Human-level concept learning through probabilistic program induction.
about
Toward an Integration of Deep Learning and NeuroscienceA method for exploring implicit concept relatedness in biomedical knowledge networkInteractive machine learning for health informatics: when do we need the human-in-the-loop?Hybrid computing using a neural network with dynamic external memoryBuilding Machines That Learn and Think Like PeopleThe Predictive Processing Paradigm Has Roots in Kant.Adaptive Local Spatiotemporal Features from RGB-D Data for One-Shot Learning Gesture Recognition.Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach.Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approachUnsupervised feature learning from finite data by message passing: Discontinuous versus continuous phase transition.Multiple processes in two-dimensional visual statistical learningLow Data Drug Discovery with One-Shot Learning.PEDLA: predicting enhancers with a deep learning-based algorithmic framework.Progress in Fully Automated Abdominal CT Interpretation.Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity.Classical Statistics and Statistical Learning in Imaging Neuroscience.A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs.Consensus queries in ligand-based virtual screening experiments.BACS: The Brussels Artificial Character Sets for studies in cognitive psychology and neuroscience.Ingredients of intelligence: From classic debates to an engineering roadmap.Building machines that adapt and compute like brains.Causal generative models are just a start.Building machines that learn and think for themselves.Continuous track paths reveal additive evidence integration in multistep decision making.Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model.Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.A machine learning model with human cognitive biases capable of learning from small and biased datasets.Quantum Image Processing and Its Application to Edge Detection: Theory and ExperimentTheoretical perspectives on active sensing“Are Machines Better Than Humans in Image Tagging?” - A User Study Adds to the PuzzleFuture Directions in Machine LearningFinding strong lenses in CFHTLS using convolutional neural networks
P2860
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P2860
Human-level concept learning through probabilistic program induction.
description
2015 nî lūn-bûn
@nan
2015 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
Human-level concept learning through probabilistic program induction.
@ast
Human-level concept learning through probabilistic program induction.
@en
Human-level concept learning through probabilistic program induction.
@nl
type
label
Human-level concept learning through probabilistic program induction.
@ast
Human-level concept learning through probabilistic program induction.
@en
Human-level concept learning through probabilistic program induction.
@nl
prefLabel
Human-level concept learning through probabilistic program induction.
@ast
Human-level concept learning through probabilistic program induction.
@en
Human-level concept learning through probabilistic program induction.
@nl
P2860
P356
P1433
P1476
Human-level concept learning through probabilistic program induction.
@en
P2093
Brenden M Lake
P2860
P304
P356
10.1126/SCIENCE.AAB3050
P407
P577
2015-12-01T00:00:00Z