8 citations · 10 across the 9 of their papers we have counts for
9 papers
Parameter Efficient Continual Learning for Sparse Event-Based Transformers
Vaishnavi Nagabhushana, Kartikay Agrawal, Ayon Borthakur
Robotic and edge intelligence systems operate in dynamic environments where data arrives continuously, requiring models to adapt while preserving previously learned knowledge under…
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…
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…
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…
A Study on Tiny YOLO for Resource Constrained Xray Threat Detection
Raghav Ambati, Ayon Borthakur
This paper implements and analyzes multiple networks with the goal of understanding their suitability for edge device applications such as X-ray threat detection. In this study, we…
Sapinet: A sparse event-based spatiotemporal oscillator for learning in the wild
Ayon Borthakur
We introduce Sapinet -- a spike timing (event)-based multilayer neural network for \textit{learning in the wild} -- that is: one-shot online learning of multiple inputs without cat…
Signal Conditioning for Learning in the Wild
Ayon Borthakur, Thomas A. Cleland
The mammalian olfactory system learns rapidly from very few examples, presented in unpredictable online sequences, and then recognizes these learned odors under conditions of subst…