5 papers
CATFormer: When Continual Learning Meets Spiking Transformers With Dynamic Thresholds
Vaishnavi Nagabhushana, Kartikay Agrawal, Ayon Borthakur
Although deep neural networks perform extremely well in controlled environments, they fail in real-world scenarios where data isn't available all at once, and the model must adapt…
Discriminative Flow Matching Via Local Generative Predictors
Om Govind Jha, Manoj Bamniya, Ayon Borthakur
Traditional discriminative computer vision relies predominantly on static projections, mapping input features to outputs in a single computational step. Although efficient, this pa…
ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma +2
Spiking neural networks have garnered increasing attention due to their energy efficiency, multiplication-free computation, and sparse event-based processing. In parallel, state sp…
Learning Using a Single Forward Pass
Aditya Somasundaram, Pushkal Mishra, Ayon Borthakur
We propose a learning algorithm to overcome the limitations of traditional backpropagation in resource-constrained environments: Solo Pass Embedded Learning Algorithm (SPELA). SPEL…
Heterogeneous quantization regularizes spiking neural network activity
Roy Moyal, Kyrus R. Mama, Matthew Einhorn +2
The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains are adept at learning stable re…