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2020 – today
- 2023
[e5]Ben Goertzel
, Matt Iklé, Alexey Potapov, Denis K. Ponomaryov:
Artificial General Intelligence - 15th International Conference, AGI 2022, Seattle, WA, USA, August 19-22, 2022, Proceedings. Lecture Notes in Computer Science 13539, Springer 2023, ISBN 978-3-031-19906-6 [contents]- [i17]Ben Goertzel, Vitaly Bogdanov, Michael Duncan, Deborah Duong, Zarathustra Amadeus Goertzel, Jan Horlings, Matthew Iklé, Lucius Gregory Meredith, Alexey Potapov, Andre Luiz de Senna, Hedra Seid, Andres Suarez, Adam Vandervorst, Robert Werko:
OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond. CoRR abs/2310.18318 (2023) - 2022
- [c25]Alexey Potapov, Anatoly Belikov, Oleg Scherbakov, Vitaly Bogdanov:
General-Purpose Minecraft Agents and Hybrid AGI. AGI 2022: 75-85 - [c24]Jonathan Warrell, Alexey Potapov, Adam Vandervorst, Ben Goertzel:
A Meta-Probabilistic-Programming Language for Bisimulation of Probabilistic and Non-Well-Founded Type Systems. AGI 2022: 434-451 - [e4]Ben Goertzel, Matthew Iklé, Alexey Potapov:
Artificial General Intelligence - 14th International Conference, AGI 2021, Palo Alto, CA, USA, October 15-18, 2021, Proceedings. Lecture Notes in Computer Science 13154, Springer 2022, ISBN 978-3-030-93757-7 [contents] - [i16]Jonathan Warrell, Alexey Potapov, Adam Vandervorst, Ben Goertzel:
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems. CoRR abs/2203.15970 (2022) - 2021
- [c23]Alexey Potapov, Vitaly Bogdanov:
Univalent Foundations of AGI are (not) All You Need. AGI 2021: 184-195 - 2020
- [c22]Alexey Potapov, Oleg Scherbakov, Vitaly Bogdanov, Vita Potapova, Anatoly Belikov, Sergey Rodionov, Artem Yashenko:
Analyzing Elementary School Olympiad Math Tasks as a Benchmark for AGI. AGI 2020: 279-289 - [e3]Ben Goertzel, Aleksandr I. Panov, Alexey Potapov, Roman Yampolskiy:
Artificial General Intelligence - 13th International Conference, AGI 2020, St. Petersburg, Russia, September 16-19, 2020, Proceedings. Lecture Notes in Computer Science 12177, Springer 2020, ISBN 978-3-030-52151-6 [contents] - [i15]Anatoly Belikov, Alexey Potapov:
GoodPoint: unsupervised learning of keypoint detection and description. CoRR abs/2006.01030 (2020) - [i14]A. V. Yashchenko, Anatoly Belikov, M. V. Peterson, Alexey Potapov:
Distillation of neural network models for detection and description of key points of images. CoRR abs/2006.10502 (2020)
2010 – 2019
- 2019
- [c21]Alexey Potapov, Anatoly Belikov, Vitaly Bogdanov, Alexander Scherbatiy:
Cognitive Module Networks for Grounded Reasoning. AGI 2019: 148-158 - [i13]Alexey Potapov, Anatoly Belikov, Vitaly Bogdanov, Alexander Scherbatiy:
Differentiable Probabilistic Logic Networks. CoRR abs/1907.04592 (2019) - 2018
- [j3]Alexey Potapov:
Technological Singularity: What Do We Really Know? Inf. 9(4): 82 (2018) - [c20]Alexey Potapov, Sergey Rodionov, Maxim Peterson, Oleg Scherbakov, Innokentii Zhdanov, Nikolai Skorobogatko:
Vision System for AGI: Problems and Directions. AGI 2018: 185-195 - [c19]Alexey Potapov, Innokentii Zhdanov, Oleg Scherbakov, Nikolai Skorobogatko, Hugo Latapie, Enzo Fenoglio:
Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures. AGI 2018: 196-206 - [c18]Alexey Potapov, Sergey Rodionov, Hugo Latapie, Enzo Fenoglio:
Metric Embedding Autoencoders for Unsupervised Cross-Dataset Transfer Learning. ICANN (3) 2018: 289-299 - [c17]Alexey Potapov, Oleg Shcherbakov, Innokentii Zhdanov, Sergey Rodionov, Nikolai Skorobogatko:
HyperNets and Their Application to Learning Spatial Transformations. ICANN (1) 2018: 476-486 - [c16]Sergey Rodionov, Alexey Potapov, Hugo Latapie, Enzo Fenoglio, Maxim Peterson:
Improving Deep Models of Person Re-identification for Cross-Dataset Usage. AIAI 2018: 75-84 - [i12]Alexey Potapov, Innokentii Zhdanov, Oleg Scherbakov, Nikolai Skorobogatko, Hugo Latapie, Enzo Fenoglio:
Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures. CoRR abs/1806.06946 (2018) - [i11]Alexey Potapov, Sergey Rodionov, Maxim Peterson, Oleg Shcherbakov, Innokentii Zhdanov, Nikolai Skorobogatko:
Vision System for AGI: Problems and Directions. CoRR abs/1807.03887 (2018) - [i10]Sergey Rodionov, Alexey Potapov, Hugo Latapie, Enzo Fenoglio, Maxim Peterson:
Improving Deep Models of Person Re-identification for Cross-Dataset Usage. CoRR abs/1807.08526 (2018) - [i9]Alexey Potapov, Oleg Shcherbakov, Innokentii Zhdanov, Sergey Rodionov, Nikolai Skorobogatko:
HyperNets and their application to learning spatial transformations. CoRR abs/1807.09226 (2018) - [i8]Alexey Potapov, Sergey Rodionov, Hugo Latapie, Enzo Fenoglio:
Metric Embedding Autoencoders for Unsupervised Cross-Dataset Transfer Learning. CoRR abs/1807.10591 (2018) - [i7]Alexey Potapov, Sergey Rodionov:
Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search. CoRR abs/1809.04520 (2018) - 2017
- [c15]Alexey Potapov, Sergey Rodionov:
Genetic Algorithms with DNN-Based Trainable Crossover as an Example of Partial Specialization of General Search. AGI 2017: 101-111 - [e2]Tom Everitt, Ben Goertzel, Alexey Potapov:
Artificial General Intelligence - 10th International Conference, AGI 2017, Melbourne, VIC, Australia, August 15-18, 2017, Proceedings. Lecture Notes in Computer Science 10414, Springer 2017, ISBN 978-3-319-63702-0 [contents] - 2016
- [j2]Alexey Potapov, Vita Potapova, Maxim Peterson:
A feasibility study of an autoencoder meta-model for improving generalization capabilities on training sets of small sizes. Pattern Recognit. Lett. 80: 24-29 (2016) - [c14]Alexey Potapov, Sergey Rodionov, Vita Potapova:
Real-Time GA-Based Probabilistic Programming in Application to Robot Control. AGI 2016: 95-105 - [c13]Alexey Potapov, Vita Batishcheva:
A Generative Probabilistic Model for Leaning Complex Visual Stimuli. BICA 2016: 58-63 - [i6]Alexey Potapov:
A Step from Probabilistic Programming to Cognitive Architectures. CoRR abs/1605.01180 (2016) - 2015
- [c12]Vita Batishcheva, Alexey Potapov:
Genetic Programming on Program Traces as an Inference Engine for Probabilistic Languages. AGI 2015: 14-24 - [c11]Alexey Potapov, Vita Batishcheva, Sergey Rodionov:
Optimization Framework with Minimum Description Length Principle for Probabilistic Programming. AGI 2015: 331-340 - [e1]Jordi Bieger, Ben Goertzel, Alexey Potapov:
Artificial General Intelligence - 8th International Conference, AGI 2015, AGI 2015, Berlin, Germany, July 22-25, 2015, Proceedings. Lecture Notes in Computer Science 9205, Springer 2015, ISBN 978-3-319-21364-4 [contents] - 2014
- [j1]Alexey Potapov, Sergey Rodionov:
Universal empathy and ethical bias for artificial general intelligence. J. Exp. Theor. Artif. Intell. 26(3): 405-416 (2014) - [c10]Alexey Potapov, Sergey Rodionov:
Making Universal Induction Efficient by Specialization. AGI 2014: 133-142 - [c9]Alexey Potapov, Vita Batishcheva, Shuchao Pang:
Universalization of narrow methods: Case study on autoencoders. CCIS 2014: 302-304 - [c8]Alexey Potapov, Vita Batishcheva, Maxim Peterson:
Limited Generalization Capabilities of Autoencoders with Logistic Regression on Training Sets of Small Sizes. AIAI 2014: 256-264 - 2013
- [c7]Alexey Potapov, Sergey Rodionov:
Universal Induction with Varying Sets of Combinators. AGI 2013: 88-97 - [c6]Alexey Potapov, Oleg Scherbakov, Innokentii Zhdanov:
Practical algorithmic probability: an image inpainting example. ICMV 2013: 906719 - [i5]Alexey Potapov, Sergey Rodionov:
Universal Induction with Varying Sets of Combinators. CoRR abs/1306.0095 (2013) - [i4]Sergey Rodionov, Alexey Potapov, Yurii Vinogradov:
Direct Uncertainty Estimation in Reinforcement Learning. CoRR abs/1306.1553 (2013) - [i3]Alexey Potapov, Sergey Rodionov:
Extending Universal Intelligence Models with Formal Notion of Representation. CoRR abs/1306.1557 (2013) - [i2]Alexey Potapov, Sergey Rodionov:
Universal Empathy and Ethical Bias for Artificial General Intelligence. CoRR abs/1308.0702 (2013) - 2012
- [c5]Alexey Potapov, Sergey Rodionov:
Extending Universal Intelligence Models with Formal Notion of Representation. AGI 2012: 242-251 - [c4]Alexey Potapov, Andrew Svitenkov, Yurii Vinogradov:
Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI. AGI 2012: 252-261 - [c3]Alexey Potapov, Maxim Peterson:
A Representational MDL Framework for Improving Learning Power of Neural Network Formalisms. AIAI (1) 2012: 68-77 - [i1]Alexey Potapov, Sergey Rodionov, Andrew Myasnikov, Galymzhan Begimov:
Cognitive Bias for Universal Algorithmic Intelligence. CoRR abs/1209.4290 (2012) - 2011
- [c2]Anton N. Averkin, Igor Gurov, Maxim Peterson, Alexey Potapov:
Spectral-Differential Feature Matching and Clustering for Multi-body Motion Estimation. MVA 2011: 173-176
2000 – 2009
- 2009
- [c1]Igor Gurov, Alexey Potapov:
Investigation of OCT Images Descriptions on the Base of Representational MDL Principle. MVA 2009: 320-323
Coauthor Index
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