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.
Generative AI predicts Arctic sea ice dynamics over decades.
Neural-network emulators predict sea-level changes due to Antarctic ice melt.
New method corrects seasonal Arctic sea ice predictions with probabilistic models.
New neural network enforces mass conservation for better ice flow predictions.
Study characterizes cryospheric spectral feature space using joint PC+t-SNE approach.
New algorithms compute Koopman operators on RKHSs efficiently and accurately.
Study introduces a probabilistic framework for air-sea fluxes using neural networks.
Probabilistic programming aids in automatically dating ice cores, reducing manual error and uncertainty.
Cross-sectional "Information Coefficient" (IC) is a widely and deeply accepted measure in portfolio management. The paper gives an insight into IC in view of high-dimensional directional statistics: IC is a linear operator on the components of a centralizing-unitizing standardized random vector of next-period cross-sec…
GenUQ uses generative models to estimate uncertainty in operator learning.
The study analyzes the differences between physical and risk-neutral correlation estimates for equity baskets.
The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many diff…
Investigates the use of Information Coefficient as a stock selection model performance measure.
Proposes ICE-based metric for better understanding interactions in black-box models.
Batch Normalization (BN) techniques have been proposed to reduce the so-called Internal Covariate Shift (ICS) by attempting to keep the distributions of layer outputs unchanged. Experiments have shown their effectiveness on training deep neural networks. However, since only the first two moments are controlled in these…
Neural networks have been widely used, and most networks achieve excellent performance by stacking certain types of basic units. Compared to increasing the depth and width of the network, designing more effective basic units has become an important research topic. Inspired by the elastic collision model in physics, we …
Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shorefast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern, since the majority of …
Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.
System predicts ice formation to improve road safety.
Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity constraints while others take advantage of structured similarity information among multiple data points. In this work, we approach deep metric l…
New DR-IC estimator reduces bias and variance in OPE.
We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our algorithm is based on Neural DUDE (N-DUDE), a recently proposed neural network-based discrete denoiser, and it estimates the channel transiti…
Neural networks are increasingly used for intrusion detection on industrial control systems (ICS). With neural networks being vulnerable to adversarial examples, attackers who wish to cause damage to an ICS can attempt to hide their attacks from detection by using adversarial example techniques. In this work we address…
Decision tool helps manage biofouling risks for ships in the Baltic Sea.
Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered. In this paper, we demonstrate techniques from unsupervised learning of future video frame predict…
Study optimizes climate adaptation strategies for NYC.
Introduces injective category number for continuous maps, linking classical and contemporary research.
A grand challenge in representation learning is to learn the different explanatory factors of variation behind the high dimen- sional data. Encoder models are often determined to optimize performance on training data when the real objective is to generalize well to unseen data. Although there is enough numerical eviden…
This paper identifies and bounds ICE central moments using PO marginal central moments.
ICE-Pruning accelerates deep neural network pruning by 9.61x.
We found an easy and quick post-learning method named "Icing on the Cake" to enhance a classification performance in deep learning. The method is that we train only the final classifier again after an ordinary training is done.
Forecast dam inflow using sea surface feature weights.
Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.
Wind power, as an alternative to burning fossil fuels, is abundant and inexhaustible. To fully utilize wind power, wind farms are usually located in areas of high altitude and facing serious ice conditions, which can lead to serious consequences. Quick detection of blade ice accretion is crucial for the maintenance of …
In this paper we consider two semimartingales driven by diffusions and jumps. We allow both for finite activity and for infinite activity jump components. Given discrete observations we disentangle the {\it integrated covariation} (the covariation between the two diffusion parts, indicated by IC) from the co-jumps. Thi…
New method for online learning IC models with node-level feedback.
To restore the historical sea surface temperatures (SSTs) better, it is important to construct a good calibration model for the associated proxies. In this paper, we introduce a new model for alkenone () based on the heteroscedastic Gaussian process (GP) regression method. Our nonparametric app…
In this work, we propose a novel technique to boost training efficiency of a neural network. Our work is based on an excellent idea that whitening the inputs of neural networks can achieve a fast convergence speed. Given the well-known fact that independent components must be whitened, we introduce a novel Independent-…
We present a procedure which allows one to integrate explicitly the class of checkerboard IC-nets which has recently been introduced as a generalisation of incircular (IC) nets. The latter class of privileged congruences of lines in the plane is known to admit a great variety of geometric properties which are also pres…
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MC…
New method prevents posterior collapse in iVAE models.
New approach improves AI's handling of incomplete data.
Improved regret bounds for online convex optimization under stochastic and adversarial settings.
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
This paper detects torsion elements in homology cylinder monoids.
We consider congruences of straight lines in a plane with the combinatorics of the square grid, with all elementary quadrilaterals possessing an incircle. It is shown that all the vertices of such nets (we call them incircular or IC-nets) lie on confocal conics. Our main new results are on checkerboard IC-nets in the p…
Paper tackles imbalanced binary classification by optimizing precision and recall directly.