88 citations · 255 across the 45 of their papers we have counts for
5 papers · 1 filter
NEAT: Non-linearity Aware Training for Accurate and Energy-Efficient Implementation of Neural Networks on 1T-1R Memristive Crossbars
Abhiroop Bhattacharjee, Lakshya Bhatnagar, Youngeun Kim +1
Memristive crossbars suffer from non-idealities (such as, sneak paths) that degrade computational accuracy of the Deep Neural Networks (DNNs) mapped onto them. A 1T-1R synapse, add…
Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch
Youngeun Kim, Priyadarshini Panda
Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield…
Associative Partial Domain Adaptation
Youngeun Kim, Sungeun Hong, Seunghan Yang +3
Partial Adaptation (PDA) addresses a practical scenario in which the target domain contains only a subset of classes in the source domain. While PDA should take into account both c…
Domain Adaptation without Source Data
Youngeun Kim, Donghyeon Cho, Kyeongtak Han +2
Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such an assumption is rarely plausible in the real-wor…
Partial Domain Adaptation Using Graph Convolutional Networks
Seunghan Yang, Youngeun Kim, Dongki Jung +1
Partial domain adaptation (PDA), in which we assume the target label space is included in the source label space, is a general version of standard domain adaptation. Since the targ…