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Chang Gao

4 papers hereh-index 592 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AR1
  • cs.CV1
  • cs.LG1
  • eess.SP1
same name
  • Chang Gao — 14 papers, h 7
  • Chang Gao — 10 papers, h 14
  • Chang Gao — 10 papers, h 4
  • Chang Gao — 9 papers, h 5
  • Chang Gao — 6 papers, h 7
  • Chang Gao — 4 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedEpilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer

39 citations · 51 across the 4 of their papers we have counts for

collaborators

4 papers

cs.AR2024★ 12 cited

DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM

Qinyu Chen, Kwantae Kim, Chang Gao +4

This paper introduces DeltaKWS, to the best of our knowledge, the first ΔRNN-enabled fine-grained temporal sparsity-aware KWS IC for voice-controlled devices. The 65 nm prototype…

cs.CV2024

Event-Based Eye Tracking. AIS 2024 Challenge Survey

Zuowen Wang, Chang Gao, Zongwei Wu +36

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting t…

eess.SP2024★ 39 cited

Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer

Qinyu Chen, Congyi Sun, Chang Gao +1

Epilepsy is a common disease of the nervous system. Timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect…

cs.LG2023

Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training

Xi Chen, Chang Gao, Zuowen Wang +4

Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large…

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