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Found 7 papers, 1 papers with code
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks
Spiking Neural Networks (SNNs) serve as ideal paradigms to handle event camera outputs, but deep SNNs suffer in terms of performance due to the spike vanishing phenomenon.
Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures
Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm.
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks
In this work, we present, for the first time, a comprehensive analysis of the behavior of more bio-plausible networks, namely Spiking Neural Network (SNN) under state-of-the-art adversarial tests.
X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks
We rank the DNN weights and kernels based on a sensitivity analysis, and re-arrange the columns such that the most sensitive kernels are mapped closer to the drivers, thereby minimizing the impact of errors on the overall accuracy.
Emerging Technologies
Towards Understanding the Effect of Leak in Spiking Neural Networks
While the leaky models have been argued as more bioplausible, a comparative analysis between models with and without leak from a purely computational point of view demands attention.
Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures
Standard frame-based cameras that sample light intensity frames are heavily impacted by motion blur for high-speed motion and fail to perceive scene accurately when the dynamic range is high.
ChatQA: Building GPT-4 Level Conversational QA Models
In this work, we introduce ChatQA, a family of conversational question answering (QA) models that obtain GPT-4 level accuracies.
