continuous-time Markov chain
PulseAugur coverage of continuous-time Markov chain — every cluster mentioning continuous-time Markov chain across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New H-Glow framework accelerates graph generation using hazard guidance
Researchers have developed H-Glow, a new framework designed to accelerate graph generation using hazard-guided discrete flow matching. This approach decomposes continuous-time Markov chain generators into hazards and co…
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Discrete MeanFlow enables one-step generation in discrete state spaces
Researchers have introduced Discrete MeanFlow, a novel theoretical framework for one-step generation in discrete state spaces. This approach replaces the continuous velocity field concept of MeanFlow with probability-ma…
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New AI methods improve materials property prediction with scarce data · 4 sources tracked
Researchers have developed new methods for predicting materials properties, particularly in scenarios with limited data. FrOGS, a discrete neural sampler, uses a hybrid approach combining autoregressive models with cont…
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New TTS research explores discrete flow matching for efficiency
Two new research papers explore advancements in zero-shot text-to-speech (TTS) technology, focusing on discrete flow matching techniques. The first paper introduces DiFlow-TTS, a framework that uses a discrete flow matc…
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New research frames insertion language models with Markov chains and learnable dynamics
Two new research papers explore advancements in insertion language models (ILMs), a method for generating sequences by inserting tokens. The first paper introduces a continuous-time Markov chain framework to derive a di…