A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
SidAE combines autoencoders and Siamese networks for self-supervised feature extraction.
Enhanced financial forecasting using supervised autoencoders with noise augmentation and triple labeling.
Autoencoders are widely used for unsupervised learning and as a regularization scheme in semi-supervised learning. However, theoretical understanding of their generalization properties and of the manner in which they can assist supervised learning has been lacking. We utilize recent advances in the theory of deep learn…
Enhances supervised visualization for unseen data using autoencoders and random forest.
This paper investigates semi-supervised hashing methods using variational autoencoders.
Enhanced financial forecasting with supervised autoencoders for S&P 500 and cryptocurrencies.
Paper proposes a new autoencoder metric for balanced learning in imbalanced tabular datasets.
Deep Autoencoder outperforms in anomaly detection for building energy data.
We show how a deep denoising autoencoder with lateral connections can be used as an auxiliary unsupervised learning task to support supervised learning. The proposed model is trained to minimize simultaneously the sum of supervised and unsupervised cost functions by back-propagation, avoiding the need for layer-wise pr…
New JSA autoencoders tackle discrete latent variable models for semi-supervised learning.
AEALT uses autoencoders to reduce text embedding dimensions for improved efficiency.
This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, this then allows us t…
BSAC improves credit scoring models by leveraging autoencoders and addressing imbalanced datasets.
In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explore the use of neural networks as an alternative to a popular speech variance model based on supervised non-negative matrix factorization (NMF). More precisely, we use a variational autoencoder …
Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings. This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional. We motivate and formalize the general framework of ta…
We present a new flavor of Variational Autoencoder (VAE) that interpolates seamlessly between unsupervised, semi-supervised and fully supervised learning domains. We show that unlabeled datapoints not only boost unsupervised tasks, but also the classification performance. Vice versa, every label not only improves class…
We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is …
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects the known anomalies included in training data, but it cannot detect the unk…
Domain adaptation aims to exploit the knowledge in source domain to promote the learning tasks in target domain, which plays a critical role in real-world applications. Recently, lots of deep learning approaches based on autoencoders have achieved a significance performance in domain adaptation. However, most existing …
MoCA uses a novel autoencoder to analyze multi-modal health data.
Method separates data into class and style factors using semi-supervised learning.
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sp…
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sp…
Contrastive learning outperforms autoencoders and GANs in feature recovery and downstream tasks.
Weak supervision enables learning causal representations from unstructured data.
New autoencoder learns structured representations without regularization.
We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes. This development allows us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian nonparametric version of the variational autoencoder that has a latent representation with stocha…
Method generates natural ECGs with 25 interpretable features.
In this paper we address speaker-independent multichannel speech enhancement in unknown noisy environments. Our work is based on a well-established multichannel local Gaussian modeling framework. We propose to use a neural network for modeling the speech spectro-temporal content. The parameters of this supervised model…
Graph semi-supervised learning classifies points on manifold using variational autoencoders and GNN.
We solve a high-dimensional model where nonlinear autoencoders detect hidden structure missed by PCA.
Proposes a VAE with Student- mixture model for authorship attribution.
Proposes a new VAE framework for anomaly detection in time series data.
Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori. In practical applications, however, obtaining accurate labels based on real-time bearing conditions can be far more…
AugmentedPCA improves PCA with supervised or adversarial objectives.
The Information Plane theory predicts autoencoders do not compress input information.
We develop a novel probabilistic generative model based on the variational autoencoder approach. Notable aspects of our architecture are: a novel way of specifying the latent variables prior, and the introduction of an ordinality enforcing unit. We describe how to do supervised, unsupervised and semi-supervised learnin…
In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions. We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoe…
Centroid-Encoder reduces high-dimensional data for better visualization.
New technique learns causally disentangled representations for better generation.
Active learning improves soft sensor development by suggesting informative labels.
This paper tackles unsupervised speech enhancement using RVAE and proposes efficient sampling methods.
Autoencoders identify brain networks linked to stress and genotype.
EXoN creates an explainable latent space for semi-supervised learning.
New method disentangles hidden data structures using HSIC and supervision.
daep learns from irregular, multimodal astronomical data.
We introduce TzK (pronounced "task"), a conditional probability flow-based model that exploits attributes (e.g., style, class membership, or other side information) in order to learn tight conditional prior around manifolds of the target observations. The model is trained via approximated ML, and offers efficient appro…
Data-driven fault diagnostics of safety-critical systems often faces the challenge of a complete lack of labeled data associated with faulty system conditions (i.e., fault types) at training time. Since an unknown number and nature of fault types can arise during deployment, data-driven fault diagnostics in this scenar…