliterated subsequent learning These frozen features available subsequent learning serve parts weight structures build recognize categories As categories learned set features gradually stabilizes learning new category requires less effort Eventually learning new category may involve combining existing features appropriate manner The approach promotes sharing learned features among number categories also alleviates wellknown catastrophic interference problem We present results applying ICL approach Handwritten Digit Recognition problem based spatiotemporal representation patterns |
0 | Virtually largescale sequencing projects use automatic sequenceassembly programs aid determination DNA sequences The computergenerated assemblies require substantial handediting transform submissions GenBank As size sequencing projects increases becomes essential improve quality automated assemblies timeconsuming handediting may reduced Current ABI sequencing technology uses base calls made fluorescentlylabeled DNA fragments run gels We present new representation fluorescent trace data associated individual base calls This representation used fragment assembly improve quality assemblies We demonstrate one use endtrimming suboptimal data results significant improvement quality subsequent assemblies |
5 | Learning viewed problem planning series modifications memory We adopt view learning propose applicability casebased planning methodology task planning learn We argue relatively simple finegrained primitive inferential operators needed support flexible planning We show possible obtain benefits casebased reasoning within planning learn framework |
1 | Foveal vision features imagers graded acuity coupled context sensitive sensor gaze control analogous prevalent throughout vertebrate vision Foveal vision operates efficiently uniform acuity vision resolution treated dynamically allocatable resource requires refined visual attention mechanism We demonstrate reinforcement learning RL significantly improves performance foveal visual attention overall vision system task model based target recognition A simulated foveal vision system shown classify targets fewer fixations learning strategies acquisition visual information relevant task learning generalize strategies ambiguous unexpected scenario conditions |
3 | We address problem finding subset features allows supervised induction algorithm induce small highaccuracy concepts We examine notions relevance irrelevance show definitions used machine learning literature adequately partition features useful categories relevance We present definitions irrelevance two degrees relevance These definitions improve understanding behavior previous subset selection algorithms help define subset features sought The features selected depend features target concept also induction algorithm We describe method feature subset selection using crossvalidation applicable induction algorithm discuss experiments conducted ID C artificial real datasets |
3 | A constant rebalanced portfolio investment strategy keeps distribution wealth among set stocks period period Recently work online investment strategies competitive best constant rebalanced portfolio determined hindsight Cover Helmbold et al Cover Ordentlich Cover Ordentlich b Ordentlich Cover Cover For universal algorithm Cover Cover provide simple analysis naturally extends case fixed percentage transaction cost commission answering question raised Cover Helmbold et al Cover Ordentlich Cover Ordentlich b Ordentlich Cover Cover In addition present simple randomized implementation significantly faster practice We conclude explaining algorithms applied problems combining predictions statistical language models resulting guarantees striking |
3 | The Longest common subsequence problem examined point view parameterized computational complexity There several different ways parameters enter problem number sequences analyzed length common subsequence size alphabet Lower bounds complexity basic problem imply lower bounds number sequence alignment consensus problems At issue theory parameterized complexity whether problem takes input x k solved time f k n ff ff independent k termed fixedparameter tractability It argued appropriate asymptotic model feasible computability problems small range parameter values covers important applications situation certainly holds many problems biological sequence analysis Our main results show The Longest Common Subsequence LCS parameterized number sequences analyzed hard W The LCS problem problem parameterized length common subsequence belongs W P hard W The LCS problem parameterized number sequences length common subsequence complete W All results obtained unrestricted alphabet sizes For alphabets fixed size problems fixedparameter tractable We conjecture remains hard |
3 | We consider learning situations function used classify examples may switch back forth small number different concepts course learning We examine several models situations oblivious models switches made independent selection examples adversarial models single adversary controls concept switches example selection We show relationships benign models pconcepts Kearns Schapire present polynomialtime algorithms learning switches two kDNF formulas For adversarial model present model success patterned popular competitive analysis used studying online algorithms We describe randomized query algorithm adversarial switches two monotone disjunctions competitive total number mistakes plus queries high probability bounded number switches plus fixed polynomial n number variables We also use notions described provide sufficient conditions learning pconcept class decision rule implies able learn class model probability |
4 | Genetic algorithms proven powerful tool within area machine learning However classes problems seem scarcely applicable eg solution given problem consists several parts influence In case classic genetic operators crossover mutation work well thus preventing good performance This paper describes approach overcome problem using highlevel genetic operators integrating task specific domain independent knowledge guide use operators The advantages approach shown learning rule base adapt parameters image processing operator path within SOLUTION system |
3 | We study learnability ReadkSatisfyj RkSj DNF formulas These boolean formulas disjunctive normal form DNF maximum number occurrences variable bounded k number terms satisfied assignment j After motivating investigation class DNF formulas present algorithm unknown RkSj DNF formula learned high probability finds logically equivalent DNF formula using wellstudied protocol equivalence membership queries The algorithm runs polynomial time k j O log n log log n n number input variables |
5 | This paper proposes model ratio decidendi justification structure consisting series reasoning steps relate abstract predicates abstract predicates relate abstract predicates specific facts This model satisfies important set characteristics ratio decidendi identified jurisprudential literature In particular model shows theory case decided controls precedential effect By contrast purely exemplarbased model ratio decidendi fails account dependency precedential effect theory decision |
0 | In paper problem asymptotic identification fading memory systems presence bounded noise studied For experiment worstcase error characterized terms diameter worstcase uncertainty set Optimal inputs minimize radius uncertainty studied characterized Finally convergent algorithm require knowledge noise upper bound furnished The algorithm based interpolating data spline functions shown well suited identification presence bounded noise basis functions polynomials |
1 | The paper describes selflearning control system mobile robot Based sensor information control system provide steering signal way collisions avoided Since case examples available system learns basis external reinforcement signal negative case collision zero otherwise Rules Temporal Difference learning used find correct mapping discrete sensor input space steering signal We describe algorithm learning correct mapping input state vector output steering signal algorithm used discrete coding input state space |
4 | This paper presents algorithm discovery building blocks genetic programming GP called adaptive representation learning ARL The central idea ARL adaptation problem representation extending set terminals functions set evolvable subroutines The set subroutines extracts common knowledge emerging evolutionary process acquires necessary structure solving problem ARL supports subroutine creation deletion Subroutine creation discovery performed automatically based differential parentoffspring fitness block activation Subroutine deletion relies utility measure similar schema fitness window past generations The technique described tested problem controlling agent dynamic nondeterministic environment The automatic discovery subroutines help scale GP technique complex problems |
4 | Hierarchical genetic programming HGP approaches rely discovery modification use new functions accelerate evolution This paper provides qualitative explanation improved behavior HGP based analysis evolution process dual perspective diversity causality From static point view use HGP approach enables manipulation population higher diversity programs Higher diversity increases exploratory ability genetic search process demonstrated theoretical experimental fitness distributions expanded structural complexity individuals From dynamic point view analysis causality crossover operator suggests HGP discovers exploits useful structures bottomup hierarchical manner Diversity causality complementary affecting exploration exploitation genetic search Unlike machine learning techniques need extra machinery control tradeoff HGP automatically trades exploration exploitation |
5 | Discussions casebased reasoning often reflect implicit assumption case memory system become better informed ie increase knowledge cases added casebase This paper considers formalisations knowledge content necessary preliminary rigourous analysis performance casebased reasoning systems In particular interested modelling learning aspects casebased reasoning order study performance casebased reasoning system changes accumlates problemsolving experience The current paper presents casebase semantics generalises recent formalisations casebased classification Within framework paper explores various issues assuring sematics welldefined illustrates knowledge content case memory system seen reside chosen similarity measure cases casebase |
4 | The pursuerevader PE game recognized important domain study coevolution robust adaptive behavior protean behavior Miller Cliff Nevertheless potential game largely unrealized due methodological hurdles coevolutionary simulation raised PE versions game optimal solutions Isaacs closedended formulations opaque respect solution space lack rigorous metric agent behavior This inability characterize behavior turn obfuscates coevolutionary dynamics We present new formulation PE affords rigorous measure agent behavior system dynamics The game moved twodimensional plane onedimensional bitstring time step evader generates bit pursuer must simultaneously predict Because behavior expressed time series employ information theory provide quantitative analysis agent activity Further version PE opens vistas onto communicative component pursuit evasion behavior providing openended serial communications channel open world via coevolution Results show subtle changes game determine whether openended profoundly affect viability armsrace dynamics |
4 | In many applications decision support negotiation planning scheduling etc one needs express requirements partially satisfied In order express requirements propose technique called forwardtracking Intuitively forwardtracking kind dual chronological backtracking program globally fails find solution new execution started program point state forward computation tree This search technique applied constraint logic programming obtaining powerful extension preserves useful properties original scheme We report successful practical application forwardtracking evolutionary training constrained neural networks |
0 | Five related factors identified enable single compartment HodgkinHuxley model neurons convert random synaptic input irregular spike trains similar seen vivo cortical recordings We suggest cortical neurons may operate narrow parameter regime synaptic intrinsic conductances balanced flect spike timing detailed correlations inputs fl Please send comments tonysalkedu The reference paper Technical Report INC February Institute Neural Computation UCSD San Diego CA |
4 | This paper describes new samplingbased heuristic tree search named SAGE presents analysis performance problem grammar induction This last work inspired Abbadingo DFA learning competition took place Mars November SAGE ended one two winners competition The second winning algorithm first proposed Rodney Price implements new evidencedriven heuristic state merging Our version heuristic also described paper compared SAGE |
5 | When reasoner explains surprising events internal use key motivation explaining perform learning facilitate achievement goals Human explainers use range strategies build explanations including internal reasoning external information search goalbased considerations profound effect choices pursue explanations However standard AI models explanation rely goalneutral use single fixed strategygenerally backwards chainingto build explanations This paper argues explanation modeled goaldriven learning process gathering transforming information discusses issues involved developing active multistrategy process goaldriven explanation |
6 | Conventional speculative architectures use branch prediction evaluate likely execution path program execution However certain branches difficult predict One solution problem evaluate paths following conditional branch Predicated execution used implement form multipath execution Predicated architectures fetch issue instructions associated predicates These predicates indicate instruction commit result Predicating branch reduces number branches executed eliminating chance branch misprediction cost executing additional instructions In paper propose restricted form multipath execution called Dynamic Predication architectures little support predicated instructions instruction set Dynamic predication dynamically predicates instruction sequences form branch hammock concurrently executing paths branch A branch hammock short forward branch spans instructions form ifthen ifthenelse construct We mark constructs executable When decode stage detects sequence passes predicated instruction sequence dynamically scheduled execution core Our results show dynamic predication accrue speedups |
0 | Reliable visionbased control autonomous vehicle requires ability focus attention important features input scene Previous work autonomous lane following system ALVINN Pomerleau yielded good results uncluttered conditions This paper presents artificial neural network based learning approach handling difficult scenes confuse ALVINN system This work presents mechanism achieving taskspecific focus attention exploiting temporal coherence A saliency map based upon computed expectation contents inputs next time step indicates regions input retina important performing task The saliency map used accentuate features important task deemphasize |
6 | A version paper appear ACM Transactions Computer Systems August Permission make digital copies part work personal classroom use grantedwithout fee provided copies made distributed profit commercial advantage copies bear notice full citation first page Copyrights components work owned others ACM must honored Abstracting credit permitted To copy otherwise republish post servers redistribute lists requires prior specific permission andor fee Abstract To achieve high performance contemporary computer systems rely two forms parallelism instructionlevel parallelism ILP threadlevel parallelism TLP Wideissue superscalar processors exploit ILP executing multiple instructions single program single cycle Multiprocessors MP exploit TLP executing different threads parallel different processors Unfortunately parallelprocessing styles statically partition processor resources thus preventing adapting dynamicallychanging levels ILP TLP program With insufficient TLP processors MP idle insufficient ILP multipleissue hardware superscalar wasted This paper explores parallel processing alternative architecture simultaneous multithreading SMT allows multiple threads compete share processors resources every cycle The compelling reason running parallel applications SMT processor ability use threadlevel parallelism instructionlevel parallelism interchangeably By permitting multiple threads share processors functional units simultaneously processor use ILP TLP accommodate variations parallelism When program single thread SMT processors resources dedicated thread TLP exists parallelism compensate lack |
2 | This paper demonstrates use graphs mathematical tool expressing independencies formal language communicating processing causal information decision analysis We show complex information external interventions organized represented graphically conversely graphical representation used facilitate quantitative predictions effects interventions We first review theory Bayesian networks show directed acyclic graphs DAGs offer economical scheme representing conditional independence assumptions deducing displaying logical consequences assumptions We introduce manipulative account causation show DAG defines simple transformation tells us probability distribution change result external interventions system Using transformation possible quantify nonexperimental data effects external interventions specify conditions randomized experiments necessary As example show effect smoking lung cancer quantified nonexperimental data using minimal set qualitative assumptions Finally paper offers graphical interpretation Rubins model causal effects demonstrates equivalence manipulative account causation We exemplify tradeoffs two approaches deriving nonparametric bounds treatment effects conditions imperfect compliance fl Portions paper presented th Session International Statistical Institute Florence Italy August September |
0 | A paradigm statistical mechanics financial markets SMFM fit multivariate financial markets using Adaptive Simulated Annealing ASA global optimization algorithm perform maximum likelihood fits Lagrangians defined path integrals multivariate conditional probabilities Canonical momenta thereby derived used technical indicators recursive ASA optimization process tune trading rules These trading rules used outofsample data demonstrate profit SMFM model illustrate markets likely efficient This methodology extended systems eg electroencephalography This approach complex systems emphasizes utility blending intuitive powerful mathematicalphysics formalism generate indicators used AItype rulebased models management |
1 | Previous approaches multiagent reinforcement learning either limited heuristic nature The main reason agents animats environment continually changes learning animats keep changing Traditional reinforcement learning algorithms properly deal Their convergence theorems require repeatable trials strong typically Markovian assumptions environment In paper however use novel general sound method multiple reinforcement learning animats living single life limited computational resources unrestricted changing environment The method called incremental selfimprovement IS Schmidhuber IS properly takes account whatever animat learns point may affect learning conditions animats later point The learning algorithm ISbased animat embedded policy animat improve performance principle also improve way improves etc At certain times animats life IS uses reinforcementtime ratios estimate single training example namely entire life far previously learned things still useful selectively keeps gets rid start appearing harmful IS based efficient stackbased backtracking procedure guaranteed make animats learning history history longterm reinforcement accelerations Experiments demonstrate IS effectiveness In one experiment IS learns sequence complex function approximation problems In another multiagent system consisting three coevolving ISbased animats chasing learns interesting stochastic predator prey strategies |
0 | Just input state stability iss generalizes idea finite gains respect supremum norms new notion integral input state stability iiss generalizes concept finite gain using integral norm inputs In paper obtain necessary sufficient characterization iiss property expressed terms dissipation inequalities |
0 | We derive new selforganising learning algorithm maximises information transferred network nonlinear units The algorithm assume knowledge input distributions defined zeronoise limit Under conditions information maximisation extra properties found linear case Linsker The nonlinearities transfer function able pick higherorder moments input distributions perform something akin true redundancy reduction units output representation This enables network separate statistically independent components inputs higherorder generalisation Principal Components Analysis We apply network source separation cocktail party problem successfully separating unknown mixtures ten speakers We also show variant network architecture able perform blind deconvolution cancellation unknown echoes reverberation speech signal Finally derive dependencies information transfer time delays We suggest information maximisation provides unifying framework problems blind signal processing fl Please send comments tonysalkedu This paper appear Neural Computation The reference version Technical Report INC February Institute Neural Computation UCSD San Diego CA |
3 | Research bias machine learning algorithms generally concerned impact bias predictive accuracy We believe factors also play role evaluation bias One factor stability algorithm words repeatability results If obtain two sets data phenomenon underlying probability distribution would like learning algorithm induce approximately concepts sets data This paper introduces method quantifying stability based measure agreement concepts We also discuss relationships among stability predictive accuracy bias |
3 | This paper describes new efficient algorithms learning deterministic finite automata Our approach primarily distinguished two features adoption averagecase setting model typical labeling finite automaton retaining worstcase model underlying graph automaton along learning model learner provided means experiment machine rather must learn solely observing automatons output behavior random input sequence The main contribution paper presenting first efficient algorithms learning nontrivial classes automata entirely passive learning model We adopt online learning model learner asked predict output next state given next symbol random input sequence goal learner make prediction mistakes possible Assuming learner means resetting target machine fixed start state first present efficient algorithm makes expected polynomial number mistakes model Next show first algorithm used subroutine second algorithm also makes polynomial number mistakes even absence reset Along way prove number combinatorial results randomly labeled automata We also show labeling states bits input sequence need truly random merely semirandom Finally discuss extension results model automata used represent distributions binary strings |
0 | In paper consider problem approximating function belonging function space linear combination n translates given function G Using lemma Jones Barron show possible define function spaces functions G rate convergence zero error O p n number dimensions The apparent avoidance curse dimensionality due fact function spaces constrained dimension increases Examples include spaces Sobolev type number weak derivatives required larger number dimensions We give results approximation L norm L norm The interesting feature results thanks constructive nature Jones Barrons lemma iterative procedure defined achieve rate This paper describes research done within Center Biological Information Processing Department Brain Cognitive Sciences Artificial Intelligence Laboratory Department Mathematics University Trento Italy Gabriele Anzellotti Department Mathematics University Trento Italy This research sponsored grant Office Naval Research ONR Cognitive Neural Sciences Division Artificial Intelligence Center Hughes Aircraft Corporation S Support A I Laboratorys artificial intelligence research provided Advanced Research Projects Agency Department Defense Army contract DACAC part ONR contract NK c fl Massachusetts Institute Technology |
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