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.
Formalizes explanations as blending input and model output.
Neural GDEs improve graph prediction by blending discrete structures and differential equations.
We introduce the framework of continuous--depth graph neural networks (GNNs). Graph neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs where the input-output relationship is determined by a continuum of GNN layers, blending discrete topological structures and differential equations.…
Deformations of a Courant Algebroid E and its Dirac subbundle A have been widely considered under the assumption that the pseudo-Euclidean metric is fixed. In this paper, we attack the same problem in a setting that allows the pseudo-Euclidean metric to deform. Thanks to Roytenberg, a Courant algebroid is equivalent to…
New model improves volatility forecasting by reducing overestimation and underestimation.
Study shows ethanol blends and incentives can significantly reduce transportation carbon emissions.
BayesBlend blends multiple models' predictions for better insurance loss predictions.
Safe neural networks for input-output specifications.
Quantum walks blend patterns into splines when averaged.
This paper improves level generation using VAEs for coherent, logically following segments.
The paper proposes blending gradient boosted trees and neural networks for hierarchical time series forecasting.
New PCGML approach generates novel game content across multiple platformer domains.
Economic systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD) is one of the first efforts to construct the multi-regional input-output (MRIO) tables at the global level. By viewing t…
The paper presents a framework for optimizing crypto-currency portfolios using generative models.
There has been a recent shift in sequence-to-sequence modeling from recurrent network architectures to convolutional network architectures due to computational advantages in training and operation while still achieving competitive performance. For systems having limited long-term temporal dependencies, the approximatio…
Unitary recurrent neural networks (URNNs) have been proposed as a method to overcome the vanishing and exploding gradient problem in modeling data with long-term dependencies. A basic question is how restrictive is the unitary constraint on the possible input-output mappings of such a network? This work shows that for …
Quantized deep neural networks (QDNNs) are attractive due to their much lower memory storage and faster inference speed than their regular full precision counterparts. To maintain the same performance level especially at low bit-widths, QDNNs must be retrained. Their training involves piecewise constant activation func…
A novel approach for augmenting histopathological images by blending Gaussian-Laplacian pyramids.
CoCoAFusE fuses expert predictions to model complex patterns with interpretability and uncertainty.
Study examines Indian equity mutual funds' investment style and risk-shifting.
PIML model improves hydrological predictions by blending physics and ML.
New approach ties loss curvature to model performance in deep learning.
OPERA blends multiple OPE estimators to evaluate new policies offline.
This paper investigates how economic shocks propagate and amplify through the input-output network connecting industrial sectors in developed economies. We study alternative models of diffusion on networks and we calibrate them using input-output data on real-world inter-sectoral dependencies for several European count…
Matching pursuit algorithms are an important class of algorithms in signal processing and machine learning. We present a blended matching pursuit algorithm, combining coordinate descent-like steps with stronger gradient descent steps, for minimizing a smooth convex function over a linear space spanned by a set of atoms…
Breiman's two cultures reconciled through blending statistical thinking.
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
Improved prediction of polymer morphology through machine learning and simulations.
We introduce a new framework for training deep generative models for high-dimensional conditional density estimation. The Bottleneck Conditional Density Estimator (BCDE) is a variant of the conditional variational autoencoder (CVAE) that employs layer(s) of stochastic variables as the bottleneck between the input a…
Method combines clustering and matrix completion for missing data in I/O tables.
In this paper we explore techniques for generating new music using a Variational Autoencoder (VAE) neural network that was trained on a corpus of specific style. Instead of randomly sampling the latent states of the network to produce free improvisation, we generate new music by querying the network with musical input …
Blend-ASC improves self-consistency efficiency by dynamically allocating samples, reducing costs.
Starting from a dataset with input/output time series generated by multiple deterministic linear dynamical systems, this paper tackles the problem of automatically clustering these time series. We propose an extension to the so-called Martin cepstral distance, that allows to efficiently cluster these time series, and a…
New method ranks sectors and countries using local and aggregate I-O data.
BiPE blends intra-segment and inter-segment encodings for better length extrapolation.
Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students' performance in face to face classes.…
New algorithm completes nonnegative tensors with fewer samples and faster convergence.
We consider the problem of learning a realization for a linear time-invariant (LTI) dynamical system from input/output data. Given a single input/output trajectory, we provide finite time analysis for learning the system's Markov parameters, from which a balanced realization is obtained using the classical Ho-Kalman al…
Develops neural network approximations for infinite-dimensional input-output maps.
Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our pa…
Graph-to-Tree Neural Networks improve structured input-output translation in tasks like semantic parsing and math word problems.
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-invariant features helps to achieve this goal, whereas it underpins unlabeled samples drawn from a single or multiple explicit target domains …
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the class…
Mack-Net model combines Mack's model with RNNs for better insurance liability estimation.
Machine learning in context of physical systems merits a re-examination of the learning strategy. In addition to data, one can leverage a vast library of physical prior models (e.g. kinematics, fluid flow, etc) to perform more robust inference. The nascent sub-field of \emph{physics-based learning} (PBL) studies the bl…
Improved sampling efficiency for inverse problems using variance-reduced diffusion methods.
Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior. Decision trees characterize the input-output relationship through a series of nested questions, the testing nodes, leading to a set of predictions, th…
We consider the learning of algorithmic tasks by mere observation of input-output pairs. Rather than studying this as a black-box discrete regression problem with no assumption whatsoever on the input-output mapping, we concentrate on tasks that are amenable to the principle of divide and conquer, and study what are it…