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ORCIDHava T. Siegelmann
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- affiliation: University of Massachusetts Amherst, USA
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2020 – today
- 2024
[j51]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)- [j50]Devdhar Patel, Terrence Sejnowski, Hava T. Siegelmann:
Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures. Neural Comput. 36(12): 2734-2763 (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 - [i26]Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann:
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. CoRR abs/2402.10163 (2024) - [i25]Devdhar Patel, Hava T. Siegelmann:
Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control. CoRR abs/2410.08979 (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) - [j44]Mark Shifrin, Hava T. Siegelmann:
Near-optimal insulin treatment for diabetes patients: A machine learning approach. Artif. Intell. Medicine 107: 101917 (2020) - [c52]Alex Gain, Hava T. Siegelmann:
Abstraction Mechanisms Predict Generalization in Deep Neural Networks. ICML 2020: 3357-3366 - [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 - [c50]Alex Gain, Prakhar Kaushik, Hava T. Siegelmann:
Adaptive Neural Connections for Sparsity Learning. WACV 2020: 3177-3182 - [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)
2010 – 2019
- 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) - [i10]Alex Gain, Hava T. Siegelmann:
Deep Neural Networks Abstract Like Humans. CoRR abs/1905.11515 (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 - [i3]Mark Shifrin, Hava T. Siegelmann:
Insulin Regimen ML-based control for T2DM patients. CoRR abs/1710.07855 (2017) - 2016
- [j40]Hava T. Siegelmann:
Preface. Theor. Comput. Sci. 633: 2-3 (2016) - 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) - [c44]J. Nicholas Hobbs, Hava T. Siegelmann:
Implementation of universal computation via small recurrent finite precision neural networks. IJCNN 2015: 1-5 - 2014
- [j37]Hava T. Siegelmann, Rudolf Freund:
Report on UCNC 2014. Bull. EATCS 114 (2014) - [j36]Jérémie Cabessa, Hava T. Siegelmann:
The Super-Turing Computational Power of plastic Recurrent Neural Networks. Int. J. Neural Syst. 24(8) (2014) - [c43]Arthur Steven Younger, Emmett Redd, Hava T. Siegelmann:
Development of Physical Super-Turing Analog Hardware. UCNC 2014: 379-391 - 2013
- [j35]Evgeny Kagan, Alexander N. Rybalov, Hava T. Siegelmann, Ronald R. Yager:
Probability-Generated Aggregators. Int. J. Intell. Syst. 28(7): 709-727 (2013) - [c42]Megan M. Olsen, Hava T. Siegelmann:
Multiscale Agent-based Model of Tumor Angiogenesis. ICCS 2013: 1016-1025 - [c41]Megan M. Olsen, Hava T. Siegelmann:
Multiscale Agent-based Model of Tumor Angiogenesis. ICCS 2013: 1026-1035 - 2012
- [j34]Jérémie Cabessa, Hava T. Siegelmann:
The Computational Power of Interactive Recurrent Neural Networks. Neural Comput. 24(4): 996-1019 (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) - 2011
- [c40]Kun Tu, Megan M. Olsen, Hava T. Siegelmann:
Activity Inference through Commonsense. AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning 2011 - [c39]Jérémie Cabessa, Hava T. Siegelmann:
Evolving recurrent neural networks are super-Turing. IJCNN 2011: 3200-3206 - [c38]Kyle Ira Harrington, Megan M. Olsen, Hava T. Siegelmann:
Communicated somatic markers benefit both the individual and the species. IJCNN 2011: 3272-3278 - 2010
- [j31]Hava T. Siegelmann:
Complex Systems Science and Brain Dynamics. Frontiers Comput. Neurosci. 4: 7 (2010) - [j30]Megan M. Olsen, Kyle Ira Harrington, Hava T. Siegelmann:
Conspecific Emotional Cooperation Biases Population Dynamics: A Cellular Automata Approach. Int. J. Nat. Comput. Res. 1(3): 51-65 (2010) - [c37]Yariv Z. Levy, Dino Levy, Jerrold S. Meyer, Hava T. Siegelmann:
Identification and control of intrinsic bias in a multiscale computational model of drug addiction. SAC 2010: 2389-2393
2000 – 2009
- 2009
- [c36]Yariv Z. Levy, Dino Levy, Jerrold S. Meyer, Hava T. Siegelmann:
Drug Addiction: A Computational Multiscale Model Combining Neuropsychology, Cognition and Behavior. BIOSIGNALS 2009: 87-94 - [c35]Kun Tu, Hava T. Siegelmann:
Text-based Reasoning with Symbolic Memory Model. NeSy 2009 - 2008
- [j29]Megan M. Olsen, N. Siegelmann-Danieli, Hava T. Siegelmann:
Robust artificial life via artificial programmed death. Artif. Intell. 172(6-7): 884-898 (2008) - [c34]David G. Cooper, Dov Katz, Hava T. Siegelmann:
Emotional Robotics: Tug of War. AAAI Spring Symposium: Emotion, Personality, and Social Behavior 2008: 23-29 - [c33]Megan M. Olsen, Kyle Ira Harrington, Hava T. Siegelmann:
Emotions for Strategic Real-Time Systems. AAAI Spring Symposium: Emotion, Personality, and Social Behavior 2008: 104-110 - 2007
- [j28]Fabian Roth, Hava T. Siegelmann, Rodney J. Douglas:
The Self-Construction and -Repair of a Foraging Organism by Explicitly Specified Development from a Single Cell. Artif. Life 13(4): 347-368 (2007) - [j27]William S. Bush, Hava T. Siegelmann:
Circadian synchrony in networks of protein rhythm driven neurons. Complex. 12(6): 46 (2007) - [c32]Megan M. Olsen, Hava T. Siegelmann:
Multi-Agent System that Attains Longevity via Death. IJCAI 2007: 1428-1433 - [r1]Hava T. Siegelmann, Bhaskar DasGupta, Derong Liu:
Neural Networks. Handbook of Approximation Algorithms and Metaheuristics 2007 - 2006
- [j26]William S. Bush, Hava T. Siegelmann:
Circadian synchrony in networks of protein rhythm driven neurons. Complex. 12(1): 67-72 (2006) - [c31]Kyle Ira Harrington, Hava T. Siegelmann:
Adaptive Multi-modal Sensors. 50 Years of Artificial Intelligence 2006: 164-173 - 2005
- [j25]Oscar Loureiro, Hava T. Siegelmann:
Introducing an active cluster-based information retrieval paradigm. J. Assoc. Inf. Sci. Technol. 56(10): 1024-1030 (2005) - 2004
- [j24]Asa Ben-Hur, Alexander Roitershtein, Hava T. Siegelmann:
On probabilistic analog automata. Theor. Comput. Sci. 320(2-3): 449-464 (2004) - [c30]AnYuan Guo, Hava T. Siegelmann:
Time-Warped Longest Common Subsequence Algorithm for Music Retrieval. ISMIR 2004 - 2003
- [j23]João Pedro Guerreiro Neto, Hava T. Siegelmann, José Félix Costa:
Symbolic Processing in Neural Networks. J. Braz. Comput. Soc. 8(3): 58- (2003) - [j22]Asa Ben-Hur, Joshua Feinberg, Shmuel Fishman, Hava T. Siegelmann:
Probabilistic analysis of a differential equation for linear programming. J. Complex. 19(4): 474-510 (2003) - [j21]Hava T. Siegelmann:
Neural and Super-Turing Computing. Minds Mach. 13(1): 103-114 (2003) - [i2]Asa Ben-Hur, Alexander Roitershtein, Hava T. Siegelmann:
On probabilistic analog automata. CoRR cs.OH/0304042 (2003) - 2002
- [j20]Asa Ben-Hur, Hava T. Siegelmann, Shmuel Fishman:
A Theory of Complexity for Continuous Time Systems. J. Complex. 18(1): 51-86 (2002) - 2001
- [j19]Hava T. Siegelmann:
Neural Computing. Bull. EATCS 73: 107-130 (2001) - [j18]Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik:
Support Vector Clustering. J. Mach. Learn. Res. 2: 125-137 (2001) - [c29]Pedro Rodrigues, José Félix Costa, Hava T. Siegelmann:
Verifying Properties of Neural Networks. IWANN (1) 2001: 158-165 - [c28]Asa Ben-Hur, Hava T. Siegelmann:
Computation in Gene Networks. MCU 2001: 11-24 - [c27]Tommi S. Jaakkola, Hava T. Siegelmann:
Active Information Retrieval. NIPS 2001: 777-784 - [i1]Asa Ben-Hur, Joshua Feinberg, Shmuel Fishman, Hava T. Siegelmann:
Probabilistic analysis of a differential equation for linear programming. CoRR cs.CC/0110056 (2001) - 2000
- [j17]Hod Lipson, Hava T. Siegelmann:
Clustering Irregular Shapes Using High-Order Neurons. Neural Comput. 12(10): 2331-2353 (2000) - [j16]Haim Karniely, Hava T. Siegelmann:
Sensor registration using neural networks. IEEE Trans. Aerosp. Electron. Syst. 36(1): 85-101 (2000) - [j15]Daniel H. Lange, Hava T. Siegelmann, Hillel Pratt, Gideon F. Inbar:
Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials. IEEE Trans. Biomed. Eng. 47(6): 822-826 (2000) - [c26]Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik:
A Support Vector Clustering Method. ICPR 2000: 2724-2727 - [c25]Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik:
A Support Vector Method for Clustering. NIPS 2000: 367-373 - [c24]Hava T. Siegelmann, Asa Ben-Hur:
Macroscopical Molecular Computation with Gene Networks. UMC 2000: 119-120 - [p2]Hava T. Siegelmann:
Finite Versus Infinite Neural Computation. Finite Versus Infinite 2000: 285-299
1990 – 1999
- 1999
- [b1]Hava T. Siegelmann:
Neural networks and analog computation - beyond the Turing limit. Progress in theoretical computer science, Birkhäuser 1999, ISBN 978-0-8176-3949-5, pp. I-XIII, 1-181 - [j14]Hava T. Siegelmann:
Stochastic Analog Networks and Computational Complexity. J. Complex. 15(4): 451-475 (1999) - [j13]Ricard Gavaldà, Hava T. Siegelmann:
Discontinuities in Recurrent Neural Networks. Neural Comput. 11(3): 715-745 (1999) - [j12]Hava T. Siegelmann, Maurice Margenstern:
Nine switch-affine neurons suffice for Turing universality. Neural Networks 12(4-5): 593-600 (1999) - [c23]Hava T. Siegelmann, Alexander Roitershtein, Asa Ben-Hur:
Noisy Neural Networks and Generalizations. NIPS 1999: 335-341 - 1998
- [c22]Hod Lipson, Hava T. Siegelmann:
High Order Eigentensors as Symbolic Rules in Competitive Learning. Hybrid Neural Systems 1998: 286-297 - [c21]Hava T. Siegelmann, Shmuel Fishman:
Attractor systems and analog computation. KES (1) 1998: 237-242 - [c20]Hava T. Siegelmann, Asa Ben-Hur, Shmuel Fishman:
A Theory of Complexity for Continuous Time Dynamics. MCU (1) 1998: 179-203 - 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) - [j9]Ophir Frieder, Hava T. Siegelmann:
Multiprocessor Document Allocation: A Genetic Algorithm Approach. IEEE Trans. Knowl. Data Eng. 9(4): 640-642 (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) - [c19]João Pedro Guerreiro Neto, Hava T. Siegelmann, José Félix Costa, Carmen Paz Suárez Araujo:
Turing Universality of Neural Nets (Revisited). EUROCAST 1997: 361-366 - [c18]Daniel H. Lange, Hava T. Siegelmann, Hillel Pratt, Gideon F. Inbar:
A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure. NIPS 1997: 901-907 - [c17]Hava T. Siegelmann:
Neural Dynamics with Stochasticity. Summer School on Neural Networks 1997: 346-369 - 1996
- [j7]Hava T. Siegelmann:
Recurrent Neural Networks and Finite Automata. Comput. Intell. 12: 567-574 (1996) - [j6]Joe Kilian, Hava T. Siegelmann:
The Dynamic Universality of Sigmoidal Neural Networks. Inf. Comput. 128(1): 48-56 (1996) - [j5]Hava T. Siegelmann:
On NIL: the Software Constructor of Neural Networks. Parallel Process. Lett. 6(4): 575-582 (1996) - [j4]Hava T. Siegelmann:
The Simple Dynamics of Super Turing Theories. Theor. Comput. Sci. 168(2): 461-472 (1996) - 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) - [c16]Joachim Utans, John E. Moody, Steven Rehfuss, Hava T. Siegelmann:
Input variable selection for neural networks: application to predicting the U.S. business cycle. CIFEr 1995: 118-122 - [c15]Hava T. Siegelmann:
Welcoming the Super Turing Theories. SOFSEM 1995: 83-94 - [c14]Bill G. Horne, Hava T. Siegelmann, C. Lee Giles:
What NARX Networks Can Compute. SOFSEM 1995: 95-102 - [p1]Hava T. Siegelmann:
Recurrent Neural Networks. Computer Science Today 1995: 29-45 - 1994
- [j1]Hava T. Siegelmann, Eduardo D. Sontag:
Analog Computation via Neural Networks. Theor. Comput. Sci. 131(2): 331-360 (1994) - [c13]Hava T. Siegelmann:
Neural Programming Language. AAAI 1994: 877-882 - [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 - [c11]Hava T. Siegelmann:
On The Computational Power of Probabilistic and Faulty Neural Networks. ICALP 1994: 23-34 - [c10]Ephraim Nissan, Hava T. Siegelmann, Alex Galperin:
An integrated symbolic and neural network architecture for machine learning in the domain of nuclear engineering. ICPR (2) 1994: 494-496 - [c9]Ephraim Nissan, Hava T. Siegelmann, Alex Galperin, Shuky Kimhi:
Towards Full Automation of the Discovery of Heuristics in a Nuclear Engineering Project: Integration With a Neural Information Language. ISMIS 1994: 427-436 - 1993
- [c8]José L. Balcázar, Ricard Gavaldà, Hava T. Siegelmann, Eduardo D. Sontag:
Some Structural Complexity Aspects of Neural Computation. SCT 1993: 253-265 - [c7]Joe Kilian, Hava T. Siegelmann:
On the Power of Sigmoid Neural Networks. COLT 1993: 137-143 - [c6]Hava T. Siegelmann, Eduardo D. Sontag:
Analog Computation Via Neural Networks. ISTCS 1993: 98-107 - [c5]Hava T. Siegelmann, Ophir Frieder:
Document Allocation In Multiprocessor Information Retrieval Systems. Advanced Database Systems 1993: 289-310 - 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 - 1991
- [c2]Ophir Frieder, Hava T. Siegelmann:
On the Allocation of Documents in Multiprocessor Information Retrieval Systems. SIGIR 1991: 230-239 - [c1]Hava T. Siegelmann, B. R. Badrinath:
Integrating Implicit Answers with Object-Oriented Queries. VLDB 1991: 15-24
Coauthor Index
Terrence J. Sejnowski
aka: Terry J. Sejnowski
aka: Terry J. Sejnowski
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