arXiv research

Proposes a method to predict time series data using scale information.

New method selects diffusion scales for graph wavelets.

Study utility indifference pricing with delayed investment information in a Bachelier model.

Tensor networks reveal limitations for efficient text description but suggest potential for images.

MCFNet recovers spatial detail and fuses it with semantic information for real-time segmentation.

Proposes a new hyperprior and predictive criterion for weakly informative hyperprior in relevance vector machine.

We introduce a notion of "effective dimension" of a statistical model based on the number of cubes of size $1/\sqrt{n}$ needed to cover the model space when endowed with the Fisher Information Matrix as metric, $n$ being the number of observations. The number of observations fixes a natural scale or resolution. The eff…

Graph cross network improves graph classification accuracy.

A new deep learning framework captures multi-scale spatio-temporal dependencies.

Bayesian approach scores influential training examples for model predictions.

SIMPGEN improves SWOT SSH data interpretation by removing noise and preserving fine-scale features.

We study super--replication of European contingent claims in an illiquid market with insider information. Illiquidity is captured by quadratic transaction costs and insider information is modeled by an investor who can peek into the future. Our main result describes the scaling limit of the super--replication prices wh…

We consider strictly stationary heavy tailed time series whose finite-dimensional exponent measures are concentrated on axes, and hence their extremal properties cannot be tackled using classical multivariate regular variation that is suitable for time series with extremal dependence. We recover relevant information ab…

Topology-enhanced loss improves 3D object reconstruction from 2D images.

CrossAD detects anomalies in time series data by considering cross-scale associations and cross-window modeling.

Extracts geometric information from point-clouds for multiclass classification.

Temperature scaling improves model uncertainty but not diversity in LLMs.

Analyzing and interpreting time-dependent stochastic data requires accurate and robust density estimation. In this paper we extend the concept of normalizing flows to so-called temporal Normalizing Flows (tNFs) to estimate time dependent distributions, leveraging the full spatio-temporal information present in the data…

Method scales up ML science by measuring multiple molecules at once.

ADGAN improves risk tolerance prediction by aligning cross-domain data.

New PINN architectures learn high-frequency features using Fourier features.

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-s…

Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scal…

We introduce a notion of measuring scales for quantum abelian gauge systems. At each measuring scale a finite dimensional affine space stores information about the evaluation of the curvature on a discrete family of surfaces. Affine maps from the spaces assigned to finer scales to those assigned to coarser scales play …

New method uses KL-divergence to create non-informative priors for multivariate Gaussian.

Paper shows noisy labels can improve PLR variable selection.

We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method \textit{Kernel Fried Tensor}(KFT) and present it as a large scale forecasting tool for high dimensional data. Our re…

Paper proposes PPMM for fast estimation of large-scale OTM.

Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems. However, their bottlenecks still need to be addressed, and the advantages of multi-scale information and deep architectures have not been sufficiently exploited. In this paper, we theor…

Matched filters reveal optimal normalization methods for different market participants.

Sequence models assign probabilities to variable-length sequences such as natural language texts. The ability of sequence models to capture temporal dependence can be characterized by the temporal scaling of correlation and mutual information. In this paper, we study the mutual information of recurrent neural networks …

The condition number predicts efficient information encoding in neural units, aiding model fine-tuning.

NN-GPR improves climate model predictions by preserving fine-scale spatial information.

Paper introduces multi-scale methods to improve CATE estimation from EO data.

Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary inf…

We define the intrinsic scale at which a network begins to reveal its identity as the scale at which subgraphs in the network (created by a random walk) are distinguishable from similar sized subgraphs in a perturbed copy of the network. We conduct an extensive study of intrinsic scale for several networks, ranging fro…

Improves speaker verification for variable-duration utterances using a feature pyramid module.

We develop an entropic framework to model the dynamics of stocks and European Options. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. The objective of the paper is to lay down an alternative framework for modeling dynamic…

We apply information-based complexity analysis to support vector machine (SVM) algorithms, with the goal of a comprehensive continuous algorithmic analysis of such algorithms. This involves complexity measures in which some higher order operations (e.g., certain optimizations) are considered primitive for the purposes …

The study extracts market direction from transaction data.

The paper studies scaling limits of Wasserstein metrics on Gaussian mixture models.

Scalable kernel methods for large datasets using Fourier representations and NUFFT.

Weak lensing maps contain information beyond two-point statistics on small scales. Much recent work has tried to extract this information through a range of different observables or via nonlinear transformations of the lensing field. Here we train and apply a 2D convolutional neural network to simulated noiseless lensi…

Study finds market inefficiencies vary by time scale, with news uncertainty key.

This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compa…

Examines predictability and complexity of economic time series using symbolic dynamics and entropy.

Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there exist mutual information and correlation between price and trading volume. We are curious about fractal features of this correlation and need to know how structures in different scales translate information. To expl…

Symmetries in shrinking Ricci solitons spread outward.