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Found 5 papers, 1 papers with code
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation
In this work, we improve upon the inference latency of the state-of-the-art methods by removing the floating-point operations, which are associated with the GELU activation in Swin Transformer.
AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation
This paper describes a simple yet effective technique for refining a pretrained classifier network.
Robustness to distribution shifts of compressed networks for edge devices
It is important to investigate the robustness of compressed networks in two types of data distribution shifts: domain shifts and adversarial perturbations.
Standard Deviation-Based Quantization for Deep Neural Networks
Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices.
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
End-to-end models are an attractive new approach to spoken language understanding (SLU) in which the meaning of an utterance is inferred directly from the raw audio without employing the standard pipeline composed of a separately trained speech recognizer and natural language understanding module.
Ranked #7 on
Spoken Language Understanding
on Snips-SmartLights
