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.
FCC-GAN combines fully connected and convolutional layers for improved GAN performance.
A new convolution method stabilizes GANs by learning coarse structures first.
Perturbative GAN reduces training complexity and improves image quality.
Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator. We argue that this kind of fixed operation is problematic for GANs to model objects that have very different visual appearan…
Convolutional GANs favor low spatial frequencies, affecting fine detail generation.
RSM-GAN detects anomalies in noisy, multivariate, seasonal time series.
Proposes a fixed smooth convolutional layer to reduce checkerboard artifacts in CNNs.
Semi-supervised GANs improve travel mode inference from GPS data.
We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) setting, while modeling image data. We provide guidelines for designing CapsNet…
Over the past decade, many Super Resolution techniques have been developed using deep learning. Among those, generative adversarial networks (GAN) and very deep convolutional networks (VDSR) have shown promising results in terms of HR image quality and computational speed. In this paper, we propose two approaches based…
Generative adversarial networks with attention improve financial time series simulation.
Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two common aspects: solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discrimi…
Generative models create paintings that match training data.
The paper uses machine learning to predict cryptocurrency price changes.
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder to the network, makin…
Quant GANs model financial time series using GANs with TCNs.
Geometric models improve feature extraction and equivariance in image generation.
GANs generate samples from time series data.
Generative model generates images with multiple object classes.
A GAN-based method diagnoses faults in imbalanced industrial time series data.
In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positional-equivariance of features, Capsule Network's ability to encode spatial relationships between the features of the image helps it become a more powerful critic in…
WaveletGAN improves GANs by homogenizing noise through multi-channel wavelet filtering.
The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We emp…
The clinical management of several cardiovascular conditions, such as pulmonary hypertension, require the assessment of the right ventricular (RV) function. This work addresses the fully automatic and robust access to one of the key RV biomarkers, its ejection fraction, from the gold standard imaging modality, MRI. The…
Generative adversarial networks generate realistic, time-evolving high-resolution atmospheric fields.
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stoch…
It has been shown recently that deep convolutional generative adversarial networks (GANs) can learn to generate music in the form of piano-rolls, which represent music by binary-valued time-pitch matrices. However, existing models can only generate real-valued piano-rolls and require further post-processing, such as ha…
We introduce a generative adversarial network (GAN) model to simulate the 3-dimensional Lagrangian motion of particles trapped in the recirculation zone of a buoyancy-opposed flame. The GAN model comprises a stochastic recurrent neural network, serving as a generator, and a convoluted neural network, serving as a discr…
Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be o…
Self-supervised fine-tuning corrects SR CNNs for unseen models and artifacts.
PolyGAN uses high-order polynomials to generate data without activation functions.
Generative adversarial networks create realistic equity option market simulations.
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In …
Proposes a method to balance imbalanced image datasets using capsule-GAN.
Combining deep learning and ensemble smoothers for better history matching.
Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details whi…
CorGAN generates synthetic healthcare records while preserving privacy.
Y-GAN uses multi-camera data to estimate depth maps without expensive hardware.
New model detects anomalies robustly in noisy, seasonal multivariate time series.
This paper uses GAN and ERMSE to improve stock price movement prediction accuracy.
Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate tra…
Enhances spoken speech quality using EEG signals.
A novel generative encoder model for imaging and image processing.
New divergences improve estimation and GAN training performance.
Survey of GANs and autoencoders, addressing mode collapse and likelihood issues.
Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, few deep graph generative models have been proposed to generate discrete data such as graphs. They are typically unconditioned generative models which has no control on modes of the …
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods f…
SoilingNet detects soiling on automotive cameras for better autonomous driving performance.