9 citations · 24 across the 8 of their papers we have counts for
4 papers · 1 filter
Boosted Dynamic Neural Networks
Haichao Yu, Haoxiang Li, Gang Hua +2
Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different la…
Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength
Haichao Yu, Zhe Chen, Dong Lin +2
Dropout has been commonly used to quantify prediction uncertainty, i.e, the variations of model predictions on a given input example. However, using dropout in practice can be expe…
Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain Calibration for Network Quantization
Haichao Yu, Linjie Yang, Humphrey Shi
Post-training quantization methods use a set of calibration data to compute quantization ranges for network parameters and activations. The calibration data usually comes from the…
Any-Precision Deep Neural Networks
Haichao Yu, Haoxiang Li, Honghui Shi +2
We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The s…