6 citations · 9 across the 6 of their papers we have counts for
6 papers
StochEP: Stochastic Equilibrium Propagation for Spiking Convergent Recurrent Neural Networks
Jiaqi Lin, Yi Jiang, Abhronil Sengupta
Spiking Neural Networks (SNNs) promise energy-efficient, sparse, biologically inspired computation. Training them with Backpropagation Through Time (BPTT) and surrogate gradients a…
Scalable Equilibrium Propagation via Intermediate Error Signals for Deep Convolutional CRNNs
Jiaqi Lin, Malyaban Bal, Abhronil Sengupta
Equilibrium Propagation (EP) is a biologically inspired local learning rule first proposed for convergent recurrent neural networks (CRNNs), in which synaptic updates depend only o…
On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning
Jiaqi Lin, Abhronil Sengupta
Recent research has shown the vulnerability of Spiking Neural Networks (SNNs) under adversarial examples that are nearly indistinguishable from clean data in the context of frame-b…
Scaling SNNs Trained Using Equilibrium Propagation to Convolutional Architectures
Jiaqi Lin, Malyaban Bal, Abhronil Sengupta
Equilibrium Propagation (EP) is a biologically plausible local learning algorithm initially developed for convergent recurrent neural networks (RNNs), where weight updates rely sol…
Benchmarking Spiking Neural Network Learning Methods with Varying Locality
Jiaqi Lin, Sen Lu, Malyaban Bal +1
Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have been shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machin…
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Aidan Good, Jiaqi Lin, Hannah Sieg +5
Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that t…