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.
Bayesian model averaging under predictor redundancy
The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method (Idep) has recently been proposed for computing a two-predictor PID over discrete spaces. [arXiv:1709.06653] A lattice of maximum en…
Feature selection, as a critical pre-processing step for machine learning, aims at determining representative predictors from a high-dimensional feature space dataset to improve the prediction accuracy. However, the increase in feature space dimensionality, comparing to the number of observations, poses a severe challe…
We develop an online learning method for prediction, which is important in problems with large and/or streaming data sets. We formulate the learning approach using a covariance-fitting methodology, and show that the resulting predictor has desirable computational and distribution-free properties: It is implemented onli…
Williams and Beer (2010) proposed a nonnegative mutual information decomposition, based on the construction of redundancy lattices, which allows separating the information that a set of variables contains about a target variable into nonnegative components interpretable as the unique information of some variables not p…
New method quantifies redundant information using information bottleneck.
Redundancy improves learning stability and generalization in structured systems.
Transformers reduce redundancy by focusing on invariant relational quantities.
The customizable nature of deep learning models have allowed them to be successful predictors in various disciplines. These models are often trained with respect to thousands or millions of instances for complicated problems, but the gathering of such an immense collection may be infeasible and expensive. However, what…
Convolutional neural networks (CNN) are generally designed with a heuristic initialization of network architecture and trained for a certain task. This often leads to overparametrization after learning and induces redundancy in the information flow paths within the network. This robustness and reliability is at the inc…
Paper proposes redundancy-free features for zero-shot object recognition.
This work explains scaling laws as redundancy laws in deep learning.
Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these co…
Study on neural networks to identify redundancy issues in safe machine learning.
Exclusive Lasso improves survival prediction in cancer datasets.
Logistic regression with wavelets achieves bacterial infection detection accuracy.
Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques …
We simplify SSL by approximating redundant structural components with low-rank factorization.
Redundancy in deep neural network (DNN) models has always been one of their most intriguing and important properties. DNNs have been shown to overparameterize, or extract a lot of redundant features. In this work, we explore the impact of size (both width and depth), activation function, and weight initialization on th…
This paper introduces a new measure to identify model redundancy in compressed CNNs.
Optimized GPRNN reduces model complexity and overfitting, improving performance.
The study analyzes optimization trajectories in neural networks to reveal redundancy and redundancy-reducing strategies.
Contrastive learning works well with redundant data views.
This work introduces RISE to explain LLMs more reliably by distinguishing essential context.
This work identifies redundant tests in conditional-independence-based discovery that can improve graphical model accuracy.
New method distinguishes feature relevance in non-linear contexts.
Paper compresses deep neural networks by eliminating redundant neurons.
Large datasets have been crucial to the success of deep learning models in the recent years, which keep performing better as they are trained with more labelled data. While there have been sustained efforts to make these models more data-efficient, the potential benefit of understanding the data itself, is largely unta…
FactorMiner discovers financial alpha factors with low redundancy.
Wide neural networks' last hidden layers split into groups of redundant neurons.
Spectral dimensionality reduction algorithms are widely used in numerous domains, including for recognition, segmentation, tracking and visualization. However, despite their popularity, these algorithms suffer from a major limitation known as the "repeated Eigen-directions" phenomenon. That is, many of the embedding co…
Machine learning (ML) is probably the first and foremost used technique to deal with the size and complexity of the new generation of data. In this paper, we analyze one of the means to increase the performances of ML algorithms which is exploiting data locality. Data locality and access patterns are often at the heart…
In 1980 J. Powell proposed that five specific elements sufficed to generate the Goeritz group of any Heegaard splitting of . This conjecture remains unresolved for genus . Here a short argument shows that one of his proposed generators is redundant, in fact a consequence of three of the other four.
An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabil…
Motivation: Radiomics refers to the high-throughput mining of quantitative features from radiographic images. It is a promising field in that it may provide a non-invasive solution for screening and classification. Standard machine learning classification and feature selection techniques, however, tend to display infer…
Cell-based NAS search spaces are redundant and lack novelty.
Redundancy in AI perception systems doesn't guarantee independent error occurrences.
Estimates interactions between modalities for multimodal data.
Signed pairwise interactions conflate uniqueness, redundancy, and synergy
PAC-Bayesian bounds improve understanding of K-NN classifier performance.
New CR representations are found and shown to be redundant.
New method reduces overfitting in deep neural networks by measuring and regulating hidden unit diversity.
Optimal algorithms for mixable losses in dynamic environments with reduced redundancy.
Neural networks learn task-specific features, influenced by nonlinearity.
Reducing the latency variance in machine learning inference is a key requirement in many applications. Variance is harder to control in a cloud deployment in the presence of stragglers. In spite of this challenge, inference is increasingly being done in the cloud, due to the advent of affordable machine learning as a s…
Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two feat…
To improve the resilience of distributed training to worst-case, or Byzantine node failures, several recent approaches have replaced gradient averaging with robust aggregation methods. Such techniques can have high computational costs, often quadratic in the number of compute nodes, and only have limited robustness gua…
This paper is concerned with the question of reconstructing a vector in a finite-dimensional real Hilbert space when only the magnitudes of the coefficients of the vector under a redundant linear map are known. We analyze various Lipschitz bounds of the nonlinear analysis map and we establish theoretical performance bo…