Tag: systems
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Reinforcement Learning for Control Systems with Time Delays: A Comprehensive Survey
Reinforcement Learning for Control Systems with Time Delays: A Comprehensive Survey arXiv:2602.00399v1 Announce Type: new Abstract: In the last decade, Reinforcement Learning (RL) has achieved remarkable success in the control and decision-making of complex dynamical systems. However, most RL algorithms rely on the Markov Decision Process assumption, which is violated in practical cyber-physical systems affected…
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Deep Neural Networks as Iterated Function Systems and a Generalization Bound
Deep Neural Networks as Iterated Function Systems and a Generalization Bound arXiv:2601.19958v1 Announce Type: new Abstract: Deep neural networks (DNNs) achieve remarkable performance on a wide range of tasks, yet their mathematical analysis remains fragmented: stability and generalization are typically studied in disparate frameworks and on a case-by-case basis. Architecturally, DNNs rely on the recursive…
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Drift Detection in Robust Machine Learning Systems
Drift Detection in Robust Machine Learning Systems A prerequisite for long-term success of machine learning systems The post Drift Detection in Robust Machine Learning Systems appeared first on Towards Data Science. Morris Stallmann Go to original source
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Chunk Size as an Experimental Variable in RAG Systems
Chunk Size as an Experimental Variable in RAG Systems Understanding retrieval in RAG systems by experimenting with different chunk sizes The post Chunk Size as an Experimental Variable in RAG Systems appeared first on Towards Data Science. Sarah Schürch Go to original source
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Six Lessons Learned Building RAG Systems in Production
Six Lessons Learned Building RAG Systems in Production Best practices for data quality, retrieval design, and evaluation in production RAG systems The post Six Lessons Learned Building RAG Systems in Production appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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Riemannian Stochastic Interpolants for Amorphous Particle Systems
Riemannian Stochastic Interpolants for Amorphous Particle Systems arXiv:2512.16607v1 Announce Type: new Abstract: Modern generative models hold great promise for accelerating diverse tasks involving the simulation of physical systems, but they must be adapted to the specific constraints of each domain. Significant progress has been made for biomolecules and crystalline materials. Here, we address amorphous materials…
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Data-driven Learning of Interaction Laws in Multispecies Particle Systems with Gaussian Processes: Convergence Theory and Applications
Data-driven Learning of Interaction Laws in Multispecies Particle Systems with Gaussian Processes: Convergence Theory and Applications arXiv:2511.02053v1 Announce Type: new Abstract: We develop a Gaussian process framework for learning interaction kernels in multi-species interacting particle systems from trajectory data. Such systems provide a canonical setting for multiscale modeling, where simple microscopic interaction rules generate complex…
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The Westworld Blunder
The Westworld Blunder We’re entering an interesting moment in AI development. AI systems are getting memory, reasoning chains, self-critiques, and long-context recall. These capabilities are exactly some of the things that I’ve previously written would be prerequisites for an AI system to be conscious. Just to be clear, I don’t believe today’s AI systems are self-aware, but…
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Attractors in Neural Network Circuits: Beauty and Chaos
Attractors in Neural Network Circuits: Beauty and Chaos The state space of the first two neuron activations over time follows an attractor. What is one thing in common between memories, oscillating chemical reactions and double pendulums? All these systems have a basin of attraction for possible states, like a magnet that draws the system towards certain…
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Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs
Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs arXiv:2502.14121v1 Announce Type: new Abstract: Designing modern industrial systems requires balancing several competing objectives, such as profitability, resilience, and sustainability, while accounting for complex interactions between technological, economic, and environmental factors. Multi-objective optimization (MOO) methods are commonly used to navigate…
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Reinforcement Learning with PDEs
Reinforcement Learning with PDEs Previously we discussed applying reinforcement learning to Ordinary Differential Equations (ODEs) by integrating ODEs within gymnasium. ODEs are a powerful tool that can describe a wide range of systems but are limited to a single variable. Partial Differential Equations (PDEs) are differential equations involving derivatives of multiple variables that can cover…
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Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems
Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems How natural systems fundamental laws help explain AI’s unexpected abilities Continue reading on Towards Data Science » Javier Marin Go to original source
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Fighting Fraud Fairly: Upgrade Your AI Toolkit
Fighting Fraud Fairly: Upgrade Your AI Toolkit A practical approach to address bias in AI systems Photo by the author As sophisticated AI systems are increasingly used in decision-making, ensuring fairness has become a priority, with a growing need to prevent algorithms from disproportionately affecting vulnerable groups in sensitive areas like the justice or educational system. One…
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Learning Networks from Wide-Sense Stationary Stochastic Processes
Learning Networks from Wide-Sense Stationary Stochastic Processes arXiv:2412.03768v1 Announce Type: new Abstract: Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from node outputs (potentials). We focus on systems governed by steady-state linear conservation laws: $X_t = {L^{ast}}Y_{t}$,…