No. 7: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence
All Papers
Integrating Human Input for Decision Making with Informative Bayesian Beliefs
PDFPredicting When Eye Fixations Are Consistent
PDFComputational Vision for Social Intelligence
PDFHuman Learning in Atari
PDFScale Invariant Value Computation for Reinforcement Learning in Continuous Time
PDFMental Representations as Distribution-Sensitive Data Structures
PDFRepresentation Learning from Orbit Sets for One-Shot Classification
PDFTypes of Cognition and Its Implications for Future High-Level Cognitive Machines
PDFAnalysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance
PDFNew Approaches for Studying Cortical Representations
PDFLifelong Learning of Action Representations with Deep Neural Self-Organization
PDFGroup Invariant Deep Representations for Image Instance Retrieval
PDFActive Interpretation of Visual Situations
PDFFeynman Machine: A Novel Neural Architecture for Cortical and Machine Intelligence
PDFSparse Representation Learning Approach Resolves Deep Sources Underlying MEG and EEG Data
PDFContent-Dependent Fusion: Combining Human MEG and FMRI Data to Reveal Spatiotemporal Dynamics of Animacy and Real-world Object Size
PDFUnsupervised Learning via Maximizing Mutual Information in Neural Population Coding
PDFPrinciples of Noology: A Theory and Science of Intelligence for Natural and Artificial Intelligence
PDFA Model for Interpreting Social Interactions in Local Image Regions
PDFIs the Human Visual System Invariant to Translation and Scale?
PDFGetting Up to Speed on Vehicle Intelligence
PDFOn Active Video Summarization: Customized Summaries via On-Line Interaction with the User
PDFMultiple Plasticity Mechanisms Enhance Associative Memory Retrieval in a Spiking Network Model of the Hippocampus
PDFEccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision
PDFModeling the Resituation of Memory in Neurobiology and Narrative
PDFMarkov Transitions between Attractor States in a Recurrent Neural Network
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