The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.
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Cross-Validatory Choice and Assessment of Statistical Pre- dictions
14 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 14representative citing papers
LLMs display left bias on abstract policy questions but align with centrist parties and exhibit change-aversion on real Swiss federal referenda.
Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
Introduces multi-time renewal chains via multi-index convolution and power series, with FFT-based computation, asymptotic theorems under proportional growth, and a nonparametric MLE for fixed-horizon censored data.
A finite-element variational inference method delivers full-covariance Bayesian field reconstruction at dimensions exceeding 400,000 for 3D porous media flow using sparse precision parameterization from SPDE priors.
Maximum-likelihood-based posterior predictive checks detect model misspecification better than event-level versions for uncertain spin tilts, but current detector sensitivity limits their power; the Gaussian Component Spins model underpredicts high spin magnitudes and overpredicts anti-aligned tilts
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
Case study of 18,020 Kubernetes PRs shows label-diff congruence is prevalent and stable, with higher congruence linked to fewer review participants among core developers and more among one-time contributors.
PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.
A consistent two-stage GPH-filtered Hannan-Rissanen generalized information criterion for selecting finite AR and MA orders in ARFIMA models with growing candidate sets.
FunnelNet is a ~5.4k-parameter CNN that detects heart murmurs from PCG signals at 85% accuracy and deploys in real time on edge hardware via TinyML.
Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.
This paper reviews methods for assessing credibility of simulation architectures made from multiple models, comparing sensitivity analysis, expert judgment, AI explainability, and network techniques on rigor, generalization, and resource requirements.
A curated dataset of counterintuitive discrete probability problems with human solutions, built to benchmark LLM reasoning on bias-prone tasks.
citing papers explorer
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Partitioning Neural Co-Variability
The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.
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The Invisible Coalition Partner: How LLMs Vote When Democracy Gets Concrete
LLMs display left bias on abstract policy questions but align with centrist parties and exhibit change-aversion on real Swiss federal referenda.
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How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior
Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
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Discrete time-multidimensional renewal theory and applications
Introduces multi-time renewal chains via multi-index convolution and power series, with FFT-based computation, asymptotic theorems under proportional growth, and a nonparametric MLE for fixed-horizon censored data.
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Scalable High-Dimensional Bayesian Field Reconstruction with Finite Elements: Application to 3D Porous Media Flow
A finite-element variational inference method delivers full-covariance Bayesian field reconstruction at dimensions exceeding 400,000 for 3D porous media flow using sparse precision parameterization from SPDE priors.
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Posterior Predictive Checks for Gravitational-wave Populations: Limitations and Improvements
Maximum-likelihood-based posterior predictive checks detect model misspecification better than event-level versions for uncertain spin tilts, but current detector sensitivity limits their power; the Gaussian Component Spins model underpredicts high spin magnitudes and overpredicts anti-aligned tilts
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Opal: Private Memory for Personal AI
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
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Efficiency for Experts, Visibility for Newcomers: A Case Study of Label-Code Alignment in Kubernetes
Case study of 18,020 Kubernetes PRs shows label-diff congruence is prevalent and stable, with higher congruence linked to fewer review participants among core developers and more among one-time contributors.
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Physics-Guided Tiny-Mamba Transformer for Reliability-Aware Early Fault Warning
PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.
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A GPH-Filtered Hannan--Rissanen Information Criterion for ARFIMA Order Selection
A consistent two-stage GPH-filtered Hannan-Rissanen generalized information criterion for selecting finite AR and MA orders in ARFIMA models with growing candidate sets.
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FunnelNet: An End-to-End Deep Learning Framework to Monitor Digital Heart Murmur in Real-Time
FunnelNet is a ~5.4k-parameter CNN that detects heart murmurs from PCG signals at 85% accuracy and deploys in real time on edge hardware via TinyML.
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Explaining Machine Learning and Memorization with Statistical Mechanics
Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.
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Why Model Credibility Isn't Enough: -Rethinking Trust in Simulation Architectures
This paper reviews methods for assessing credibility of simulation architectures made from multiple models, comparing sensitivity analysis, expert judgment, AI explainability, and network techniques on rigor, generalization, and resource requirements.
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Counterintuitive problems in discrete probability
A curated dataset of counterintuitive discrete probability problems with human solutions, built to benchmark LLM reasoning on bias-prone tasks.