A video-to-PDE pipeline extracts the model u_t + v(t)·∇u = 9.005|∇u|^2 + 0.666Δu from grayscale ink-plume footage, outperforming advection-diffusion baselines on held-out frames and reducing to linear form via Cole-Hopf transformation.
10 Evaluating Bivariate Causal Statements Based on Mutual Compatibility Richardson, T
15 Pith papers cite this work. Polarity classification is still indexing.
15
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 15representative citing papers
Newton's recursive mixture estimator is a discrete gradient flow on the Fisher-Rao manifold of probability measures.
LLM personas exhibit model-dependent personality effects on color choices and context-driven chart preferences, limiting their use as direct substitutes for human participants in visualization design.
Operator Boosting constructs compact neural-operator PDE surrogates by sequential residual learning with validation-selected shrinkage, yielding 72-95% parameter reduction and accuracy gains on 21 of 30 dataset-architecture pairs.
XtrAIn shifts occlusion from input space to parameter space along the training trajectory to produce cleaner feature attributions than standard methods.
Proposes compatibility scores for bivariate causal statements that quantify plausibility via the confounding implied by the induced multivariate model, plus an incompatibility score based on acyclicity and faithfulness constraints.
Bayesian joint model infers infectious virus shedding trajectories and derived infectiousness metrics from PCR and other proxies in SARS-CoV-2 using data from five cohorts of roughly 2000 infections.
Branch-resolved Choi-shadow benchmarking of teleportation shows the relative benefit of PROM mitigation vs post-processing flips depending on mid-circuit readout error.
Structured light modes act as collinear interferometer arms, enabling common-path quantitative phase imaging with AFM-level agreement and optional polarization encoding.
Context-based adversarial attacks raise vulnerable code generation in models like GPT-4 and CodeLlama from 3.5% to 37.4%, with 60-100% transferability, and a dual-layer defense reaches 89.1% detection at low false positives.
A method that translates causal relationships into a Bipolar Argumentation Framework and applies semi-stable semantics to generate explanatory feature sets for machine learning predictions.
A kernel-based regularized learning framework for FDR control that unifies arbitrary structures and supplies provably valid decision rules with likelihood-based tuning.
Post-hoc isotonic regression calibration for deep Cox survival models that improves calibration with theoretical guarantees including double-robustness and asymptotic calibration.
crossfit is an R package that supplies a general-purpose cross-fitting engine driven by user-specified DAGs of nuisance models with configurable fold allocations and reproducibility features.
Established mathematical bottlenecks in representation, optimization, complexity, and high-dimensional learning aligned with the central disappointments of early AI research periods.
citing papers explorer
-
From Video-to-PDE: Data-Driven Discovery of Nonlinear Dye Plume Dynamics
A video-to-PDE pipeline extracts the model u_t + v(t)·∇u = 9.005|∇u|^2 + 0.666Δu from grayscale ink-plume footage, outperforming advection-diffusion baselines on held-out frames and reducing to linear form via Cole-Hopf transformation.
-
Newton's Algorithm as a Gradient Flow: A Geometric Framework for Recursive Mixture Estimation
Newton's recursive mixture estimator is a discrete gradient flow on the Fisher-Rao manifold of probability measures.
-
When Do LLM Personas Support Visualization Design? A Cross-Model Study of Color Assignment and Chart Choice
LLM personas exhibit model-dependent personality effects on color choices and context-driven chart preferences, limiting their use as direct substitutes for human participants in visualization design.
-
Operator Boosting Produces Pareto-Efficient PDE Surrogates
Operator Boosting constructs compact neural-operator PDE surrogates by sequential residual learning with validation-selected shrinkage, yielding 72-95% parameter reduction and accuracy gains on 21 of 30 dataset-architecture pairs.
-
XtrAIn: Training-Guided Occlusion for Feature Attribution
XtrAIn shifts occlusion from input space to parameter space along the training trajectory to produce cleaner feature attributions than standard methods.
-
Evaluating Bivariate Causal Statements Based on Mutual Compatibility
Proposes compatibility scores for bivariate causal statements that quantify plausibility via the confounding implied by the induced multivariate model, plus an incompatibility score based on acyclicity and faithfulness constraints.
-
Inferring infectiousness: a joint model of the within-host viral kinetics of SARS-CoV-2
Bayesian joint model infers infectious virus shedding trajectories and derived infectiousness metrics from PCR and other proxies in SARS-CoV-2 using data from five cohorts of roughly 2000 infections.
-
Branch-Resolved Characterization of Feed-Forward Error in Dynamic Teleportation via Classical Choi Shadows
Branch-resolved Choi-shadow benchmarking of teleportation shows the relative benefit of PROM mitigation vs post-processing flips depending on mid-circuit readout error.
-
Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages
Structured light modes act as collinear interferometer arms, enabling common-path quantitative phase imaging with AFM-level agreement and optional polarization encoding.
-
Context-Based Adversarial Attacks on AI Code Generators: Vulnerability Analysis and Implications
Context-based adversarial attacks raise vulnerable code generation in models like GPT-4 and CodeLlama from 3.5% to 37.4%, with 60-100% transferability, and a dual-layer defense reaches 89.1% detection at low false positives.
-
A Causal Argumentation Method for Explainability of Machine Learning Models
A method that translates causal relationships into a Bipolar Argumentation Framework and applies semi-stable semantics to generate explanatory feature sets for machine learning predictions.
-
Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels
A kernel-based regularized learning framework for FDR control that unifies arbitrary structures and supplies provably valid decision rules with likelihood-based tuning.
-
Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
Post-hoc isotonic regression calibration for deep Cox survival models that improves calibration with theoretical guarantees including double-robustness and asymptotic calibration.
-
crossfit: A Graph-Based Cross-Fitting Engine in R
crossfit is an R package that supplies a general-purpose cross-fitting engine driven by user-specified DAGs of nuisance models with configurable fold allocations and reproducibility features.
-
The Mathematics of AI Winters: The mathematical Taxonomy of Paradigm Fragility in AI Winter
Established mathematical bottlenecks in representation, optimization, complexity, and high-dimensional learning aligned with the central disappointments of early AI research periods.