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.
Derives a fluid model for fish swarming in arbitrary dimensions.
Predicting illegal fishing on Patagonian Shelf using machine learning.
Deep learning enhances smart fish farming through automated feature extraction.
Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as robustness, scalability, and flexibility. Yet, we do not know why swarm-based alg…
The paper improves marine buoy placement to detect ships robustly against disruptions.
According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous - that is, i…
DBS uses swarm intelligence to cluster data without needing a global objective function.
A combination of a priority queueing model and mean field theory shows the emergence of traders' swarm behavior, even when each has a subjective prediction of the market driven by a limit order book. Using a nonlinear Markov model, we analyze the dynamics of traders who select a favorable order price taking into accoun…
Paper proposes a federated learning framework for UAV swarms, optimizing convergence rate and energy consumption.
In this study, we present a simple stochastic order-book model for investors' swarm behaviors seen in the continuous double auction mechanism, which is employed by major global exchanges. Our study shows a characteristic called "fat tail" is seen in the data obtained from our model that incorporates the investors' swar…
The paper learns particle swarming models from data using Gaussian processes.
AdaSwarm optimizes deep learning models with swarm intelligence, outperforming Adam.
We study a distributed framework for stochastic optimization which is inspired by models of collective motion found in nature (e.g., swarming) with mild communication requirements. Specifically, we analyze a scheme in which each one of independent threads, implements in a distributed and unsynchronized fashion,…
Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large…
This paper reviews feature selection methods using swarm intelligence.
Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems. In this paper, we show how the principle of IRL can be extended to homogeneous large-scale problems, inspired by the collective swarming b…
The values that the first two Vassiliev invariants take on prime knots with up to fourteen crossings are considered. This leads to interesting fish-like graphs. Several results about the values taken on torus knots are proved.
Deep learning autoencoder detects bee colony anomalies.
ARS visualization improves t-SNE dynamics with tunable attraction and repulsion.
The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.
We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multi-armed bandit (rMAB). A player competes with multiple agents. Each bandit has a payoff that changes with a probability per round. The agents and player choose one of three options: (1) Exploit (a …
Gradient Descent (GD) approximators often fail in the solution space with multiple scales of convexities, i.e., in subspace learning and neural network scenarios. To handle that, one solution is to run GD multiple times from different randomized initial states and select the best solution over all experiments. However,…
We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. …
In this work we propose a new neural network architecture that efficiently implements and learns general purpose set-equivariant functions. Such a function f maps a set of entities x = {x1, . . . , xn} from one domain to a set of same cardinality y = f (x) = {y1, . . . , yn} in another domain regardless of the ordering…
Study examines CSO algorithm for 3D swarming and tracking multiple targets.
PolySwarm uses a swarm of LLMs to predict and arbitrage prediction markets.
Extends FC-RAG to anytime-valid sequential coverage for language model swarms.
In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as theArbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorit…
Paper uses averaging from many particle filters to approximate posterior predictive distributions.
Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. While it is often difficult to directly define the behavior of the agents, simple communicatio…
Framework infers coordination strategies from movement data.
The paper connects PSO and CBO methods using stochastic modeling and mean-field limits.
As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Opt…
Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite a rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by which complex behavior emerges in these systems is still not well und…
Gradient matching method improves domain generalization across various datasets.
A new optimizer, MVO, improves nonlinear regression performance.
Unified model learns from proteins and ligands for drug design.
In this paper, we investigate how to learn to control a group of cooperative agents with limited sensing capabilities such as robot swarms. The agents have only very basic sensor capabilities, yet in a group they can accomplish sophisticated tasks, such as distributed assembly or search and rescue tasks. Learning a pol…
New algorithm uses PSO to optimize DNN training parameters in distributed systems.
PSO optimizes model parameters in nonstandard distributions.
GeoStat simplifies time series classification with fast, intuitive features.
Empirical comparison of 18 hyperparameter tuning algorithms for SVM.
Study on stability of network flow shrinkers with findings on instability of specific shapes.
A new time-series clustering method using slope-based similarity and PSO.
mFI-PSO generates effective adversarial images for DNNs.
Supervised classification is the most active and emerging research trends in today's scenario. In this view, Artificial Neural Network (ANN) techniques have been widely employed and growing interest to the researchers day by day. ANN training aims to find the proper setting of parameters such as weights () …
Optimal income crossover found using particle swarm optimization.
Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.