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.
GMM-HMMs improve malware classification compared to discrete HMMs.
New entropy-based objective for sparse coding improves learning.
Entropy-based GP adaptive design improves failure probability estimation.
We present a new method of generating mixture models for data with categorical attributes. The keys to this approach are an entropy-based density metric in categorical space and annealing of high-entropy/low-density components from an initial state with many components. Pruning of low-density components using the entro…
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among objectives or introduce unreliable approximations. We propose a novel entropy-based MBO called Pare…
Entropy-based model for hierarchical learning from multiscale data.
Paper proposes a Renyi entropy-based method for tuning hierarchical topic models.
Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - s…
Study predicts price predictability in ultra-high frequency financial data using entropy tests.
A novel framework infers causal direction from symbolic sequences using pattern entropy.
MESMOC optimizes constrained multi-objective problems efficiently.
This work improves VAEs using MCMC methods for better variational bounds.
Paper models entropy-based impact of soft errors on neural network inference.
The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves …
This is full length article (draft version) where problem number of topics in Topic Modeling is discussed. We proposed idea that Renyi and Tsallis entropy can be used for identification of optimal number in large textual collections. We also report results of numerical experiments of Semantic stability for 4 topic mode…
Entropy based ideas find wide-ranging applications in finance for calibrating models of portfolio risk as well as options pricing. The abstracted problem, extensively studied in the literature, corresponds to finding a probability measure that minimizes relative entropy with respect to a specified measure while satisfy…
An empirical investigation of the interaction of sample size and discretization - in this case the entropy-based method CAIM (Class-Attribute Interdependence Maximization) - was undertaken to evaluate the impact and potential bias introduced into data mining performance metrics due to variation in sample size as it imp…
A novel outlier score detects new road infrastructure images.
Study generalizes Picard iteration for nonlinear PDEs, deriving bounds on error.
A new active learning method for Gaussian process models.
FEDS distills LIC model knowledge into a lightweight student for efficient compression.
Unified framework for network model assessment using maximum entropy.
Study potential computational gaps in symmetric binary perceptrons using fl-RDT.
This work introduces a novel method to evaluate generative model novelty.
ALIEN improves uncertainty estimation of language models by refining entropy-based methods.
Entropy-based decoding improves DLM sampling efficiency.
The study proves compactness and existence of entropy minimizers for self-shrinking surfaces.
Introduces RPU to explain randomization preference in dynamic settings.
Local Sobolev inequality on Ricci flows with applications.
MESSY estimation recovers symbolic density functions from samples using maximum entropy.
A framework uses free probability to analyze Transformer models.
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data for any ML system typically improves accuracy, but only if it provides examples not already adequately covered in the existing data. However, …
We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically …
Paper introduces a new measure combining entropy and Gini index.
CAT framework improves AI medical screening fairness and reliability.
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT) enables reduction of time-to-therapy for endovascular procedures. However, the prolonged acquisition time compared to helical CT increases the likelihood of rigid patient motion. Rigid motion corrupts the geometry alignment assumed during reconst…
SOAR improves deep networks' robustness against adversarial examples.
In this paper, we present a machine learning approach for estimating the number of incident wavefronts in a direction of arrival scenario. In contrast to previous works, a multilayer neural network with a cross-entropy objective is trained. Furthermore, we investigate an online training procedure that allows an adaptio…
Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the inherent structure of clusterings. We model the relation between two clusterings as a bipartite graph and propose a general component-based …
Exploration is an extremely challenging problem in reinforcement learning, especially in high dimensional state and action spaces and when only sparse rewards are available. Effective representations can indicate which components of the state are task relevant and thus reduce the dimensionality of the space to explore.…
The study evaluates different probability models for uncertainty visualization using entropy calculations.
We consider the issue of multiple agents learning to communicate through reinforcement learning within partially observable environments, with a focus on information asymmetry in the second part of our work. We provide a review of the recent algorithms developed to improve the agents' policy by allowing the sharing of …
The interdependent nature of the global economy has become stronger with increases in international trade and investment. We propose a new model to reconstruct the international trade network and associated cost network by maximizing entropy based on local information about inward and outward trade. We show that the tr…
Paper introduces variance-based measures for second-order uncertainty quantification in classification problems.
We investigate entropy as a financial risk measure. Entropy explains the equity premium of securities and portfolios in a simpler way and, at the same time, with higher explanatory power than the beta parameter of the capital asset pricing model. For asset pricing we define the continuous entropy as an alternative meas…
High quality reconstruction with interventional C-arm cone-beam computed tomography (CBCT) requires exact geometry information. If the geometry information is corrupted, e. g., by unexpected patient or system movement, the measured signal is misplaced in the backprojection operation. With prolonged acquisition times of…
Sample size determination for a data set is an important statistical process for analyzing the data to an optimum level of accuracy and using minimum computational work. The applications of this process are credible in every domain which deals with large data sets and high computational work. This study uses Bayesian a…
New estimator for joint entropy outperforms existing methods in various distributions.