Hava T. Siegelmann

ORCID

Publication Activity (10 Years)

Years Active: 1991-2024
Publications (10 Years): 48

Top Venues

Publications

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) (2024)
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) (2024)
Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. CoRR (2024)
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)
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)
Devdhar Patel, Terrence J. Sejnowski, Hava T. Siegelmann
Temporally Layered Architecture for Efficient Continuous Control. CoRR (2023)
Zhongyang Zhang, Kaidong Chai, Haowen Yu, Ramzi Majaj, Francesca Walsh, Edward Jay Wang, Upal Mahbub, Hava T. Siegelmann, Donghyun Kim, Tauhidur Rahman
YeLan: Event Camera-Based 3D Human Pose Estimation for Technology-Mediated Dancing in Challenging Environments with Comprehensive Motion-to-Event Simulator. CoRR (2023)
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)
Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrance J. Sejnowski, Hava T. Siegelmann
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control. CoRR (2023)
Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
Temporal Weights. CoRR (2023)
Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann
General Sequential Episodic Memory Model. ICML (2023)
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 (2023)
Peter DelMastro, Rushiv Arora, Edward A. Rietman, Hava T. Siegelmann
On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks. CoRR (2023)
Arjun Karuvally, Peter DelMastro, Hava T. Siegelmann
Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networks. CoRR (2023)
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)
Hananel Hazan, Simon Caby, Christopher Earl, Hava T. Siegelmann, Michael Levin
Memory via Temporal Delays in weightless Spiking Neural Network. CoRR (2022)
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) (2022)
Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
Forward Signal Propagation Learning. CoRR (2022)
Arjun Karuvally, Terry J. Sejnowski, Hava T. Siegelmann
Energy-based General Sequential Episodic Memory Networks at the Adiabatic Limit. CoRR (2022)
Devdhar Patel, Hava T. Siegelmann
QuickNets: Saving Training and Preventing Overconfidence in Early-Exit Neural Architectures. CoRR (2022)
Stephen Chung, Hava T. Siegelmann
Turing Completeness of Bounded-Precision Recurrent Neural Networks. NeurIPS (2021)
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 (2021)
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) (2021)
Alex Gain, Prakhar Kaushik, Hava T. Siegelmann
Adaptive Neural Connections for Sparsity Learning. WACV (2020)
Alex Gain, Hava T. Siegelmann
Abstraction Mechanisms Predict Generalization in Deep Neural Networks. ICML (2020)
Mark Shifrin, Hava T. Siegelmann
Near-optimal insulin treatment for diabetes patients: A machine learning approach. Artif. Intell. Medicine 107 (2020)
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 (2020)
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) (2020)
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)
Robert Kozma, Raymond Noack, Hava T. Siegelmann
Models of Situated Intelligence Inspired by the Energy Management of Brains. SMC (2019)
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 (2019)
Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, Robert Kozma
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning. CoRR (2019)
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 (2019)
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 (2019)
Daniel J. Saunders, Cooper Sigrist, Kenneth Chaney, Robert Kozma, Hava T. Siegelmann
Minibatch Processing in Spiking Neural Networks. CoRR (2019)
Alex Gain, Hava T. Siegelmann
Deep Neural Networks Abstract Like Humans. CoRR (2019)
Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, Robert Kozma
Locally connected spiking neural networks for unsupervised feature learning. Neural Networks 119 (2019)
Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
Unsupervised Learning with Self-Organizing Spiking Neural Networks. CoRR (2018)
Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
Unsupervised Learning with Self-Organizing Spiking Neural Networks. IJCNN (2018)
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 (2018)
Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó
STDP Learning of Image Patches with Convolutional Spiking Neural Networks. IJCNN (2018)
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 (2018)
Robert Kozma, Roman Ilin, Hava T. Siegelmann
Evolution of Abstraction Across Layers in Deep Learning Neural Networks. INNS Conference on Big Data (2018)
Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning. CoRR (2018)
Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó
STDP Learning of Image Patches with Convolutional Spiking Neural Networks. CoRR (2018)
Raymond Noack, Chetan Manjesh, Miklós Ruszinkó, Hava T. Siegelmann, Robert Kozma
Resting state neural networks and energy metabolism. IJCNN (2017)
Mark Shifrin, Hava T. Siegelmann
Insulin Regimen ML-based control for T2DM patients. CoRR (2017)
Hava T. Siegelmann
Preface. Theor. Comput. Sci. 633 (2016)
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 (2015)
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 (2015)
J. Nicholas Hobbs, Hava T. Siegelmann
Implementation of universal computation via small recurrent finite precision neural networks. IJCNN (2015)
Arthur Steven Younger, Emmett Redd, Hava T. Siegelmann
Development of Physical Super-Turing Analog Hardware. UCNC (2014)
Jérémie Cabessa, Hava T. Siegelmann
The Super-Turing Computational Power of plastic Recurrent Neural Networks. Int. J. Neural Syst. 24 (8) (2014)
Hava T. Siegelmann, Rudolf Freund
Report on UCNC 2014. Bull. EATCS 114 (2014)
Megan M. Olsen, Hava T. Siegelmann
Multiscale Agent-based Model of Tumor Angiogenesis. ICCS (2013)
Evgeny Kagan, Alexander N. Rybalov, Hava T. Siegelmann, Ronald R. Yager
Probability-Generated Aggregators. Int. J. Intell. Syst. 28 (7) (2013)
Megan M. Olsen, Hava T. Siegelmann
Multiscale Agent-based Model of Tumor Angiogenesis. ICCS (2013)
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) (2012)
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 (2012)
Jérémie Cabessa, Hava T. Siegelmann
The Computational Power of Interactive Recurrent Neural Networks. Neural Comput. 24 (4) (2012)
Jérémie Cabessa, Hava T. Siegelmann
Evolving recurrent neural networks are super-Turing. IJCNN (2011)
Kun Tu, Megan M. Olsen, Hava T. Siegelmann
Activity Inference through Commonsense. AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning (2011)
Kyle Ira Harrington, Megan M. Olsen, Hava T. Siegelmann
Communicated somatic markers benefit both the individual and the species. IJCNN (2011)
Hava T. Siegelmann
Complex Systems Science and Brain Dynamics. Frontiers Comput. Neurosci. 4 (2010)
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) (2010)
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)
Kun Tu, Hava T. Siegelmann
Text-based Reasoning with Symbolic Memory Model. NeSy (2009)
Yariv Z. Levy, Dino Levy, Jerrold S. Meyer, Hava T. Siegelmann
Drug Addiction: A Computational Multiscale Model Combining Neuropsychology, Cognition and Behavior. BIOSIGNALS (2009)
Megan M. Olsen, N. Siegelmann-Danieli, Hava T. Siegelmann
Robust artificial life via artificial programmed death. Artif. Intell. 172 (6-7) (2008)
Megan M. Olsen, Kyle Ira Harrington, Hava T. Siegelmann
Emotions for Strategic Real-Time Systems. AAAI Spring Symposium: Emotion, Personality, and Social Behavior (2008)
David G. Cooper, Dov Katz, Hava T. Siegelmann
Emotional Robotics: Tug of War. AAAI Spring Symposium: Emotion, Personality, and Social Behavior (2008)
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) (2007)
William S. Bush, Hava T. Siegelmann
Circadian synchrony in networks of protein rhythm driven neurons. Complex. 12 (6) (2007)
Megan M. Olsen, Hava T. Siegelmann
Multi-Agent System that Attains Longevity via Death. IJCAI (2007)
Kyle Ira Harrington, Hava T. Siegelmann
Adaptive Multi-modal Sensors. 50 Years of Artificial Intelligence (2006)
William S. Bush, Hava T. Siegelmann
Circadian synchrony in networks of protein rhythm driven neurons. Complex. 12 (1) (2006)
Oscar Loureiro, Hava T. Siegelmann
Introducing an active cluster-based information retrieval paradigm. J. Assoc. Inf. Sci. Technol. 56 (10) (2005)
Asa Ben-Hur, Alexander Roitershtein, Hava T. Siegelmann
On probabilistic analog automata. Theor. Comput. Sci. 320 (2-3) (2004)
AnYuan Guo, Hava T. Siegelmann
Time-Warped Longest Common Subsequence Algorithm for Music Retrieval. ISMIR (2004)
Hava T. Siegelmann
Neural and Super-Turing Computing. Minds Mach. 13 (1) (2003)
Asa Ben-Hur, Joshua Feinberg, Shmuel Fishman, Hava T. Siegelmann
Probabilistic analysis of a differential equation for linear programming. J. Complex. 19 (4) (2003)
Asa Ben-Hur, Alexander Roitershtein, Hava T. Siegelmann
On probabilistic analog automata CoRR (2003)
João Pedro Guerreiro Neto, Hava T. Siegelmann, José Félix Costa
Symbolic Processing in Neural Networks. J. Braz. Comput. Soc. 8 (3) (2003)
Asa Ben-Hur, Hava T. Siegelmann, Shmuel Fishman
A Theory of Complexity for Continuous Time Systems. J. Complex. 18 (1) (2002)
Hava T. Siegelmann
Neural Computing. Bull. EATCS 73 (2001)
Tommi S. Jaakkola, Hava T. Siegelmann
Active Information Retrieval. NIPS (2001)
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
Support Vector Clustering. J. Mach. Learn. Res. 2 (2001)
Pedro Rodrigues, José Félix Costa, Hava T. Siegelmann
Verifying Properties of Neural Networks. IWANN (1) (2001)
Asa Ben-Hur, Hava T. Siegelmann
Computation in Gene Networks. MCU (2001)
Asa Ben-Hur, Joshua Feinberg, Shmuel Fishman, Hava T. Siegelmann
Probabilistic analysis of a differential equation for linear programming CoRR (2001)
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
A Support Vector Method for Clustering. NIPS (2000)
Hod Lipson, Hava T. Siegelmann
Clustering Irregular Shapes Using High-Order Neurons. Neural Comput. 12 (10) (2000)
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) (2000)
Hava T. Siegelmann, Asa Ben-Hur
Macroscopical Molecular Computation with Gene Networks. UMC (2000)
Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik
A Support Vector Clustering Method. ICPR (2000)
Haim Karniely, Hava T. Siegelmann
Sensor registration using neural networks. IEEE Trans. Aerosp. Electron. Syst. 36 (1) (2000)
Ricard Gavaldà, Hava T. Siegelmann
Discontinuities in Recurrent Neural Networks. Neural Comput. 11 (3) (1999)
Hava T. Siegelmann, Alexander Roitershtein, Asa Ben-Hur
Noisy Neural Networks and Generalizations. NIPS (1999)
Hava T. Siegelmann
Stochastic Analog Networks and Computational Complexity. J. Complex. 15 (4) (1999)
Hava T. Siegelmann, Maurice Margenstern
Nine switch-affine neurons suffice for Turing universality. Neural Networks 12 (4-5) (1999)

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Publication Activity (10 Years)

Top Topics

Top Venues

Hava T. Siegelmann