Markov
PulseAugur coverage of Markov — every cluster mentioning Markov across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New Latent Jump Models offer faster training for time series analysis
Researchers have introduced Latent Jump Models (LJMs), a new framework for analyzing irregularly observed time series. Unlike traditional models that rely on smooth ODE or SDE dynamics, LJMs incorporate discrete, Poisso…
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New method synthesizes policies for finite MDP agent populations
Researchers have developed a new method for policy synthesis in multi-agent systems, specifically addressing finite populations of Markov decision process (MDP) agents. This approach tackles aggregate reach-avoid chance…
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New framework for online resource allocation using Markov states
This research paper introduces a novel online resource allocation framework that utilizes an endogenous Markov state, where actions influence future state transitions and rewards. The study identifies conditions under w…
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New Lattice Framework Unifies Diffusion and Autoregressive Models
Researchers have unified diffusion and autoregressive (AR) models by conceptualizing them as paths on a corruption lattice. This framework allows for the prediction of decoding schedule performance based on data geometr…
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1984 Soviet Union paper argues AI is impossible
A 1984 perspective from the Soviet Union argues that artificial intelligence is impossible, drawing on the work of A. V. Brushlinsky and Markov's cybernetics. The piece questions the ability of machines to truly replica…
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EMD-LSTM-Markov model leads in building thermal load prediction accuracy
A new paper reviews hybrid forecasting models for short-term building thermal load prediction, comparing various data-driven techniques. The study found that the Empirical Modal Decomposition - long short-term memory - …
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New algorithms improve causal discovery in mixed-variable datasets
Researchers have developed new methods for causal discovery in Directed Acyclic Graphs (DAGs), particularly for datasets that include a mix of ordinal, count, and continuous variables. The paper proves that edge directi…
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Parameterised graph theory applied to tensor networks for simulation and learning
Researchers have applied parameterised graph theory to tensor networks, exploring how graph parameters influence the complexity of simulating and learning these networks. The study demonstrates that cutwidth and tree-cu…
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CoSMO framework optimizes edge-cloud task execution using reinforcement learning
Researchers have developed CoSMO, a novel reinforcement learning framework designed to optimize task execution in edge-cloud computing environments. This system addresses the challenge of partial observability by coordi…
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User trolls AI scrapers with Markov-generated data
An individual is intentionally feeding AI scrapers, including GPTBot, large amounts of data generated using Markov chains. This action is described as a form of trolling, aimed at disrupting or misleading these AI syste…
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New method simplifies partial observability in reinforcement learning
Researchers have developed a method called Minimal Markovization to address the challenge of agents acting under partial observability. This technique characterizes the minimal Markov sufficient statistic for holonomy-c…
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Single equation may unlock AGI without GPUs or LLMs, claims new research
A new project, MCR, proposes that artificial general intelligence (AGI) may not require massive computational resources like GPUs or large language models (LLMs). Instead, it suggests that a single, simple equation, ins…
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New AI Framework '3rd-level Hysteresis' Detailed in Manifesto
The author has completed a four-part document, including a "Manifest and Epilogue," which outlines a new architectural framework for understanding AI. This framework, termed "3rd-level Hysteresis," is presented as a suc…
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AI models learn time-inhomogeneous Markov dynamics in financial time series
Researchers have developed a new framework that uses neural networks to parameterize time-varying Markov transition matrices for financial time series. This approach aims to balance the representational power of deep le…