| 2025 | ICLR | Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control. | Devdhar Patel, Hava T. Siegelmann |
| 2025 | ICLR | Optimizing Neural Network Representations of Boolean Networks. | Joshua Russell, Ignacio Gavier, Devdhar Patel, Edward A. Rietman, Hava T. Siegelmann |
| 2024 | ICML | Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. | Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann |
| 2023 | ICML | General Sequential Episodic Memory Model. | Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann |
| 2022 | IJCNN | Automatic Transpiler that Efficiently Converts Digital Circuits to a Neural Network Representation. | Devdhar Patel, Ignacio Gavier, Joshua Russell, Andrew Malinsky, Edward A. Rietman, Hava T. Siegelmann |
| 2020 | ICML | Abstraction Mechanisms Predict Generalization in Deep Neural Networks. | Alex Gain, Hava T. Siegelmann |
| 2020 | IJCNN | Minibatch Processing for Speed-up and Scalability of Spiking Neural Network Simulation. | Daniel J. Saunders, Cooper Sigrist, Kenneth Chaney, Robert Kozma, Hava T. Siegelmann |
| 2020 | WACV | Adaptive Neural Connections for Sparsity Learning. | Alex Gain, Prakhar Kaushik, Hava T. Siegelmann |
| 2019 | SMC | Models of Situated Intelligence Inspired by the Energy Management of Brains. | Robert Kozma, Raymond Noack, Hava T. Siegelmann |
| 2018 | IJCNN | Unsupervised Learning with Self-Organizing Spiking Neural Networks. | Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma |
| 2018 | IJCNN | STDP Learning of Image Patches with Convolutional Spiking Neural Networks. | Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Mikls Ruszink |
| 2017 | IJCNN | Resting state neural networks and energy metabolism. | Raymond Noack, Chetan Manjesh, Mikls Ruszink, Hava T. Siegelmann, Robert Kozma |
| 2015 | IJCNN | Implementation of universal computation via small recurrent finite precision neural networks. | J. Nicholas Hobbs, Hava T. Siegelmann |
| 2014 | UC | Development of Physical Super-Turing Analog Hardware. | Arthur Steven Younger, Emmett Redd, Hava T. Siegelmann |
| 2013 | ICCS | Multiscale Agent-based Model of Tumor Angiogenesis. | Megan M. Olsen, Hava T. Siegelmann |
| 2013 | ICCS | Multiscale Agent-based Model of Tumor Angiogenesis. | Megan M. Olsen, Hava T. Siegelmann |
| 2011 | IJCNN | Evolving recurrent neural networks are super-Turing. | Jrmie Cabessa, Hava T. Siegelmann |
| 2011 | IJCNN | Communicated somatic markers benefit both the individual and the species. | Kyle Ira Harrington, Megan M. Olsen, Hava T. Siegelmann |
| 2010 | SAC | Identification and control of intrinsic bias in a multiscale computational model of drug addiction. | Yariv Z. Levy, Dino Levy, Jerrold S. Meyer, Hava T. Siegelmann |
| 2009 | NeSy | Text-based Reasoning with Symbolic Memory Model. | Kun Tu, Hava T. Siegelmann |
| 2007 | IJCAI | Multi-Agent System that Attains Longevity via Death. | Megan M. Olsen, Hava T. Siegelmann |
| 2001 | IWANN | Verifying Properties of Neural Networks. | Pedro Rodrigues, Jos Flix Costa, Hava T. Siegelmann |
| 2001 | MCU | Computation in Gene Networks. | Asa Ben-Hur, Hava T. Siegelmann |
| 2000 | ICPR | A Support Vector Clustering Method. | Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik |
| 1998 | KES | Attractor systems and analog computation. | Hava T. Siegelmann, Shmuel Fishman |
| 1998 | MCU | A Theory of Complexity for Continuous Time Dynamics. | Hava T. Siegelmann, Asa Ben-Hur, Shmuel Fishman |
| 1995 | SOFSEM | What NARX Networks Can Compute. | Bill G. Horne, Hava T. Siegelmann, C. Lee Giles |
| 1995 | SOFSEM | Welcoming the Super Turing Theories. | Hava T. Siegelmann |
| 1994 | AAAI | Neural Programming Language. | Hava T. Siegelmann |
| 1994 | COLT | On a Learnability Question Associated to Neural Networks with Continuous Activations (Extended Abstract). | Bhaskar DasGupta, Hava T. Siegelmann, Eduardo D. Sontag |
| 1994 | ICALP | On The Computational Power of Probabilistic and Faulty Neural Networks. | Hava T. Siegelmann |
| 1994 | ICPR | An integrated symbolic and neural network architecture for machine learning in the domain of nuclear engineering. | Ephraim Nissan, Hava T. Siegelmann, Alex Galperin |
| 1994 | ISMIS | Towards Full Automation of the Discovery of Heuristics in a Nuclear Engineering Project: Integration With a Neural Information Language. | Ephraim Nissan, Hava T. Siegelmann, Alex Galperin, Shuky Kimhi |
| 1993 | COLT | On the Power of Sigmoid Neural Networks. | Joe Kilian, Hava T. Siegelmann |
| 1992 | COLT | On the Computational Power of Neural Nets. | Hava T. Siegelmann, Eduardo D. Sontag |
| 1991 | SIGIR | On the Allocation of Documents in Multiprocessor Information Retrieval Systems. | Ophir Frieder, Hava T. Siegelmann |
| 1991 | VLDB | Integrating Implicit Answers with Object-Oriented Queries. | Hava T. Siegelmann, B. R. Badrinath |