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Found 34 papers, 11 papers with code
Self Supervised Correlation-based Permutations for Multi-View Clustering
Fusing information from different modalities can enhance data analysis tasks, including clustering.
Contextual Feature Selection with Conditional Stochastic Gates
We study the problem of contextual feature selection, where the goal is to learn a predictive function while identifying subsets of informative features conditioned on specific contexts.
Efficient Verification-Based Face Identification
We study the problem of performing face verification with an efficient neural model $f$.
Gappy local conformal auto-encoders for heterogeneous data fusion: in praise of rigidity
Fusing measurements from multiple, heterogeneous, partial sources, observing a common object or process, poses challenges due to the increasing availability of numbers and types of sensors.
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
Tables are an abundant form of data with use cases across all scientific fields.
Interpretable Deep Clustering
Furthermore, we verify that our model leads to interpretable results at a sample and cluster level.
Neuronal Cell Type Classification using Deep Learning
First, we classify neuronal cell types of mice data to identify excitatory and inhibitory neurons.
Anomaly Detection with Variance Stabilized Density Estimation
Then, we design a variance stabilized density estimation problem for maximizing the likelihood of the observed samples while minimizing the variance of the density around normal samples.
Multi-modal Differentiable Unsupervised Feature Selection
Multi-modal high throughput biological data presents a great scientific opportunity and a significant computational challenge.
Revisiting the Noise Model of Stochastic Gradient Descent
Following the central limit theorem, SGN was initially modeled as Gaussian, and lately, it has been suggested that stochastic gradient noise is better characterized using $S\alpha S$ L\'evy distribution.
Domain-Generalizable Multiple-Domain Clustering
This work generalizes the problem of unsupervised domain generalization to the case in which no labeled samples are available (completely unsupervised).
Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction
Classical methods for acoustic scene mapping require the estimation of time difference of arrival (TDOA) between microphones.
SG-VAD: Stochastic Gates Based Speech Activity Detection
Our key idea is to model VAD as a denoising task, and construct a network that is designed to identify nuisance features for a speech classification task.
Ranked #3 on
Activity Detection
on AVA-Speech
(ROC-AUC metric)
Imbalanced Classification via a Tabular Translation GAN
When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class.
Imbalanced Classification via Explicit Gradient Learning From Augmented Data
Learning from imbalanced data is one of the most significant challenges in real-world classification tasks.
Support Recovery with Stochastic Gates: Theory and Application for Linear Models
Consider the problem of simultaneous estimation and support recovery of the coefficient vector in a linear data model with additive Gaussian noise.
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Experimenting on several real-world datasets, we demonstrate that our proposed approach outperforms similar approaches designed to avoid only correlated or nuisance features, but not both.
Probabilistic Robust Autoencoders for Outlier Detection
We first present the Robust AutoEncoder (RAE) objective as a minimization problem for splitting the data into inliers and outliers.
L0-Sparse Canonical Correlation Analysis
We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.
Locally Sparse Neural Networks for Tabular Biomedical Data
By forcing the model to select a subset of the most informative features for each sample, we reduce model overfitting in low-sample-size data and obtain an interpretable model.
$\ell_0$-based Sparse Canonical Correlation Analysis
We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.
Deep Gated Canonical Correlation Analysis
The proposed procedure learns two non-linear transformations and simultaneously gates the input variables to identify a subset of most correlated variables.
Kernel-based parameter estimation of dynamical systems with unknown observation functions
A low-dimensional dynamical system is observed in an experiment as a high-dimensional signal; for example, a video of a chaotic pendulums system.
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
In this paper, we present a method for unsupervised feature selection, and we demonstrate its use for the task of clustering.
LOCA: LOcal Conformal Autoencoder for standardized data coordinates
We propose a deep-learning based method for obtaining standardized data coordinates from scientific measurements. Data observations are modeled as samples from an unknown, non-linear deformation of an underlying Riemannian manifold, which is parametrized by a few normalized latent variables.
The Spectral Underpinning of word2vec
word2vec due to Mikolov \textit{et al.} (2013) is a word embedding method that is widely used in natural language processing.
Variational Diffusion Autoencoders with Random Walk Sampling
Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to approximate a homeomorphism between the data distribution and the sampling space.
Geometry Based Data Generation
We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold.
Feature Selection using Stochastic Gates
Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions.
Geometry-Based Data Generation
Then, it generates new points evenly along the manifold by pulling randomly generated points into its intrinsic structure using a diffusion kernel.
Kernel Scaling for Manifold Learning and Classification
We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.
Multi-View Kernels for Low-Dimensional Modeling of Seismic Events
The problem of learning from seismic recordings has been studied for years.
Multi-View Kernel Consensus For Data Analysis
Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set.
MultiView Diffusion Maps
The multi-view dimensionality reduction is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views.
