Search Results for author:
Found 10 papers, 1 papers with code
Explainable deep learning for insights in El Niño and river flows
The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections.
Rate-Regularization and Generalization in VAEs
Variational autoencoders optimize an objective that combines a reconstruction loss (the distortion) and a KL term (the rate).
Iterative Spectral Method for Alternative Clustering
Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition.
Streaming Adaptive Nonparametric Variational Autoencoder
We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data.
Can VAEs Generate Novel Examples?
This leads to the hypothesis that, for a sufficiently high capacity encoder and decoder, the VAE decoder will perform nearest-neighbor matching according to the coordinates in the latent space.
A Multiresolution Convolutional Neural Network with Partial Label Training for Annotating Reflectance Confocal Microscopy Images of Skin
We describe a new multiresolution "nested encoder-decoder" convolutional network architecture and use it to annotate morphological patterns in reflectance confocal microscopy (RCM) images of human skin for aiding cancer diagnosis.
Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention
Reflectance confocal microscopy (RCM) is an effective, non-invasive pre-screening tool for skin cancer diagnosis, but it requires extensive training and experience to assess accurately.
Multiple Clustering Views from Multiple Uncertain Experts
In this paper, we address the problem on how to automatically discover multiple ways to cluster data given potentially diverse inputs from multiple uncertain experts.
Multi-task Learning with Weak Class Labels: Leveraging iEEG to Detect Cortical Lesions in Cryptogenic Epilepsy
MTL methods are able to learn classification models that have higher performance as compared to learning a single model by aggregating all the data together or learning a separate model for each data source.
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning
In particular, we show that FIR can be asymptotically viewed as an upper bound of the expected variance of the log-likelihood ratio.
