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arXiv research

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.

168,695 papers · 148 categories

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0111 · Feb 201619922001200920172026
8 results for homeostasis

Sleep-based regularization stabilizes STDP in recurrent neural networks.

problem Pathological weight dynamics in recurrent SNNs.
method Periodic offline phases with stochastic decay and spontaneous activity.
result Sleep-based renormalization prevents weight saturation and preserves learned structure.

Machine learning detects subtle glucose changes for early diabetes diagnosis.

problem Challenging early-stage diabetes diagnosis due to subtle glucose changes.
method Applied machine learning to synthetic glucose profiles generated by a biophysical model.
result High accuracy (above 85%) in detecting insulin resistance using various neural networks.

Many complex ecosystems, such as those formed by multiple microbial taxa, involve intricate interactions amongst various sub-communities. The most basic relationships are frequently modeled as co-occurrence networks in which the nodes represent the various players in the community and the weighted edges encode levels o…

2016-05-16abs ↗pdf ↗

Study limits of circadian synchronization under different light signals.

problem Disruption of circadian rhythms due to misalignment with external light signals.
method Matrix-free approach for locating periodic steady states, numerical continuation, bifurcation diagrams, unsupervised learning.
result Limits of circadian synchronization to external light signals of different frequency and duty cycle.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computa…

2017-05-15abs ↗pdf ↗

Model captures neural activity related to behavior while separating internal computations.

problem Capturing neural activity related to behavior from complex brain recordings.
method Behavior-decomposed linear dynamical systems (b-dLDS) model.
result Improves over state-of-the-art models in disentangling behavior-related dynamics.