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Home / Proceedings / Papers from the 2017 AAAI Spring Symposium /

No. 7: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence

All Papers

  • Integrating Human Input for Decision Making with Informative Bayesian Beliefs

    Robert Lew, Hongsheng Wu, Chen-Hsiang Yu

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  • Predicting When Eye Fixations Are Consistent

    Anna Volokitin, Michael Gygli, Xavier Boix

    PDF
  • Computational Vision for Social Intelligence

    Alessia Vignolo, Alessandra Sciutti, Francesco Rea, Nicoletta Noceti, Francesca Odone, Giulio Sandini

    PDF
  • Human Learning in Atari

    Pedro A. Tsividis, Thomas Pouncy, Jaqueline L. Xu, Joshua B. Tenenbaum, Samuel J. Gershman

    PDF
  • Scale Invariant Value Computation for Reinforcement Learning in Continuous Time

    Zoran Tiganj, Karthik H. Shankar, Marc W. Howard

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  • Mental Representations as Distribution-Sensitive Data Structures

    Zenna Tavares, Armando Solar Lezama

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  • Representation Learning from Orbit Sets for One-Shot Classification

    Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos, Tomaso Poggio

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  • Types of Cognition and Its Implications for Future High-Level Cognitive Machines

    Camilo A. Miguel Signorelli

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  • Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance

    Mona Ragab Sayed, Rosary Yuting Lim, Bappaditya Mandal, Liyuan Li, Joo Hwee Lim, Terence Sim

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  • New Approaches for Studying Cortical Representations

    Dimitris A. Pinotsis, Earl K. Miller

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  • Lifelong Learning of Action Representations with Deep Neural Self-Organization

    German Ignacio Parisi, Stefan Wermter

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  • Group Invariant Deep Representations for Image Instance Retrieval

    Olivier Morere, Antoine Veillard, Lin Jie, Julie Petta, Vijay Chandrasekhar, Tomaso Poggio

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  • Active Interpretation of Visual Situations

    Melanie Mitchell

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  • Feynman Machine: A Novel Neural Architecture for Cortical and Machine Intelligence

    Eric Laukien, Richard Crowder, Fergal Byrne

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  • Sparse Representation Learning Approach Resolves Deep Sources Underlying MEG and EEG Data

    Pavitra Krishnaswamy, Gabriel Obregon-Henao, Sheraz Khan, Behtash Babadi, Eugenio Iglesias, Matti S. Hamalainen, Patrick L. Purdon

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  • Content-Dependent Fusion: Combining Human MEG and FMRI Data to Reveal Spatiotemporal Dynamics of Animacy and Real-world Object Size

    Seyed-Mahdi Khaligh-Razavi, Radoslaw M. Cichy, Dimitrios Pantazis, Aude Oliva

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  • Unsupervised Learning via Maximizing Mutual Information in Neural Population Coding

    Wentao Huang, Kai Liu

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  • Principles of Noology: A Theory and Science of Intelligence for Natural and Artificial Intelligence

    Seng-Beng Ho

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  • A Model for Interpreting Social Interactions in Local Image Regions

    Guy Ben-Yosef, Alon Yachin, Shimon Ullman

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  • Is the Human Visual System Invariant to Translation and Scale?

    Yena Han, Gemma Roig, Gad Geiger, Tomaso Poggio

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  • Getting Up to Speed on Vehicle Intelligence

    Leilani Henrina Gilpin, Ben Ze Yuan

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  • On Active Video Summarization: Customized Summaries via On-Line Interaction with the User

    Ana Garcia del Molino, Xavier Boix, Joo-Hwee Lim, Ah-Hwee Tan

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  • Multiple Plasticity Mechanisms Enhance Associative Memory Retrieval in a Spiking Network Model of the Hippocampus

    Yansong Chua, Cheston Tan

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  • Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision

    Francis X. Chen, Gemma Roig, Leyla Isik, Xavier Boix, Tomaso Poggio

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  • Modeling the Resituation of Memory in Neurobiology and Narrative

    Beth Cardier, Larry D. Sanford, Harold T. Goranson, Patric S. Lundberg, Richard P Ciavarra, Keith Devlin, Niccolo Cassas, Alessio Erioli

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  • Markov Transitions between Attractor States in a Recurrent Neural Network

    Jeremy Bernstein, Ishita Dasgupta, David Rolnick, Haim Sompolinsky

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