Recent breakthroughs in deep neural networks (DNNs) have motivated an explosive demand for intelligent edge devices. Many of them, such as autonomous vehicles and healthcare wearables, require real-time andon-site learning toenable them toproactivelylearn from newdataandadapt todynamic environments.

Second, on channel aspect, representation exhibits diversity ondifferent channels. But the scarce data can not enable ViTs to learn strong enough representation for accurate recognition.

Neural network quantization is one of the most effective ways to improve the efficiency of neural networks. Quantization allows weights and activations to be represented in low bit-width formats, e.g. 8 bit integers (INT8).

However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods.

Our approach requires no additional training and enables highly flexible module composition.

Our experimental results demonstrate that CUFIT outperforms previous methods across various medical image benchmarks.