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.
The paper classifies soil texture using 1D CNNs on hyperspectral data.
The paper proposes a soil pH prediction method using nearest fields.
Data mining involves the systematic analysis of large data sets, and data mining in agricultural soil datasets is exciting and modern research area. The productive capacity of a soil depends on soil fertility. Achieving and maintaining appropriate levels of soil fertility, is of utmost importance if agricultural land i…
Agricultural research has been profited by technical advances such as automation, data mining. Today, data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively…
SoilingNet detects soiling on automotive cameras for better autonomous driving performance.
Machine learning predicts plant phenotypes from soil microbiome data.
Gene expression programming predicts compression index of fine-grained soils efficiently.
New method predicts soil moisture with uncertainty estimates.
Study uses machine learning to predict soil organic carbon content in northern Iran.
Improved prediction of soil parameters using Multi-target Stacked Generalisation on EDXRF spectra.
In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, LWIR, and soil-moisture data conducted on grassland site. W…
Generative model creates user-specified textures from datasets.
Paper proposes TBSD for efficient anomaly detection in textured images.
A new histogram layer improves texture analysis performance.
Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector …
This paper presents a novel framework for generating texture mosaics with convolutional neural networks. Our method is called GANosaic and performs optimization in the latent noise space of a generative texture model, which allows the transformation of a content image into a mosaic exhibiting the visual properties of t…
This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has sev…
GOTEX synthesizes textures by optimizing feature distributions using optimal transport.
Improves CNN robustness by reducing texture bias.
We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-the-art on texture synthesis and inpainting by parametric models. We also develop a novel RBM model with a spike-and-slab visible layer and bi…
Few-shot image classification is improved by correcting CNNs' texture bias.
The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedule. Utilizing a state-of-the-art time-series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SM…
Improved texture synthesis using wavelet-based statistics with rectifier non-linearity.
Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure b…
Macrocanonical models generate textures matching input features.
A new liquid crystalline texture is proposed using gnomonic projection of the Hopf fibration.
Model for directed synthesis of audio textures using multi-scale RNNs.
A hybrid model combines machine learning with a land surface model to improve soil moisture predictions.
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on…
Machine learning and complexity-entropy methods estimate liquid crystal properties from textures.
A new Bayesian model improves dynamic texture segmentation.
Geotechnics adopts data-driven methods from materials informatics.
Enhanced rotation prediction improves SSL models by capturing both shape and texture information.
SwiGAN generates drought scenarios for climate risk management.
Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring change…
Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the …
Paper compares WEMI target detection algorithms for weak signals.
Deep learning boosts micro-CT image resolution and texture recovery.
Algorithm segments glandular structures in colon histology images for cancer grading.
Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model, Visual Object Networks (VON), synthesizing natural images of o…
We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous approaches, our encoding feature vector is largely…
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset an…
Machine learning speeds up LSM parameter optimization and uncertainty quantification.
This work improves texture segmentation by automatically tuning hyperparameters for Total-Variation.
AT-CNNs show improved shape recognition over texture recognition.
Enhances sound texture in CNN for better acoustic scene classification.
Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthesis and voice conversion based on two mechanisms: approximate inversion of the representation learned by a speech recognition neural network,…
Hybrid models combine domain knowledge and data-driven learning for Earth observation.