Hava T. Siegelmann

150 papers A* 14A 3B 12C 3Misc 3Journal 83Unranked 26

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A novel neuromorphic engine for emotional and affective analysis by miming neurotransmitters and bioliquid intelligence. Multim. Tools Appl. Gerardo Iovane, Hava T. Siegelmann, Yossi Avni
2025	J	`jnl`	Bridging Expressivity and Scalability with Adaptive Unitary SSMs. CoRR Arjun Karuvally, Franz Nowak, Thomas A. Keller, Carmen Amo Alonso, Terrence J. Sejnowski, Hava T. Siegelmann
2025	J	`jnl`	Distributed Multi-Agent Lifelong Learning. Trans. Mach. Learn. Res. Prithviraj Tarale, Edward A. Rietman, Hava T. Siegelmann
2025	J	`jnl`	Exponential Dynamic Energy Network for High Capacity Sequence Memory. CoRR Arjun Karuvally, Pichsinee Lertsaroj, Terrence J. Sejnowski, Hava T. Siegelmann
2025	A*	`conf`	Optimizing Neural Network Representations of Boolean Networks. ICLR Joshua Russell, Ignacio Gavier, Devdhar Patel, Edward A. Rietman, Hava T. Siegelmann
2025	A*	`conf`	Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control. ICLR Devdhar Patel, Hava T. Siegelmann
2025	J	`jnl`	Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators. CoRR Alexander Yeung, Peter DelMastro, Arjun Karuvally, Hava T. Siegelmann, Edward A. Rietman, Hananel Hazan
2025	J	`jnl`	Transient Dynamics in Lattices of Differentiating Ring Oscillators. CoRR Peter DelMastro, Arjun Karuvally, Hananel Hazan, Hava T. Siegelmann, Edward A. Rietman
2024	J	`jnl`	A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 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
2024	A*	`conf`	Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. ICML Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann
2024	J	`jnl`	Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. CoRR Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann
2024	J	`jnl`	Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures. Neural Comput. Devdhar Patel, Terrence J. Sejnowski, Hava T. Siegelmann
2024	J	`jnl`	Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control. CoRR Devdhar Patel, Hava T. Siegelmann
2024	J	`jnl`	Signal Propagation: The Framework for Learning and Inference in a Forward Pass. IEEE Trans. Neural Networks Learn. Syst. Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
2023	J	`jnl`	Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networks. CoRR Arjun Karuvally, Peter DelMastro, Hava T. Siegelmann
2023	—	`conf`	Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation. TAG-ML Arjun Karuvally, Peter DelMastro, Hava T. Siegelmann
2023	A*	`conf`	General Sequential Episodic Memory Model. ICML Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann
2023	A	`conf`	Neural Network Compiler for Parallel High-Throughput Simulation of Digital Circuits. IPDPS Ignacio Gavier, Joshua Russell, Devdhar Patel, Edward A. Rietman, Hava T. Siegelmann
2023	J	`jnl`	Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment. CoRR Zhongyang Zhang, Kaidong Chai, Haowen Yu, Ramzi Majaj, Francesca Walsh, Edward Jay Wang, Upal Mahbub, Hava T. Siegelmann, Donghyun Kim, Tauhidur Rahman
2023	J	`jnl`	Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment. Neurocomputing Zhongyang Zhang, Kaidong Chai, Haowen Yu, Ramzi Majaj, Francesca Walsh, Edward Jay Wang, Upal Mahbub, Hava T. Siegelmann, Donghyun Kim, Tauhidur Rahman
2023	J	`jnl`	On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks. CoRR Peter DelMastro, Rushiv Arora, Edward A. Rietman, Hava T. Siegelmann
2023	J	`jnl`	Temporal Weights. CoRR Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
2023	A	`conf`	Temporally Layered Architecture for Adaptive, Distributed and Continuous Control. AAMAS Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrence J. Sejnowski, Hava T. Siegelmann
2023	J	`jnl`	Temporally Layered Architecture for Adaptive, Distributed and Continuous Control. CoRR Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrence J. Sejnowski, Hava T. Siegelmann
2023	J	`jnl`	Temporally Layered Architecture for Efficient Continuous Control. CoRR Devdhar Patel, Terrence J. Sejnowski, Hava T. Siegelmann
2022	B	`conf`	Automatic Transpiler that Efficiently Converts Digital Circuits to a Neural Network Representation. IJCNN Devdhar Patel, Ignacio Gavier, Joshua Russell, Andrew Malinsky, Edward A. Rietman, Hava T. Siegelmann
2022	J	`jnl`	Biological underpinnings for lifelong learning machines. Nat. Mach. Intell. 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
2022	J	`jnl`	Energy-based General Sequential Episodic Memory Networks at the Adiabatic Limit. CoRR Arjun Karuvally, Terry J. Sejnowski, Hava T. Siegelmann
2022	J	`jnl`	Forward Signal Propagation Learning. CoRR Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
2022	J	`jnl`	Memory via Temporal Delays in weightless Spiking Neural Network. CoRR Hananel Hazan, Simon Caby, Christopher Earl, Hava T. Siegelmann, Michael Levin
2022	J	`jnl`	QuickNets: Saving Training and Preventing Overconfidence in Early-Exit Neural Architectures. CoRR Devdhar Patel, Hava T. Siegelmann
2021	J	`jnl`	Replay in Deep Learning: Current Approaches and Missing Biological Elements. CoRR Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski, Christopher Kanan
2021	J	`jnl`	Replay in Deep Learning: Current Approaches and Missing Biological Elements. Neural Comput. Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski, Christopher Kanan
2021	A*	`conf`	Turing Completeness of Bounded-Precision Recurrent Neural Networks. NeurIPS Stephen Chung, Hava T. Siegelmann
2020	A*	`conf`	Abstraction Mechanisms Predict Generalization in Deep Neural Networks. ICML Alex Gain, Hava T. Siegelmann
2020	A	`conf`	Adaptive Neural Connections for Sparsity Learning. WACV Alex Gain, Prakhar Kaushik, Hava T. Siegelmann
2020	J	`jnl`	Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data. Ann. Math. Artif. Intell. Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
2020	B	`conf`	Minibatch Processing for Speed-up and Scalability of Spiking Neural Network Simulation. IJCNN Daniel J. Saunders, Cooper Sigrist, Kenneth Chaney, Robert Kozma, Hava T. Siegelmann
2020	J	`jnl`	Nanotechnology-inspired Information Processing Systems of the Future. CoRR 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
2020	J	`jnl`	Near-optimal insulin treatment for diabetes patients: A machine learning approach. Artif. Intell. Medicine Mark Shifrin, Hava T. Siegelmann
2019	J	`jnl`	Deep Neural Networks Abstract Like Humans. CoRR Alex Gain, Hava T. Siegelmann
2019	J	`jnl`	Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games. CoRR Devdhar Patel, Hananel Hazan, Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma
2019	J	`jnl`	Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game. Neural Networks Devdhar Patel, Hananel Hazan, Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma
2019	J	`jnl`	Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data. CoRR Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
2019	J	`jnl`	Locally Connected Spiking Neural Networks for Unsupervised Feature Learning. CoRR Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, Robert Kozma
2019	J	`jnl`	Locally connected spiking neural networks for unsupervised feature learning. Neural Networks Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, Robert Kozma
2019	J	`jnl`	Minibatch Processing in Spiking Neural Networks. CoRR Daniel J. Saunders, Cooper Sigrist, Kenneth Chaney, Robert Kozma, Hava T. Siegelmann
2019	B	`conf`	Models of Situated Intelligence Inspired by the Energy Management of Brains. SMC Robert Kozma, Raymond Noack, Hava T. Siegelmann
2019	—	`ch.`	Toward Modeling Regeneration via Adaptable Echo State Networks. From Parallel to Emergent Computing Jennifer Hammelman, Hava T. Siegelmann, Santosh Manicka, Michael Levin
2018	J	`jnl`	BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python. Frontiers Neuroinformatics Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Devdhar Patel, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
2018	J	`jnl`	BindsNET: A machine learning-oriented spiking neural networks library in Python. CoRR Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
2018	J	`jnl`	Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning. CoRR Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
2018	—	`conf`	Evolution of Abstraction Across Layers in Deep Learning Neural Networks. INNS Conference on Big Data Robert Kozma, Roman Ilin, Hava T. Siegelmann
2018	—	`ch.`	Neural Networks. Handbook of Approximation Algorithms and Metaheuristics (1) Bhaskar DasGupta, Derong Liu, Hava T. Siegelmann
2018	B	`conf`	STDP Learning of Image Patches with Convolutional Spiking Neural Networks. IJCNN Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó
2018	J	`jnl`	STDP Learning of Image Patches with Convolutional Spiking Neural Networks. CoRR Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó
2018	B	`conf`	Unsupervised Learning with Self-Organizing Spiking Neural Networks. IJCNN Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
2018	J	`jnl`	Unsupervised Learning with Self-Organizing Spiking Neural Networks. CoRR Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
2017	J	`jnl`	Insulin Regimen ML-based control for T2DM patients. CoRR Mark Shifrin, Hava T. Siegelmann
2017	B	`conf`	Resting state neural networks and energy metabolism. IJCNN Raymond Noack, Chetan Manjesh, Miklós Ruszinkó, Hava T. Siegelmann, Robert Kozma
2016	J	`jnl`	Preface. Theor. Comput. Sci. Hava T. Siegelmann
2015	J	`jnl`	EyeFrame: Real-Time Memory Aid Improves Human Multitasking via Domain-General Eye Tracking Procedures. Frontiers ICT P. Taylor, Noah Bilgrien, Ze He, Hava T. Siegelmann
2015	J	`jnl`	Human Strategies for Multitasking, Search, and Control Improved via Real-Time Memory Aid for Gaze Location. Frontiers ICT P. Taylor, Ze He, Noah Bilgrien, Hava T. Siegelmann
2015	B	`conf`	Implementation of universal computation via small recurrent finite precision neural networks. IJCNN J. Nicholas Hobbs, Hava T. Siegelmann
2014	—	`conf`	Development of Physical Super-Turing Analog Hardware. UCNC Arthur Steven Younger, Emmett Redd, Hava T. Siegelmann
2014	J	`jnl`	Report on UCNC 2014. Bull. EATCS Hava T. Siegelmann, Rudolf Freund
2014	J	`jnl`	The Super-Turing Computational Power of plastic Recurrent Neural Networks. Int. J. Neural Syst. Jérémie Cabessa, Hava T. Siegelmann
2013	Misc	`conf`	Multiscale Agent-based Model of Tumor Angiogenesis. ICCS Megan M. Olsen, Hava T. Siegelmann
2013	Misc	`conf`	Multiscale Agent-based Model of Tumor Angiogenesis. ICCS Megan M. Olsen, Hava T. Siegelmann
2013	J	`jnl`	Probability-Generated Aggregators. Int. J. Intell. Syst. Evgeny Kagan, Alexander N. Rybalov, Hava T. Siegelmann, Ronald R. Yager
2012	J	`jnl`	A year of neural network research: Special Issue on the 2011 International Joint Conference on Neural Networks. Neural Networks Jean-Philippe Thivierge, Ali A. Minai, Hava T. Siegelmann, Cesare Alippi, Michael Georgiopoulos
2012	J	`jnl`	Guest Editorial: Biologically Inspired Human-Robot Interactions - Developing More Natural Ways to Communicate with our Machines. IEEE Trans. Auton. Ment. Dev. Frederick C. Harris Jr., Jeffrey L. Krichmar, Hava T. Siegelmann, Hiroaki Wagatsuma
2012	J	`jnl`	The Computational Power of Interactive Recurrent Neural Networks. Neural Comput. Jérémie Cabessa, Hava T. Siegelmann
2011	—	`conf`	Activity Inference through Commonsense. AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning Kun Tu, Megan M. Olsen, Hava T. Siegelmann
2011	B	`conf`	Communicated somatic markers benefit both the individual and the species. IJCNN Kyle Ira Harrington, Megan M. Olsen, Hava T. Siegelmann
2011	B	`conf`	Evolving recurrent neural networks are super-Turing. IJCNN Jérémie Cabessa, Hava T. Siegelmann
2010	J	`jnl`	Complex Systems Science and Brain Dynamics. Frontiers Comput. Neurosci. Hava T. Siegelmann
2010	J	`jnl`	Conspecific Emotional Cooperation Biases Population Dynamics: A Cellular Automata Approach. Int. J. Nat. Comput. Res. Megan M. Olsen, Kyle Ira Harrington, Hava T. Siegelmann
2010	Misc	`conf`	Identification and control of intrinsic bias in a multiscale computational model of drug addiction. SAC Yariv Z. Levy, Dino Levy, Jerrold S. Meyer, Hava T. Siegelmann
2009	—	`conf`	Drug Addiction: A Computational Multiscale Model Combining Neuropsychology, Cognition and Behavior. BIOSIGNALS Yariv Z. Levy, Dino Levy, Jerrold S. Meyer, Hava T. Siegelmann
2009	C	`conf`	Text-based Reasoning with Symbolic Memory Model. NeSy Kun Tu, Hava T. Siegelmann
2008	—	`conf`	Emotional Robotics: Tug of War. AAAI Spring Symposium: Emotion, Personality, and Social Behavior David G. Cooper, Dov Katz, Hava T. Siegelmann
2008	—	`conf`	Emotions for Strategic Real-Time Systems. AAAI Spring Symposium: Emotion, Personality, and Social Behavior Megan M. Olsen, Kyle Ira Harrington, Hava T. Siegelmann
2008	J	`jnl`	Robust artificial life via artificial programmed death. Artif. Intell. Megan M. Olsen, N. Siegelmann-Danieli, Hava T. Siegelmann
2007	J	`jnl`	Circadian synchrony in networks of protein rhythm driven neurons. Complex. William S. Bush, Hava T. Siegelmann
2007	A*	`conf`	Multi-Agent System that Attains Longevity via Death. IJCAI Megan M. Olsen, Hava T. Siegelmann
2007	—	`ch.`	Neural Networks. Handbook of Approximation Algorithms and Metaheuristics Hava T. Siegelmann, Bhaskar DasGupta, Derong Liu
2007	J	`jnl`	The Self-Construction and -Repair of a Foraging Organism by Explicitly Specified Development from a Single Cell. Artif. Life Fabian Roth, Hava T. Siegelmann, Rodney J. Douglas
2006	—	`conf`	Adaptive Multi-modal Sensors. 50 Years of Artificial Intelligence Kyle Ira Harrington, Hava T. Siegelmann
2006	J	`jnl`	Circadian synchrony in networks of protein rhythm driven neurons. Complex. William S. Bush, Hava T. Siegelmann
2005	J	`jnl`	Introducing an active cluster-based information retrieval paradigm. J. Assoc. Inf. Sci. Technol. Oscar Loureiro, Hava T. Siegelmann
2004	J	`jnl`	On probabilistic analog automata. Theor. Comput. Sci. Asa Ben-Hur, Alexander Roitershtein, Hava T. Siegelmann
2004	—	`conf`	Time-Warped Longest Common Subsequence Algorithm for Music Retrieval. ISMIR Anyuan Guo, Hava T. Siegelmann
2003	J	`jnl`	Neural and Super-Turing Computing. Minds Mach. Hava T. Siegelmann
2003	J	`jnl`	On probabilistic analog automata CoRR Asa Ben-Hur, Alexander Roitershtein, Hava T. Siegelmann
2003	J	`jnl`	Probabilistic analysis of a differential equation for linear programming. J. Complex. Asa Ben-Hur, Joshua Feinberg, Shmuel Fishman, Hava T. Siegelmann
2003	J	`jnl`	Symbolic Processing in Neural Networks. J. Braz. Comput. Soc. João Pedro Guerreiro Neto, Hava T. Siegelmann, José Félix Costa
2002	J	`jnl`	A Theory of Complexity for Continuous Time Systems. J. Complex. Asa Ben-Hur, Hava T. Siegelmann, Shmuel Fishman
2001	—	`conf`	Active Information Retrieval. NIPS Tommi S. Jaakkola, Hava T. Siegelmann
2001	C	`conf`	Computation in Gene Networks. MCU Asa Ben-Hur, Hava T. Siegelmann
2001	J	`jnl`	Neural Computing. Bull. EATCS Hava T. Siegelmann
2001	J	`jnl`	Probabilistic analysis of a differential equation for linear programming CoRR Asa Ben-Hur, Joshua Feinberg, Shmuel Fishman, Hava T. Siegelmann
2001	J	`jnl`	Support Vector Clustering. J. Mach. Learn. Res. Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
2001	—	`conf`	Verifying Properties of Neural Networks. IWANN (1) Pedro Rodrigues, José Félix Costa, Hava T. Siegelmann
2000	B	`conf`	A Support Vector Clustering Method. ICPR Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik
2000	—	`conf`	A Support Vector Method for Clustering. NIPS Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
2000	J	`jnl`	Clustering Irregular Shapes Using High-Order Neurons. Neural Comput. Hod Lipson, Hava T. Siegelmann
2000	—	`ch.`	Finite Versus Infinite Neural Computation. Finite Versus Infinite Hava T. Siegelmann
2000	—	`conf`	Macroscopical Molecular Computation with Gene Networks. UMC Hava T. Siegelmann, Asa Ben-Hur
2000	J	`jnl`	Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials. IEEE Trans. Biomed. Eng. Daniel H. Lange, Hava T. Siegelmann, Hillel Pratt, Gideon F. Inbar
2000	J	`jnl`	Sensor registration using neural networks. IEEE Trans. Aerosp. Electron. Syst. Haim Karniely, Hava T. Siegelmann
1999	J	`jnl`	Discontinuities in Recurrent Neural Networks. Neural Comput. Ricard Gavaldà, Hava T. Siegelmann
1999	—	`book`	Neural networks and analog computation - beyond the Turing limit. Hava T. Siegelmann
1999	J	`jnl`	Nine switch-affine neurons suffice for Turing universality. Neural Networks Hava T. Siegelmann, Maurice Margenstern
1999	—	`conf`	Noisy Neural Networks and Generalizations. NIPS Hava T. Siegelmann, Alexander Roitershtein, Asa Ben-Hur
1999	J	`jnl`	Stochastic Analog Networks and Computational Complexity. J. Complex. Hava T. Siegelmann
1998	—	`conf`	A Theory of Complexity for Continuous Time Dynamics. MCU (1) Hava T. Siegelmann, Asa Ben-Hur, Shmuel Fishman
1998	—	`conf`	Attractor systems and analog computation. KES (1) Hava T. Siegelmann, Shmuel Fishman
1998	—	`conf`	High Order Eigentensors as Symbolic Rules in Competitive Learning. Hybrid Neural Systems Hod Lipson, Hava T. Siegelmann
1997	—	`conf`	A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure. NIPS Daniel H. Lange, Hava T. Siegelmann, Hillel Pratt, Gideon F. Inbar
1997	J	`jnl`	Computational capabilities of recurrent NARX neural networks. IEEE Trans. Syst. Man Cybern. Part B Hava T. Siegelmann, Bill G. Horne, C. Lee Giles
1997	J	`jnl`	Computational power of neural networks: a characterization in terms of Kolmogorov complexity. IEEE Trans. Inf. Theory José L. Balcázar, Ricard Gavaldà, Hava T. Siegelmann
1997	J	`jnl`	Multiprocessor Document Allocation: A Genetic Algorithm Approach. IEEE Trans. Knowl. Data Eng. Ophir Frieder, Hava T. Siegelmann
1997	—	`conf`	Neural Dynamics with Stochasticity. Summer School on Neural Networks Hava T. Siegelmann
1997	J	`jnl`	The complexity of language recognition by neural networks. Neurocomputing Hava T. Siegelmann, C. Lee Giles
1997	—	`conf`	Turing Universality of Neural Nets (Revisited). EUROCAST João Pedro Guerreiro Neto, Hava T. Siegelmann, José Félix Costa, Carmen Paz Suárez Araujo
1996	J	`jnl`	On NIL: the Software Constructor of Neural Networks. Parallel Process. Lett. Hava T. Siegelmann
1996	J	`jnl`	Recurrent Neural Networks and Finite Automata. Comput. Intell. Hava T. Siegelmann
1996	J	`jnl`	The Dynamic Universality of Sigmoidal Neural Networks. Inf. Comput. Joe Kilian, Hava T. Siegelmann
1996	J	`jnl`	The Simple Dynamics of Super Turing Theories. Theor. Comput. Sci. Hava T. Siegelmann
1995	—	`conf`	Input variable selection for neural networks: application to predicting the U.S. business cycle. CIFEr Joachim Utans, John E. Moody, Steven Rehfuss, Hava T. Siegelmann
1995	J	`jnl`	On the Computational Power of Neural Nets. J. Comput. Syst. Sci. Hava T. Siegelmann, Eduardo D. Sontag
1995	J	`jnl`	On the complexity of training neural networks with continuous activation functions. IEEE Trans. Neural Networks Bhaskar DasGupta, Hava T. Siegelmann, Eduardo D. Sontag
1995	—	`ch.`	Recurrent Neural Networks. Computer Science Today Hava T. Siegelmann
1995	B	`conf`	Welcoming the Super Turing Theories. SOFSEM Hava T. Siegelmann
1995	B	`conf`	What NARX Networks Can Compute. SOFSEM Bill G. Horne, Hava T. Siegelmann, C. Lee Giles
1994	—	`conf`	An integrated symbolic and neural network architecture for machine learning in the domain of nuclear engineering. ICPR (2) Ephraim Nissan, Hava T. Siegelmann, Alex Galperin
1994	J	`jnl`	Analog Computation via Neural Networks. Theor. Comput. Sci. Hava T. Siegelmann, Eduardo D. Sontag
1994	A*	`conf`	Neural Programming Language. AAAI Hava T. Siegelmann
1994	A*	`conf`	On The Computational Power of Probabilistic and Faulty Neural Networks. ICALP Hava T. Siegelmann
1994	A*	`conf`	On a Learnability Question Associated to Neural Networks with Continuous Activations (Extended Abstract). COLT Bhaskar DasGupta, Hava T. Siegelmann, Eduardo D. Sontag
1994	C	`conf`	Towards Full Automation of the Discovery of Heuristics in a Nuclear Engineering Project: Integration With a Neural Information Language. ISMIS Ephraim Nissan, Hava T. Siegelmann, Alex Galperin, Shuky Kimhi
1993	—	`conf`	Analog Computation Via Neural Networks. ISTCS Hava T. Siegelmann, Eduardo D. Sontag
1993	—	`conf`	Document Allocation In Multiprocessor Information Retrieval Systems. Advanced Database Systems Hava T. Siegelmann, Ophir Frieder
1993	A*	`conf`	On the Power of Sigmoid Neural Networks. COLT Joe Kilian, Hava T. Siegelmann
1993	—	`conf`	Some Structural Complexity Aspects of Neural Computation. SCT José L. Balcázar, Ricard Gavaldà, Hava T. Siegelmann, Eduardo D. Sontag
1992	A*	`conf`	On the Computational Power of Neural Nets. COLT Hava T. Siegelmann, Eduardo D. Sontag
1992	—	`conf`	The Complexity of Language Recognition by Neural Networks. IFIP Congress (1) Hava T. Siegelmann, Eduardo D. Sontag, C. Lee Giles
1991	A*	`conf`	Integrating Implicit Answers with Object-Oriented Queries. VLDB Hava T. Siegelmann, B. R. Badrinath
1991	A*	`conf`	On the Allocation of Documents in Multiprocessor Information Retrieval Systems. SIGIR Ophir Frieder, Hava T. Siegelmann