Paul Bodily and Dan Ventura, “Operationalizing Essential Characteristics of Creativity in a Computational System for Music Composition”, Proceedings of the 38th Annual AAAI Conference on Artificial Intelligence, to appear, 2024
Mary Lou Maher, Dan Ventura and Brian Magerko, “The Grounding Problem: An Approach to the Integration of Cognitive and Generative Models”, Proceedings of the AAAI Fall Symposium on Integrating Cognitive Architectures and Generative Models, 2023
Brad Spendlove and Dan Ventura, “A Constraint-centric Accounting of Some Aspects of Creativity”, Proceedings of the 14th International Conference on Computational Creativity, pp. 332-336, 2023
Jonathan Demke, Kaz Grace, Francisco Ibarrola and Dan Ventura, “Transformational Creativity Through the Lens of Quality-Diversity”, Proceedings of the 14th International Conference on Computational Creativity, pp. 304-307, 2023
Robert Morain, Brandon Kinghorn and Dan Ventura, “Are Language Models Unsupervised Multi-domain CC Systems?”, Proceedings of the 14th International Conference on Computational Creativity, pp. 39-43, 2023
Connor Wilhelm and Dan Ventura, “Gaining Expertise through Task Re-Representation”, Proceedings of the 14th International Conference on Computational Creativity, pp. 299-303, 2023
Dan Ventura, “The Emperor’s New Co-author”, Proceedings of the 14th International Conference on Computational Creativity, pp. 55-63, 2023
Tomas Lawton, Francisco Ibarrola, Dan Ventura and Kaz Grace, “Drawing with Reframer: Emergence and control in co-creative AI”, Proceedings of the 28th Annual Conference on Intelligent User Interfaces, pp. 264-277, 2023
Robert Morain and Dan Ventura, “Symbolic Semantic Memory in Transformer Language Models”, Proceedings of the International Conference on Machine Learning and Applications, pp. 992-998, 2022
Daniel G. Brown and Dan Ventura, “Ethics, Aesthetics and Computational Creativity”, Proceedings of the 13th International Conference on Computational Creativity, pp. 150-158, 2022
Brad Spendlove and Dan Ventura, “Competitive Language Games as Creative Tasks with Well-Defined Goals”, Proceedings of the 13th International Conference on Computational Creativity, pp. 291-299, 2022
Paul Bodily and Dan Ventura, “Open Computational Creativity Problems in Computational Theory”, Proceedings of the 13th International Conference on Computational Creativity, pp. 112-120, 2022
Mary Lou Maher, Brian Magerko, Dan Ventura, Douglas Fisher, Rogelio Cardona-Rivera, Nancy Fulda, John Gero, Minwoo Lee, David Wilson and James C. Kaufman, “A Research Plan for Integrating Generative and Cognitive AI for Human Centered, Explainable Co-Creative AI”, CHI Generative AI and HCI workshop, 2022
Paul Bodily and Dan Ventura, “Steerable Music Generation which SatisfiesLong-Range Dependency Constraints”, Transactions of the International Society for Music Information Retrieval, 5(1): 71-86, 2021
Puneet Jain, Najma Mathema, Jonathan Skaggs and Dan Ventura, “Ideation via Critic-based Exploration of Generator Latent Space”, Proceedings of the 12th International Conference on Computational Creativity, pp. 377-385, 2021
Paul Bodily and Dan Ventura, “Multi-agent Story-based Settlement Generation”, Proceedings of the 33rd International Conference on Tools with Artificial Intelligence, pp. 1149-1153, 2021
Paul Bodily and Dan Ventura, “Inferring Structural Constraints in Musical Sequences via Multiple Self-Alignment”, Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, pp. 1112-1118, 2021
Paul Bodily and Dan Ventura, “What Happens When a Computer Joins the Group?”, Proceedings of the 11th International Conference on Computational Creativity, pp. 41-48, 2020
Oliver Bown, Kazjon Grace, Liam Bray and Dan Ventura, “Speculative Exploration of the Role of Dialogic AI in Co-creation”, Proceedings of the 11th International Conference on Computational Creativity, pp. 25-32, 2020
Brad Spendlove and Dan Ventura, “Humans in the Black Box: A New Paradigm for Evaluating the Design of Creative Systems”, Proceedings of the 11th International Conference on Computational Creativity, pp. 311-318, 2020
Brad Spendlove and Dan Ventura, “Creating Six-word Stories via Inferred Linguistic and Semantic Formats”, Proceedings of the 11th International Conference on Computational Creativity, pp. 123-130, 2020
Rob Smith, Jebediah Rosen and Dan Ventura, “Adapting Standard External Clustering Metrics for Repetitive, Noisy Observations”, Proceedings of the 18th International Conference on Machine Learning and Applications, pp. 908-914, 2019
Benjamin Bay, Paul Bodily and Dan Ventura, “Dynamically Scoring Rhymes with Phonetic Features and Sequence Alignment”, Proceedings of the 31st International Conference on Tools with Artificial Intelligence, pp. 1521-1525, 2019
Brad Spendlove and Dan Ventura, “Modeling Knowledge, Expression and Aesthetics via Sensory Grounding”, Proceedings of the 10th International Conference on Computational Creativity, pp. 326-330, 2019
Dan Ventura, “Autonomous Intentionality in Computationally Creative Systems”, in Computational Creativity: The Philosophy and Engineering of Autonomously Creative Systems, Tony Veale and Amílcar Cardoso (eds.), pp. 49-65, Springer International Publishing, 2019
Jukka M. Toivanen, Matti Järvisalo, Olli Alm, Dan Ventura, Martti Vaino, Hannu Toivonen, “Towards Transformational Creation of Novel Songs”, Connection Science, 31(1): 4-32, 2019
Paul Bodily and Dan Ventura, “Musical Metacreation: Past, Present and Future”, Proceedings of the 6th International Workshop on Musical Metacreation, 2018
Paul Bodily and Dan Ventura, “Comparative Analysis of Key Inference Methods from Melody in Symbolic Music”, Proceedings of the 6th International Workshop on Musical Metacreation, 2018
Paul Bodily and Dan Ventura, “Explainability: An Aesthetic for Aesthetics in Computational Creative Systems”, Proceedings of the 9th International Conference on Computational Creativity, pp. 153-160, 2018
Brad Spendlove, Nathan Zabriskie and Dan Ventura, “An HBPL-based Approach to the Creation of Six-word Stories”, Proceedings of the 9th International Conference on Computational Creativity, pp. 161-168, 2018
Dan Ventura and Darin Gates, “Ethics as Aesthetic: A Computational Creativity Approach to Ethical Behavior”, Proceedings of the 9th International Conference on Computational Creativity, pp. 185-191, 2018
Dan Ventura, “Ethics as Aesthetic for Artificial General Intelligence”, AAAI Spring Symposium on AI and Society: Ethics, Safety and Trustworthiness in Intelligent Agents, 2018
Aurora Tulilaulu, Matti Nelimarkka, Joonas Paalasmaa, Daniel Johnson, Dan Ventura, Hannu Toivonen, “Data Musicalization ”, ACM Transactions on Multimedia Computing Communications and Applications, 14(2):article 47, 2018
Aaron Dennis and Dan Ventura, “Online Structure-Search for Sum-Product Networks”, Proceedings of the International Conference on Machine Learning and Applications, pp. 155-160, 2017
Aaron Dennis and Dan Ventura, “Autoencoder-Enhanced Sum-Product Networks”, Proceedings of the International Conference on Machine Learning and Applications, pp. 1041-1044, 2017
Paul Bodily and Dan Ventura, “HBPL: a Framework for Debating, Developing, and Reusing Foundational Models of Musical Metacreativity”, Proceedings of the 5th International Workshop on Musical Metacreation, 2017
Benjamin Bay, Paul Bodily and Dan Ventura, “Creative Text Transformation Via Constraints and Word Embedding”, Proceedings of the Eighth International Conference on Computational Creativity, pp. 49-56, 2017
Dan Ventura, “How to Build a CC System”, Proceedings of the Eighth International Conference on Computational Creativity, pp. 253-260, 2017
Maya Ackerman, Ashok Goel, Colin Johnson, Anna Jordanous, Carlos León, Rafael Pérez y Pérez, Hannu Toivonen and Dan Ventura, “Teaching Computational Creativity”, Proceedings of the Eighth International Conference on Computational Creativity, pp. 9-16, 2017
Paul Bodily, Benjamin Bay and Dan Ventura, “Computational Creativity via Human-Level Concept Learning”, Proceedings of the Eighth International Conference on Computational Creativity, pp. 57-64, 2017
Derrall Heath and Dan Ventura, "Semantic Style Creation”, Proceedings of the AAAI Workshop on Human-Machine Collaborative Learning, pp. 676-679, 2017
Rob Smith and Dan Ventura, “Novel Metrics for Evaluating Algorithms for Estimating Latent Signal from Repetitive Noisy Observations”, Proceedings of the 13th Annual Biotechnology and Bioinformatics Symposium, 2016
Dan Ventura, “Beyond Computational Intelligence to Computational Creativity in Games”, Proceedings of the IEEE Conference on Computational Intelligence and Games, pp. 427-435, 2016
Dan Ventura, “Mere Generation: Essential Barometer or Dated Concept?”, Proceedings of the Seventh International Conference on Computational Creativity, pp. 17-24, 2016
Derrall Heath and Dan Ventura, “Before a Computer Can Draw, It Must First Learn to See”, Proceedings of the Seventh International Conference on Computational Creativity, pp. 172-179, 2016
Derrall Heath and Dan Ventura, “Creating Images by Imagining Image Semantics using Vector Space Models”, Proceedings of the Association for the Advancement of Artificial Intelligence, pp. 1202-1208, 2016
Alok Desai, Dan Ventura and Dah-Jye Lee, “An Efficient Feature Descriptor Based on Synthetic Basis Functions and Uniqueness Matching Strategy”, Computer Vision and Image Understanding, 142: 37-49, 2016
Jared Lundell, Charles DuHadway and Dan Ventura, “Data-driven Kernels via Semi-Supervised Clustering on the Manifold”, Proceedings of the International Conference on Machine Learning and Applications, 2015
Paul M. Bodily, Stanley Fujimoto, Quinn Snell, Dan Ventura and Mark J. Clement, “ScaffoldScaffolder: Solving Contig Orientation via Bidirected to Directed Graph Reduction”, Bioinformatics, 32(1): 17-24, 2016
Daniel Johnson and Dan Ventura, “Musical Motif Discovery from Non-musical Inspiration Sources”, ACM Computers in Entertainment---Special Issue on Musical Metacreation, 14(2): article 7, 2016
Dean M. Lebaron, Logan A. Mitchell and Dan Ventura, “Intelligent Content Generation via Abstraction, Evolution and Reinforcement”, Proceedings of the AIIDE Workshop on Experimental AI in Games, pp. 36-41, 2015
David Norton, Derrall Heath and Dan Ventura, “Accounting for Bias in the Evaluation of Creative Computational Systems: An Assessment of DARCI”, Proceedings of the International Conference on Computational Creativity, pp. 31-38, 2015
Simon Colton, Ian Gouldstone, Michael Cook, Dan Ventura and Jakob Halskov, “The Painting Fool Sees! New Projects with the Automated Painter”, Proceedings of the International Conference on Computational Creativity, pp. 189-196, 2015
Martin Mumford and Dan Ventura, “The Man Behind the Curtain: Overcoming Skepticism about Creative Computers”, Proceedings of the International Conference on Computational Creativity, pp. 1-7, 2015
Derrall Heath, Aaron Dennis and Dan Ventura, “Imagining Imagination: A Computational Framework Using Associative Memory Models and Vector Space Models”, Proceedings of the International Conference on Computational Creativity, pp. 244-251, 2015
Aaron Dennis and Dan Ventura, “Greedy Structure Search for Sum-Product Networks”, Proceedings of the International Joint Conference on Artificial Intelligence, pp. 932-938, 2015
Dan Ventura, “The Computational Creativity Complex”, in Computational Creativity Research: Towards Creative Machines, Tarek R. Besold, Marco Schorlemmer, Alan Smaill (eds.), Atlantis Press, pp. 65-92, 2015
Simon Colton, Alison Pease, Joseph Corneli, Michael Cook, Rose Hepworth and Dan Ventura, “On Studying and Handling Observer Issues in Computational Creativity Research and Practice”, in Computational Creativity Research: Towards Creative Machines, Tarek R. Besold, Marco Schorlemmer, Alan Smaill (eds.), Atlantis Press, pp. 3-36, 2015
Rob Smith, John T. Prince and Dan Ventura, “A Coherent Mathematical Characterization of Isotope Trace Extraction, Isotopic Envelope Extraction, and LC-MS Correspondence”, BMC Bioinformatics, 16(Suppl 7): S1, 2015 (an earlier version of the paper was presented at the 11th Annual Biotechnology and Bioinformatics Symposium 2014)
Alok Desai, Dah-Jye Lee and Dan Ventura, “Matching Affine Features with the SYBA Feature Descriptor”, Proceedings of the International Symposium on Visual Computing Part II, LNCS 8888, pp. 448-457, 2014
Spencer G. Fowers, Alok Desai, Dah-Jye Lee, Dan Ventura and James Archibald, “TreeBASIS Feature Descriptor and Its Hardware Implementation", International Journal of Reconfigurable Computing, vol. 2014, article ID 606210, 2014
Adam Drake and Dan Ventura, “Using Spectral Features to Improve Sentiment Analysis”, Proceedings of the International Conference on Machine Learning and Applications, pp. 153-158, 2014
Adam Drake and Dan Ventura, “Improving Spectral Learning by Using Multiple Representations”, Proceedings of the International Conference on Machine Learning and Applications, pp. 147-152, 2014
Spencer G. Fowers, Alok Desai, D.J. Lee, Dan Ventura and Doran K. Wilde, “An Efficient, Tree-Based Feature Descriptor and Matching Algorithm”, AIAA Journal of Aerospace Information Systems 11(9): 596-606, 2014
Dan Ventura, “Can a Computer be Lucky? And Other Ridiculous Questions Posed by Computational Creativity”, Proceedings of the Seventh Conference on Artificial General Intelligence, LNAI 8598, pp. 208-217, 2014
Simon Colton and Dan Ventura, “You Can't Know my Mind: A Festival of Computational Creativity”, Proceedings of the 5th International Conference on Computational Creativity, pp. 351-354, 2014
Daniel Johnson and Dan Ventura, “Musical Motif Discovery in Non-musical Media”, Proceedings of the 5th International Conference on Computational Creativity, pp. 91-99, 2014
Michael R. Smith, Ryan S. Hintze and Dan Ventura, “Nehovah: A Neologism Creator Nomen Ipsum”, Proceedings of the 5th International Conference on Computational Creativity, pp. 173-181, 2014
David Norton, Derrall Heath and Dan Ventura, “Autonomously Managing Competing Objectives to Improve the Creation and Curation of Artifacts”, Proceedings of the 5th International Conference on Computational Creativity, pp. 23-32, 2014
Robert Smith, Andrew D. Mathis, Dan Ventura and John T. Prince, “Proteomics, Lipidomics, Metabolomics: A Mass Spectrometry Tutorial from a Computer Scientist’s Point of View”, BMC Bioinformatics 15(Suppl 7): S9, 2014 (an earlier version of the paper was presented at the 10th Annual Biotechnology and Bioinformatics Symposium 2013)
Robert Smith, Dan Ventura and John T. Prince, “LC-MS Alignment in Theory and Practice: A Comprehensive Algorithmic Review”, Briefings in Bioinformatics, DOI: 10.1093/bib/bbt080, 2013
Adam Drake and Dan Ventura, “An Empirical Comparison of Spectral Learning Methods for Classification”, Proceedings of the International Conference on Machine Learning and Applications, pp. 9-14, 2013
Robert Smith and Dan Ventura, "A General Model for Continuous Noninvasive Pulmonary Artery Pressure Estimation", Computers in Biology and Medicine, 43(7): 904-13, 2013.
Derrall Heath, David Norton, Eric Ringger and Dan Ventura, "Semantic Models as a Combination of Free Association Norms and Corpus-based Approaches", IEEE International Conference on Semantic Computing, pp. 48-55, 2013.
Robert Smith, Tamil S. Anthonymuthu, Dan Ventura and John Prince, "Statistical Agglomeration: Peak Summarization for Direct Infusion Lipidomics", Bioinformatics, 29(19): 2445-2451, 2013.
Kristine Monteith, Bruce Brown, Dan Ventura and Tony Martinez, “Automatic Generation of Music for Inducing Physiological Response”, Proceedings of the 35th Annual Meeting of the Cognitive Science Society, pp. 3098-3103, 2013
Robert Smith, Dan Ventura and John Prince, "Controlling for Confounding Variables in MS-omics Protocol: Why Modularity Matters", Briefings in Bioinformatics, DOI: 10.1093/bib/bbt049, 2013.
Derrall Heath, David Norton and Dan Ventura, "Autonomously Communicating Conceptual Knowledge Through Visual Art", Proceedings of the 4th International Conference on Computational Creativity, pp. 97-104, 2013.
Robert Smith, Dan Ventura, John Prince, "Novel Algorithms and the Benefits of Comparative Validation", Bioinformatics, 29(12): 1583-1585, 2013.
Robert Smith, Ryan Williamson, Dan Ventura and John Prince, "Rubabel: Wrapping OpenBabel with Ruby", Journal of Chemoinformatics, 5(1): 35-44, 2013.
Spencer G. Fowers, D.J. Lee and Dan Ventura, "A Novel Feature Descriptor for Low-Resource Embedded Vision Sensors for Micro-UAV Applications", AIAA Journal of Aerospace Information Systems 10(8): 385-395, 2013.
David Norton, Derrall Heath and Dan Ventura, “Finding Creativity in an Artificial Artist”, Journal of Creative Behavior, 47(2): 106-124, 2013.
Derrall Heath and Dan Ventura, “Improving Multi-label Classification by Avoiding Implicit Negativity with Incomplete Data”, Computational Intelligence 30(3):535-561, 2014.
Spencer G. Fowers, D.J. Lee, Dan Ventura and James Archibald, “Nature-Inspired BASIS Feature Descriptor and Its Hardware Implementation”, IEEE Transactions on Circuits and System for Video Technology, 23(5): 756-768, 2013.
Aaron Dennis and Dan Ventura, “Learning the Architecture of Sum-Product Networks Using Clustering on Variables”, Neural Information Processing Systems, pp. 2042-2050, 2012.
Derrall Heath, David Norton and Dan Ventura, “Conveying Semantics through Visual Metaphor”, ACM Transactions on Intelligent Systems and Technology, 5(2):31:1--31:17, 2014.
Skyler Murray and Dan Ventura, “Algorithmically Flexible Style Composition Through Multi-Objective Fitness Functions”, 1st International Workshop on Musical Metacreation, pp. 55-62, 2012.
Kenneth Sundberg, Mark Clement, Quinn Snell, Dan Ventura, Michael Whiting and Keith Crandall, “Phylogenetic Search Through Partial Tree Mixing”, BMC Bioinformatics, 13(13): S8, 2012.
Spencer G Fowers, D.J. Lee, Dan Ventura and Doran K. Wilde, “A Novel, Efficient, Tree-Based Descriptor and Matching Algorithm”, Proceedings of the 21st International Conference on Pattern Recognition, pp. 2464-2467, 2012.
Kristine Monteith, Tony Martinez and Dan Ventura, “Automatic Generation of Melodic Accompaniments for Lyrics”, Proceedings of the Third International Conference on Computational Creativity, pp. 87-94, 2012.
Richard Morris, Scott Burton, Paul Bodily and Dan Ventura, “Soup Over Bean of Pure Joy: Culinary Ruminations of an Artificial Chef”, Proceedings of the Third International Conference on Computational Creativity, pp.119-125, 2012.
Robert Smith, Aaron Dennis and Dan Ventura, “Automatic Composition from Non-musical Inspiration Sources”, Proceedings of the Third International Conference on Computational Creativity, pp. 160-164, 2012.
Robert Van Dam, Irene Langkilde-Geary and Dan Ventura, “Adapting ADtrees for Improved Performance on Large Datasets with High Arity Features”, Knowledge and Information Systems, 35(3): 525-552, 2013.
Dan Ventura, “Rational Irrationality”, Game Theory for Security, Sustainability and Health, AAAI 2012 Spring Symposium Series, 2012.
Toh Koon Charlie Neo and Dan Ventura, “A Direct Boosting Algorithm for the K-Nearest Neighbor Classifier via Local Warping of the Distance Metric”, Pattern Recognition Letters, 33(1): 92-102, 2012.
Kenneth Sundberg, Mark Clement, Quinn Snell, Dan Ventura, Michael F. Whiting and Keith A. Crandall, “Partial Tree Mixing--a Novel Approach to Phylogenetic Search”, Biotechnology and Bioinformatics Symposium, 2011.
David Norton, Derrall Heath and Dan Ventura, “A Collaboration with DARCI”, ACM C&C Workshop on Semi-Automated Creativity, 2011.
David Norton, Derrall Heath and Dan Ventura, “An Artistic Dialogue with the Artificial”, Proceedings of the Eighth ACM Conference on Creativity and Cognition, pp. 31-40, 2011.
Mike Gashler, Dan Ventura and Tony Martinez, “Manifold Learning by Graduated Optimization”, IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, 41(6): 1458-1470, 2011.
Kyle Dickerson and Dan Ventura, “Musical Query-by-Content using Self Organizing Maps”, Proceedings of the International Joint Conference on Neural Networks, pp. 705-710, 2011.
David Norton, Derrall Heath and Dan Ventura, “Autonomously Creating Quality Images”, Proceedings of the Second International Conference on Computational Creativity, pp. 10-15, 2011.
Kristine Monteith, Virginia Francisco, Tony Martinez, Pablo Gervás and Dan Ventura, “Automatic Generation of Emotionally-Targeted Soundtracks”, Proceedings of the Second International Conference on Computational Creativity, pp. 60-62, 2011.
Dan Ventura, “No Free Lunch in the Search for Creativity”, Proceedings of the Second International Conference on Computational Creativity, pp. 108-110, 2011.
Aaron Dennis, Andrew D. Michaels, Patti Arand and Dan Ventura, "Noninvasive Estimation of Pulmonary Artery Pressure Using Heart Sound Analysis”, Computers in Biology and Medicine, 40(9): 758-764, 2010.
David Norton and Dan Ventura, "Improving Liquid State Machines Through Iterative Refinement of the Reservoir”, Neurocomputing, 73(16-18): 2893-2904, 2010.
Jeffrey S. Whiting, Jonathan Dinerstein, Paris K. Egbert and Dan Ventura, "Cognitive and Behavioral Model Ensembles for Autonomous Virtual Characters”, Computational Intelligence, 26(2): 142-159, 2010.
Kristine Monteith, Tony Martinez and Dan Ventura, "Computational Modeling of Emotional Content in Music", Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 2356-2361, 2010.
David Norton, Derrall Heath and Dan Ventura, “Establishing Appreciation in a Creative System”, Proceedings of the First International Conference on Computational Creativity, pp. 26-35, 2010.
Kristine Monteith, Tony Martinez and Dan Ventura, "Automatic Generation of Music for Inducing Emotive Response", Proceedings of the First International Conference on Computational Creativity, pp. 140-149, 2010.
Dan Ventura, “A Sub-symbolic Model of the Cognitive Processes of Re-representation and Insight”, Proceedings of the ACM Conference on Creativity and Cognition, pp. 409-410, 2009 (a longer version of this first appeared in Creative Intelligent Systems: Papers from the AAAI Spring Symposium, 2008).
Adam Drake and Dan Ventura, "Search Techniques for Fourier-Based Learning", Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1040-1045, 2009 (this first appeared in Search in Artificial Intelligence and Robotics: Papers from the AAAI Workshop, 2008).
Ilya Raykhel and Dan Ventura, "Real-time Automatic Price Prediction for eBay Online Trading", Proceedings of the Innovative Applications of Artificial Intelligence Conference, pp. 135-140, 2009.
Neil Toronto, Bryan Morse, Kevin Seppi and Dan Ventura, "Super-Resolution via Recapture and Bayesian Effect Modeling", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2388-2395, 2009.
David Norton and Dan Ventura, "Improving the Separability of a Reservoir Facilitates Learning Transfer", Proceedings of the International Joint Conference on Neural Networks, pp.2288-2293, 2009 (this first appeared in Transfer Learning for Complex Tasks: Papers from the AAAI Workshop, 2008).
Kyle Dickerson and Dan Ventura, "Using Self-Organizing Maps to Implicitly Model Preference for a Musical Query-by-Content System", Proceedings of the International Joint Conference on Neural Networks, pp. 705-710, 2009.
Jonathan Dinerstein, Parris Egbert, Dan Ventura and Michael Goodrich, "Demonstration-based Behavior Programming for Embodied Virtual Agents", Computational Intelligence, 24(4): 235-256, 2008.
Dan Ventura, "A Reductio Ad Absurdum Experiment in Sufficiency for Evaluating (Computational) Creative Systems", Proceedings of the International Joint Workshop on Computational Creativity, pp. 11-19, 2008.
Heather Chan and Dan Ventura, “Automatic Composition of Themed Mood Pieces”, Proceedings of the International Joint Workshop on Computational Creativity, pp. 109-115, 2008.
Adam Drake, Eric Ringger and Dan Ventura, "Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment", Proceedings of the IEEE International Conference on Semantic Computing, pp. 152-157, 2008.
Robert D. Van Dam, Irene Geary and Dan Ventura, "Adapting ADtrees for High Arity Features", Proceedings of the Association for the Advancement of Artificial Intelligence, pp. 708-713, 2008.
Jonathan Dinerstein, Parris Egbert,Dan Ventura and Michael Goodrich, "Data-Driven Programming and Behavior for Autonomous Virtual Characters", Proceedings of the Association for the Advancement of Artificial Intelligence, pp. 1450-1451, 2008.
Dan Ventura, “Sub-symbolic Re-representation to Facilitate Learning Transfer”, Creative Intelligent Systems: Papers from the AAAI Spring Symposium, Technical Report SS-08-03, pp. 128-134, 2008.
Mike Gashler, Tony Martinez and Dan Ventura, "Iterative Non-linear Dimensionality Reduction with Manifold Sculpting", Neural Information Processing Systems 20, pp. 513-520, 2008.
Rob Van Dam and Dan Ventura, "ADtrees for Sequential Data and N-gram Counting", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 492-497, 2007.
Sabra Dinerstein, Jonathan Dinerstein and Dan Ventura, "Robust Multi-Modal Biometric Fusion via SVM Ensemble", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1530-1535, 2007.
Jared Lundell and Dan Ventura, "A Data-dependent Distance Measure for Transductive Instance-based Learning", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2825-2830, 2007.
Neil Toronto, Bryan Morse, Dan Ventura, Kevin Seppi, "The Hough Transform's Implicit Bayesian Foundation", Proceedings of the IEEE International Conference on Image Processing, pp. 377-380, 2007.
Jake Merrell, Dan Ventura and Bryan Morse, "Clustering Music via the Temporal Similarity of Timbre", IJCAI Workshop on Artificial Intelligence and Music, pp. 153-164, 2007.
Nancy Fulda and Dan Ventura, "Predicting and Preventing Coordination Problems in Cooperative Q-learning Systems", Proceedings of the International Joint Conference on Artificial Intelligence, pp. 780-785, 2007.
Jonathan Dinerstein, Dan Ventura and Parris Egbert, "Learning Policies for Embodied Virtual Agents Through Demonstration", Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1257-1262, 2007.
Kaivan Kamali, Dan Ventura, Amulya Garga and Soundar Kumara, "Geometric Task Decomposition in a Multi-agent Environment", Applied Artificial Intelligence, 20(5): 437-456, 2006.
Eric Goodman and Dan Ventura, "Spatiotemporal Pattern Recognition via Liquid State Machines", Proceedings of the International Joint Conference on Neural Networks, pp. 7579-7584, July 2006.
Neil Toronto and Dan Ventura, "Learning Quantum Operators from Quantum State Pairs", Proceedings of the IEEE Congress on Evolutionary Computation, pp. 9157-9162, July 2006.
David Norton and Dan Ventura, "Preparing More Effective Liquid State Machines Using Hebbian Learning", Proceedings of the International Joint Conference on Neural Networks, pp. 8359-8364, July 2006.
Nancy Fulda and Dan Ventura, "Learning a Rendezvous Task with Dynamic Joint Action Perception", Proceedings of the International Joint Conference on Neural Networks, pp. 627-632, July 2006.
Jonathan Dinerstein, Dan Ventura and Parris Egbert, "Fast and Robust Incremental Action Prediction for Interactive Agents", Computational Intelligence, 21(1): 90-110, 2005.
Adam Drake and Dan Ventura, "A Practical Generalization of Fourier-based Learning", Proceedings of the International Conference on Machine Learning, pp. 185-192, August 2005.
Neil Toronto, Dan Ventura and Bryan Morse, "Edge Inference for Image Interpolation", Proceedings of the International Joint Conference on Neural Networks, pp. 1782-1787, July 2005.
Eric Goodman and Dan Ventura, "Effectively Using Recurrently Connected Spiking Neural Networks", Proceedings of the International Joint Conference on Neural Networks, pp. 1542-1547, July 2005.
Adam Drake and Dan Ventura, "Comparing High-Order Binary Features", Proceedings of the Joint Conference on Information Sciences, pp. 428-431, July 2005.
Eric Goodman and Dan Ventura, "Time Invariance and Liquid State Machines", Proceedings of the Joint Conference on Information Sciences, pp. 420-423, July 2005.
Nancy Fulda and Dan Ventura, "Incremental Policy Learning: An Equilibrium Selection Algorithm for Reinforcement Learning Agents with Common Interests", Proceedings of the International Joint Conference on Neural Networks, pp. 1121-1126, July 2004.
Mark Richards and Dan Ventura, "Choosing a Starting Configuration for Particle Swarm Optimization", Proceedings of the International Joint Conference on Neural Networks, pp. 2309-2312, July 2004.
Stephen Whiting and Dan Ventura, "Learning Multiple Correct Classifications from Incomplete Data using Weakened Implicit Negatives", Proceedings of the International Joint Conference on Neural Networks, pp. 2953-2958, July 2004.
Bob Ricks and Dan Ventura, "Training a Quantum Neural Network", Neural Information Processing Systems 16, pp. 1019-1026, December 2003.
Nancy Fulda and Dan Ventura, "Target Sets: A Tool for Understanding and Predicting the Behavior of Interacting Q-learners", Proceedings of the International Joint Conference on Information Sciences, pp. 1549-1552, September 2003.
Mark Richards and Dan Ventura, "Dynamic Sociometry in Particle Swarm Optimization", Proceedings of the International Joint Conference on Information Sciences, pp. 1557-1560, September 2003.
Nancy Fulda and Dan Ventura, "Dynamic Joint Action Perception for Q-Learning Agents", Proceedings of the International Conference on Machine Learning and Applications, pp. 73-78, June 2003.
Nancy Fulda and Dan Ventura, "Concurrently Learning Neural Nets: Encouraging Optimal Behavior in Cooperative Reinforcement Learning Systems", Proceedings of the IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement, and Related Applications, pp. 2-5, May 2003.
Dan Ventura, "Probabilistic Connections in Relaxation Networks", Proceedings of the International Joint Conference on Neural Networks, pp.934-938, May 2002.
Dan Ventura, "Pattern Classification Using a Quantum System", Proceedings of the Joint Conference on Information Sciences, pp.537-640, March 2002.
Dan Ventura, "A Quantum Analog to Basis Function Networks", Proceedings of the International Conference on Computing Anticipatory Systems, pp. 286-295, August 2001.
Dan Ventura, "On the Utility of Entanglement in Quantum Neural Computing", Proceedings of the International Joint Conference on Neural Networks, pp. 1565-1570, July 2001.
John Howell, John Yeazell and Dan Ventura, "Optically Simulating a Quantum Associative Memory", Physical Review A 62:42303, 2000.
Alexandr Ezhov, A. Nifanova, and Dan Ventura, "Quantum Associative Memory with Distributed Queries", Information Sciences 128(3-4):271-293, 2000.
Dan Ventura and Tony Martinez, "Quantum Associative Memory", Information Sciences 124(1-4):273-296, 2000.
Alexandr Ezhov and Dan Ventura, "Quantum Neural Networks", in Future Directions for Intelligent Systems and Information Science N. Kasabov (ed.), Physica-Verlag, pp. 213-235, 2000.
Dan Ventura, "Learning Quantum Operators", Proceedings of the Joint Conference on Information Sciences, pp. 750-752, March 2000.
Dan Ventura and Tony Martinez, "Initializing the Amplitude Distribution of a Quantum State", Foundations of Physics Letters 12(6):547-559, 1999.
Dan Ventura, "Quantum Computational Intelligence: Answers and Questions", IEEE Intelligent Systems 14(4):14-16, 1999.
Dan Ventura, "Implementing Competitive Learning in a Quantum System", Proceedings of the International Joint Conference on Neural Networks, paper 513 (CD-ROM), July 1999.
Dan Ventura, D. Randall Wilson, Tony Martinez and Brian Moncur, "A Neural Model of Centered Tri-gram Speech Recognition", Proceedings of the International Joint Conference on Neural Networks, paper 2188 (CD-ROM), July 1999.
D. Randall Wilson, Dan Ventura, Tony Martinez and Brian Moncur, "The Robustness of Relaxation Rates in Constraint Satisfaction Networks", Proceedings of the International Joint Conference on Neural Networks, paper 162 (CD-ROM), July 1999.
Dan Ventura and Tony Martinez, "A Quantum Associative Memory Based on Grover's Algorithm", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 22-27, April 1999.
Dan Ventura, "Artificial Associative Memory using Quantum Processes", Proceedings of the Joint Conference on Information Sciences, vol. 2, pp. 218-221, October 1998.
Dan Ventura and Tony Martinez, "Quantum Associative Memory with Exponential Capacity", Proceedings of the International Joint Conference on Neural Networks, pp. 509-513, May 1998.
Dan Ventura and Tony Martinez, "Optimal Control Using a Neural/Evolutionary Hybrid System", Proceedings of the International Joint Conference on Neural Networks, pp. 1036-1041, May 1998.
Dan Ventura and Tony Martinez, "Using Evolutionary Computation to Facilitate Development of Neurocontrol", Proceedings of the International Workshop on Neural Networks and Neurocontrol, August 1997.
Dan Ventura and Tony Martinez, "An Artificial Neuron with Quantum Mechanical Properties", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 482-485, April 1997.
Dan Ventura and Tony Martinez, "A General Evolutionary/Neural Hybrid Approach to Learning Optimization Problems", Proceedings of the World Congress on Neural Networks, pp. 1091-5, September 1996.
Dan Ventura and Tony Martinez, "Concerning a General Framework for the Development of Intelligent Systems", Proceedings of the International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 44-47, August 1996.
Dan Ventura and Tony Martinez, "Robust Optimization Using Training Set Evolution", Proceedings of the International Conference on Neural Networks, pp. 524-8, June 1996.
Dan Ventura, Tim Andersen and Tony Martinez, "Using Evolutionary Computation to Generate Training Set Data for Neural Networks", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 468-471, April 1995.
Dan Ventura and Tony Martinez, "An Empirical Comparison of Discretization Methods", Proceedings of the International Symposium on Computer and Information Sciences, pp. 443-450, November 1995.
Dan Ventura and Tony Martinez, "Using Multiple Statistical Prototypes to Classify Continuously Valued Data", Proceedings of the International Symposium on Neuroinformatics and Neurocomputers, pp. 238-45, September 1995.
Dan Ventura and Tony Martinez, "BRACE: A Paradigm For the Discretization of Continuously Valued Data", Proceedings of the Florida Artificial Intelligence Research Symposium, pp. 117-121, May 1994.
Other
Paul Bodily, Jonathan Armstrong, Claire Smedley-Dye and Dan Ventura, “And I Think I”, AI Song Contest, 2022 (finished 10/46 with 8.1/12+9.8/12)
Kazjon Grace, Michael Cook, Dan Ventura and Mary Lou Maher (editors), Proceedings of the Tenth International Conference on Computational Creativity, Charlotte, North Carolina, Association for Computational Creativity, June 2019
Hannu Toivonen, Simon Colton, Michael Cook and Dan Ventura (editors), Proceedings of the Sixth International Conference on Computational Creativity, Park City, Utah, Brigham Young University Press, June 2015
You Can’t Know My Mind (with Simon Colton—a festival of computational creativity that included art, portraiture, poetry, music and cooking), Galerie Oberkampf, Paris, France, July 12-19, 2013
DARCI, “Peaceful on Black 4-3”, Utah County Art Gallery Fall Photography/Digital Art Show, October 3 – November 19 – Won 2nd Place in the Digital Art Category, with a $150 prize, Fall, 2011
David Norton, Derrall Heath and Dan Ventura, “Fitness Function: Turning the Loop Inside Out”, interactive art exhibit at The High Museum, Atlanta, Georgia (as part of the Eighth ACM Conference on Creativity and Cognition), November 2011
Dan Ventura, Pablo Gervás, D. Fox Harrell, Mary Lou Maher, Alison Pease and Geraint Wiggins, Proceedings of the Second International Conference on Computational Creativity, April 2011
Dan Ventura, Alison Pease, Rafael Pérez y Pérez, Graeme Ritchie and Tony Veale, Proceedings of the First International Conference on Computational Creativity, 2010
David Norton, Derrall Heath and Dan Ventura (with Joseph Ostraff and his VASTU 480R class), Fitness Function, interactive art exhibit at the Harris Fine Arts Center, Brigham Young University, March 19-31, 2010
Marcello Balduccini, Chitta Baral, Boyan Brodaric, Simon Colton, Peter Fox, David Gutelius, Knut Hinkelmann, Ian Horswill, Bernardo Huberman, Eva Hudlicka, Kristina Lerman, Christine Lisetti, Deborah McGuinness, Mary Lou Maher, Mark A. Musen, Mehran Sahami, Derek Sleeman, Barbara Thönssen, Juan Velasquez, and Dan Ventura, "Reports of the AAAI 2008 Spring Symposia", AI Magazine
29 (3), pp. 107-115, 2008Dan Ventura, Mary Lou Maher and Simon Colton (editors), Creative Intelligent Systems: Papers from the AAAI Spring Symposium, Technical Report SS-08-03, AAAI Press, 2008
Steve Bair, Uday Chakraborty, Shu-Heng Chen, Heng-Da Cheng, David K.Y. Chiu, Sanjoy Das, Grit Denker, Richard Duro, Manuel Grana Romay, Donald Hung, Etienne Kerre, Hong Va Leong, Chang-Tien Lu, Jie Lu, Liam Maguire, Chong Wah Ngo, M. Sarfraz, Chris Tseng, Shusaku Tsumoto, Dan Ventura, Paul P. Wang, Xin Yao, C.N. Zhang, Kaizhong Zhang (Editors), Proceedings of the Eighth Joint Conference on Information Sciences, July 2005
Ken Chen, Shu-Heng Chen, Heng-Da Cheng, David K.Y. Chin, Sanjoy Das, Richard Duro, Zhen Jiang, Nik Kasabov, Etienne Kerre, Hong Va Leong, Qing Li, Mi Lu, Manuel Grana Romay, Dan Ventura, Paul P. Wang, Jie Wu (Editors), Proceedings of the Seventh Joint Conference on Information Sciences, September 2003
Dan Ventura and Tony Martinez, "Kvantovaya Accotsiativnaya Pamyat'", Neirokomp'yutery: razrabotka i primenenie, N9-10, pp. 34-53, 2002 (Russian translation of "Quantum Associative Memory" in the journal Neurocomputers: development and applications)
H. John Caulfield, Shu-Heng Chen, Heng-Da Cheng, Richard Duro, Vasant Hanovar, Etienne E. Kerre, Mi Lu, Manuel Grana Romay, Timothy K. Shih, Dan Ventura, Paul Wang, Yuanyuan Yang (Editors), Proceedings of the Sixth Joint Conference on Information Sciences, March 2002
Dan Ventura, comments in "Discussion on Neurocomputers After Ten Years" (Eds. Frolov and Ezhov), Neural Network World, vol. 1 no. 2, pp. 103-174, 1999
Dan Ventura, Quantum and Evolutionary Approaches to Computational Learning, Ph.D. Dissertation, Computer Science Department, Brigham Young University, 1998
Dan Ventura, On Discretization as a Preprocessing Step for Supervised Learning Models, Master's Thesis, Computer Science Department, Brigham Young University, 1995