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Hava T. Siegelmann
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Publications
- 2024
- [j50]Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, Eric Eaton, Benjamin Epstein, Yunhao Ge, Lucy Halperin, Jonathan P. How, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Steven Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa S. Parekh, Kaushik Roy, Shahaf S. Shperberg, Hava T. Siegelmann, Peter Stone, Kyle Vedder, Jingfeng Wu, Lin Yang, Guangyao Zheng, Soheil Kolouri:
A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 6(3): 251-264 (2024) - [j49]Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann:
Signal Propagation: The Framework for Learning and Inference in a Forward Pass. IEEE Trans. Neural Networks Learn. Syst. 35(6): 8585-8596 (2024) - [c59]Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann:
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. ICML 2024 - [i25]Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann:
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. CoRR abs/2402.10163 (2024) - 2023
- [j48]Zhongyang Zhang, Kaidong Chai, Haowen Yu, Ramzi Majaj, Francesca Walsh, Edward Jay Wang, Upal Mahbub, Hava T. Siegelmann, Donghyun Kim, Tauhidur Rahman:
Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment. Neurocomputing 547: 126388 (2023) - [c58]Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrence J. Sejnowski, Hava T. Siegelmann:
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control. AAMAS 2023: 2830-2832 - [c57]Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann:
General Sequential Episodic Memory Model. ICML 2023: 15900-15910 - [c56]Ignacio Gavier, Joshua Russell, Devdhar Patel, Edward A. Rietman, Hava T. Siegelmann:
Neural Network Compiler for Parallel High-Throughput Simulation of Digital Circuits. IPDPS 2023: 613-623 - [c55]Arjun Karuvally, Peter DelMastro, Hava T. Siegelmann:
Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation. TAG-ML 2023: 371-383 - [i24]Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrence J. Sejnowski, Hava T. Siegelmann:
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control. CoRR abs/2301.00723 (2023) - [i23]Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann:
Temporal Weights. CoRR abs/2301.04126 (2023) - [i22]Zhongyang Zhang, Kaidong Chai, Haowen Yu, Ramzi Majaj, Francesca Walsh, Edward Jay Wang, Upal Mahbub, Hava T. Siegelmann, Donghyun Kim, Tauhidur Rahman:
Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment. CoRR abs/2301.06648 (2023) - [i21]Devdhar Patel, Terrence J. Sejnowski, Hava T. Siegelmann:
Temporally Layered Architecture for Efficient Continuous Control. CoRR abs/2305.18701 (2023) - [i20]Peter DelMastro, Rushiv Arora, Edward A. Rietman, Hava T. Siegelmann:
On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks. CoRR abs/2306.07125 (2023) - [i19]Arjun Karuvally, Peter DelMastro, Hava T. Siegelmann:
Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networks. CoRR abs/2310.02430 (2023) - 2022
- [j47]Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh C. Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Reddy Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, Hava T. Siegelmann:
Biological underpinnings for lifelong learning machines. Nat. Mach. Intell. 4(3): 196-210 (2022) - [c54]Devdhar Patel, Ignacio Gavier, Joshua Russell, Andrew Malinsky, Edward A. Rietman, Hava T. Siegelmann:
Automatic Transpiler that Efficiently Converts Digital Circuits to a Neural Network Representation. IJCNN 2022: 1-8 - [i18]Hananel Hazan, Simon Caby, Christopher Earl, Hava T. Siegelmann, Michael Levin:
Memory via Temporal Delays in weightless Spiking Neural Network. CoRR abs/2202.07132 (2022) - [i17]Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann:
Forward Signal Propagation Learning. CoRR abs/2204.01723 (2022) - [i16]Arjun Karuvally, Terry J. Sejnowski, Hava T. Siegelmann:
Energy-based General Sequential Episodic Memory Networks at the Adiabatic Limit. CoRR abs/2212.05563 (2022) - [i15]Devdhar Patel, Hava T. Siegelmann:
QuickNets: Saving Training and Preventing Overconfidence in Early-Exit Neural Architectures. CoRR abs/2212.12866 (2022) - 2021
- [j46]Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski, Christopher Kanan:
Replay in Deep Learning: Current Approaches and Missing Biological Elements. Neural Comput. 33(11): 2908-2950 (2021) - [c53]Stephen Chung, Hava T. Siegelmann:
Turing Completeness of Bounded-Precision Recurrent Neural Networks. NeurIPS 2021: 28431-28441 - [i14]Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski, Christopher Kanan:
Replay in Deep Learning: Current Approaches and Missing Biological Elements. CoRR abs/2104.04132 (2021) - 2020
- [j45]Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma:
Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data. Ann. Math. Artif. Intell. 88(11): 1237-1260 (2020) - [c51]Daniel J. Saunders, Cooper Sigrist, Kenneth Chaney, Robert Kozma, Hava T. Siegelmann:
Minibatch Processing for Speed-up and Scalability of Spiking Neural Network Simulation. IJCNN 2020: 1-8 - [i13]Randy Bryant, Mark D. Hill, Tom Kazior, Daniel Lee, Jie Liu, Klara Nahrstedt, Vijay Narayanan, Jan M. Rabaey, Hava T. Siegelmann, Naresh R. Shanbhag, Naveen Verma, H.-S. Philip Wong:
Nanotechnology-inspired Information Processing Systems of the Future. CoRR abs/2005.02434 (2020) - 2019
- [j43]Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, Robert Kozma:
Locally connected spiking neural networks for unsupervised feature learning. Neural Networks 119: 332-340 (2019) - [j42]Devdhar Patel, Hananel Hazan, Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma:
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game. Neural Networks 120: 108-115 (2019) - [c49]Robert Kozma, Raymond Noack, Hava T. Siegelmann:
Models of Situated Intelligence Inspired by the Energy Management of Brains. SMC 2019: 567-572 - [p4]Jennifer Hammelman, Hava T. Siegelmann, Santosh Manicka, Michael Levin:
Toward Modeling Regeneration via Adaptable Echo State Networks. From Parallel to Emergent Computing 2019: 117-134 - [i12]Devdhar Patel, Hananel Hazan, Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma:
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games. CoRR abs/1903.11012 (2019) - [i11]Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, Robert Kozma:
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning. CoRR abs/1904.06269 (2019) - [i9]Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma:
Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data. CoRR abs/1906.11826 (2019) - [i8]Daniel J. Saunders, Cooper Sigrist, Kenneth Chaney, Robert Kozma, Hava T. Siegelmann:
Minibatch Processing in Spiking Neural Networks. CoRR abs/1909.02549 (2019) - 2018
- [j41]Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Devdhar Patel, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma:
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python. Frontiers Neuroinformatics 12: 89 (2018) - [c48]Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma:
Unsupervised Learning with Self-Organizing Spiking Neural Networks. IJCNN 2018: 1-6 - [c47]Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó:
STDP Learning of Image Patches with Convolutional Spiking Neural Networks. IJCNN 2018: 1-7 - [c46]Robert Kozma, Roman Ilin, Hava T. Siegelmann:
Evolution of Abstraction Across Layers in Deep Learning Neural Networks. INNS Conference on Big Data 2018: 203-213 - [p3]Bhaskar DasGupta, Derong Liu, Hava T. Siegelmann:
Neural Networks. Handbook of Approximation Algorithms and Metaheuristics (1) 2018: 345-359 - [i7]Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma:
BindsNET: A machine learning-oriented spiking neural networks library in Python. CoRR abs/1806.01423 (2018) - [i6]Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma:
Unsupervised Learning with Self-Organizing Spiking Neural Networks. CoRR abs/1807.09374 (2018) - [i5]Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann:
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning. CoRR abs/1808.03357 (2018) - [i4]Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó:
STDP Learning of Image Patches with Convolutional Spiking Neural Networks. CoRR abs/1808.08173 (2018) - 2017
- [c45]Raymond Noack, Chetan Manjesh, Miklós Ruszinkó, Hava T. Siegelmann, Robert Kozma:
Resting state neural networks and energy metabolism. IJCNN 2017: 228-235 - 2015
- [j39]P. Taylor, Ze He, Noah Bilgrien, Hava T. Siegelmann:
Human Strategies for Multitasking, Search, and Control Improved via Real-Time Memory Aid for Gaze Location. Frontiers ICT 2: 15 (2015) - [j38]P. Taylor, Noah Bilgrien, Ze He, Hava T. Siegelmann:
EyeFrame: Real-Time Memory Aid Improves Human Multitasking via Domain-General Eye Tracking Procedures. Frontiers ICT 2: 17 (2015) - 2012
- [j33]Jean-Philippe Thivierge, Ali A. Minai, Hava T. Siegelmann, Cesare Alippi, Michael Georgiopoulos:
A year of neural network research: Special Issue on the 2011 International Joint Conference on Neural Networks. Neural Networks 32: 1-2 (2012) - [j32]Frederick C. Harris Jr., Jeffrey L. Krichmar, Hava T. Siegelmann, Hiroaki Wagatsuma:
Guest Editorial: Biologically Inspired Human-Robot Interactions - Developing More Natural Ways to Communicate with our Machines. IEEE Trans. Auton. Ment. Dev. 4(3): 190-191 (2012) - 2007
- [r1]Hava T. Siegelmann, Bhaskar DasGupta, Derong Liu:
Neural Networks. Handbook of Approximation Algorithms and Metaheuristics 2007 - 2000
- [j17]Hod Lipson, Hava T. Siegelmann:
Clustering Irregular Shapes Using High-Order Neurons. Neural Comput. 12(10): 2331-2353 (2000) - 1999
- [j13]Ricard Gavaldà, Hava T. Siegelmann:
Discontinuities in Recurrent Neural Networks. Neural Comput. 11(3): 715-745 (1999) - 1998
- [c22]Hod Lipson, Hava T. Siegelmann:
High Order Eigentensors as Symbolic Rules in Competitive Learning. Hybrid Neural Systems 1998: 286-297 - 1997
- [j11]Hava T. Siegelmann, C. Lee Giles:
The complexity of language recognition by neural networks. Neurocomputing 15(3-4): 327-345 (1997) - [j10]José L. Balcázar, Ricard Gavaldà, Hava T. Siegelmann:
Computational power of neural networks: a characterization in terms of Kolmogorov complexity. IEEE Trans. Inf. Theory 43(4): 1175-1183 (1997) - [j8]Hava T. Siegelmann, Bill G. Horne, C. Lee Giles:
Computational capabilities of recurrent NARX neural networks. IEEE Trans. Syst. Man Cybern. Part B 27(2): 208-215 (1997) - 1995
- [j3]Hava T. Siegelmann, Eduardo D. Sontag:
On the Computational Power of Neural Nets. J. Comput. Syst. Sci. 50(1): 132-150 (1995) - [j2]Bhaskar DasGupta, Hava T. Siegelmann, Eduardo D. Sontag:
On the complexity of training neural networks with continuous activation functions. IEEE Trans. Neural Networks 6(6): 1490-1504 (1995) - [c14]Bill G. Horne, Hava T. Siegelmann, C. Lee Giles:
What NARX Networks Can Compute. SOFSEM 1995: 95-102 - 1994
- [j1]Hava T. Siegelmann, Eduardo D. Sontag:
Analog Computation via Neural Networks. Theor. Comput. Sci. 131(2): 331-360 (1994) - [c12]Bhaskar DasGupta, Hava T. Siegelmann, Eduardo D. Sontag:
On a Learnability Question Associated to Neural Networks with Continuous Activations (Extended Abstract). COLT 1994: 47-56 - 1993
- [c8]José L. Balcázar, Ricard Gavaldà, Hava T. Siegelmann, Eduardo D. Sontag:
Some Structural Complexity Aspects of Neural Computation. SCT 1993: 253-265 - [c6]Hava T. Siegelmann, Eduardo D. Sontag:
Analog Computation Via Neural Networks. ISTCS 1993: 98-107 - 1992
- [c4]Hava T. Siegelmann, Eduardo D. Sontag:
On the Computational Power of Neural Nets. COLT 1992: 440-449 - [c3]Hava T. Siegelmann, Eduardo D. Sontag, C. Lee Giles:
The Complexity of Language Recognition by Neural Networks. IFIP Congress (1) 1992: 329-335
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