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Found 6 papers, 0 papers with code
AdaViPro: Region-based Adaptive Visual Prompt for Large-Scale Models Adapting
Recently, prompt-based methods have emerged as a new alternative `parameter-efficient fine-tuning' paradigm, which only fine-tunes a small number of additional parameters while keeping the original model frozen.
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS).
Deep Auto-encoder with Neural Response
In this study, we propose an integrated framework called Deep Autoencoder with Neural Response (DAE-NR), which incorporates information from ANN and the visual cortex to achieve better image reconstruction performance and higher neural representation similarity between biological and artificial neurons.
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.
Bigeminal Priors Variational auto-encoder
The likelihood-based generative models have been reported to be highly robust to the out-of-distribution (OOD) inputs and can be a detector by assuming that the model assigns higher likelihoods to the samples from the in-distribution (ID) dataset than an OOD dataset.
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs.
