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.
Evolutionary methods improve understanding of LLMs and their relationships.
In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being ro…
Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Here we use phylogenetic Gaussian process regression to model the evolution of simulated function-valued traits. Given function-valued data only from the tips of an evolutionary tree and utilising independent pri…
Momentum speeds up evolutionary processes in machine learning.
A method to control results of gradient descent unsupervised learning in a deep neural network by using evolutionary algorithm is proposed. To process crossover of unsupervisedly trained models, the algorithm evaluates pointwise fitness of individual nodes in neural network. Labeled training data is randomly sampled an…
CoNES optimizes blackbox functions using convex optimization and information geometry.
We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently c…
Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within mu…
Combines variational and evolutionary optimization for generative models.
Proves FR-NGD optimally approximates evolutionary dynamics and continuous Bayesian inference.
Evolution and learning are two of the fundamental mechanisms by which life adapts in order to survive and to transcend limitations. These biological phenomena inspired successful computational methods such as evolutionary algorithms and deep learning. Evolution relies on random mutations and on random genetic recombina…
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute t…
EPNE models evolving network patterns for better predictions.
Simplicial learning improves classification by generating compact sparse representations.
The de Rham-Hodge theory is a landmark of the 20 Century's mathematics and has had a great impact on mathematics, physics, computer science, and engineering. This work introduces an evolutionary de Rham-Hodge method to provide a unified paradigm for the multiscale geometric and topological analysis of evolv…
Evolutionary Strategies optimize hyper-parameters for off-policy learning.
New algorithm improves game learning with randomised optimism.
New research connects evolutionary dynamics to Bayesian learning.
evo-RL combines evolutionary computation with reinforcement learning for better adaptability.
VisEvol uses evolutionary optimization to find optimal hyperparameters for machine learning models.
Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward, as well as a dense agent-specific reward that incentivizes learning basic skills. Training policies solely on the team-based reward is often difficult due to its sparsity. Furthermore, relying solely on the a…
Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling (EC) analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, resp…
Evolutionary methods improve neural network loss functions, reducing overfitting.
Proposes Genetic Thompson Sampling for multi-armed bandits, improving performance in nonstationary settings.
A new method estimates protein evolutionary fields and couplings from alignments.
Enhanced evolutionary algorithms solve NP-hard portfolio optimization with cardinality constraints.
We define Lie algebroids over infinite jet spaces and establish their equivalent representation through homological evolutionary vector fields.
The paper analyzes cryptocurrency and equity markets using advanced statistical methods.
Evolutionary strategy optimizes quantum circuit design and parameters.
In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evolutionary optimization environment are ideally suited for an integration of various types of probabilistic constraints. W…
A neural network and evolutionary algorithm framework designs nonlinear optical molecules.
OpEvo automates tensor operator optimization for better efficiency.
Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons …
Evolutionary algorithm finds optimal pixel perturbations to improve neural network generalization.
A co-evolutionary approach for Heston model calibration reduces overfitting with diverse datasets.
The paper improves evolutionary computation by optimizing selection rates.
Novel evolutionary strategy solves stochastic constrained optimization problems.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
New method embeds phylogenetic trees for clustering, recovering evolutionary relationships.
BEKAN uses RBFs and evolutionary methods to solve PDEs with boundary conditions.
Neural Architecture Search has shown potential to automate the design of neural networks. Deep Reinforcement Learning based agents can learn complex architectural patterns, as well as explore a vast and compositional search space. On the other hand, evolutionary algorithms offer higher sample efficiency, which is criti…
Framework improves self-play for cooperative multi-agent learning.
ESPD improves learning efficiency in sparse reward reinforcement learning.
This paper proposes Evolutionary Multi-objective Optimization (EMO)-based Adversarial Example (AE) design method that performs under black-box setting. Previous gradient-based methods produce AEs by changing all pixels of a target image, while previous EC-based method changes small number of pixels to produce AEs. Than…
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
Most decision tree induction algorithms are based on a greedy top-down recursive partitioning strategy for tree growth. In this paper, we propose several methods for induction of decision trees and their ensembles based on evolutionary algorithms. The main difference of our approach is using real-valued vector represen…
This research proposes the econophysics kinetic market model as an evolutionary algorithm's instance. The immediate results from this proposal is a new replacement rule for family competition genetic algorithms. It also represents a starting point to adding evolvable entities to kinetic market models.
The last financial and economic crisis demonstrated the dysfunctional long-term effects of aggressive behaviour in financial markets. Yet, evolutionary game theory predicts that under the condition of strategic dependence a certain degree of aggressive behaviour remains within a given population of agents. However, as …