| DeepSITH: Efficient learning via decomposition of what and when across time scales B Jacques, Z Tiganj, M Howard, PB Sederberg Advances in Neural Information Processing Systems 34, 27530-27541, 2021 | 7 | 2021 |
| A deep convolutional neural network that is invariant to time rescaling BG Jacques, Z Tiganj, A Sarkar, M Howard, P Sederberg International conference on machine learning, 9729-9738, 2022 | 4 | 2022 |
| Scale-invariant temporal history (sith): optimal slicing of the past in an uncertain world TA Spears, BG Jacques, MW Howard, PB Sederberg arXiv preprint arXiv:1712.07165, 2017 | 4 | 2017 |
| SITHCon: A neural network robust to variations in input scaling on the time dimension BG Jacques, Z Tiganj, A Sarkar, MW Howard, PB Sederberg arXiv preprint arXiv:2107.04616 4, 2021 | 3 | 2021 |
| Quantifying mechanisms of cognition with an experiment and modeling ecosystem ER Weichart, KP Darby, AW Fenton, BG Jacques, RP Kirkpatrick, ... Behavior Research Methods, 1-24, 2021 | 1 | 2021 |
| Representing Latent Dimensions Using Compressed Number Lines SS Maini, J Mochizuki-Freeman, CS Indi, BG Jacques, PB Sederberg, ... 2023 International Joint Conference on Neural Networks (IJCNN), 1-10, 2023 | | 2023 |
| Improving Brain Computer Interfaces Using Deep Scale-Invariant Temporal History Applied to Scalp Electroencephalogram Data G Anand, A Ansari, B Dobrenz, Y Wang, BG Jacques, PB Sederberg 2021 Systems and Information Engineering Design Symposium (SIEDS), 1-6, 2021 | | 2021 |
| Constructing compressed number lines of latent variables using a cognitive model of memory and deep neural networks SS Maini, J Mochizuki-Freeman, CS Indi, BG Jacques, PB Sederberg, ... | | |
