RICA replaces ICA's global generative model with local Riemannian geometry, introducing a disentanglement tensor based on the Hessian of the log-likelihood and Ricci curvature to measure pointwise disentanglement, which recovers sources across manifolds in controlled tests.
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Generative adversarial nets.Advances in neural information processing systems, 27
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Probabilistic circuits have an output bottleneck with convex probability combinations and a context bottleneck limited to fixed vtree-aligned partitions, making them less expressive than transformers for language data with heterogeneous dependencies, though decomposable PCs are strictly more capable
IRIS unifies self-play fine-tuning under an interpolative Rényi objective with adaptive alpha scheduling and reports better benchmark scores than baselines while surpassing full supervised fine-tuning with only 13% of the annotated data.
SEF introduces GAN upsampling for diverse artifacts and expert fusion to reduce domain interference, yielding stronger generalization on 13 benchmarks for AI-generated image detection.
NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
LiBaGS scores and selects synthetic data near decision boundaries using proximity, uncertainty, density, and validity, with boundary-gap allocation and marginal stopping to improve training accuracy.
Penalty-based first-order methods find ε-KKT points in bilevel minimax problems with Õ(ε^{-4}) deterministic and Õ(ε^{-9}) stochastic oracle complexity, improving prior bounds for constrained lower-level cases via Lagrangian duality.
Conservative flows generate by running probability-preserving stochastic dynamics initialized at data points rather than noise, using corrected Langevin or predictor-corrector mechanisms on top of any pretrained flow model and showing gains on Swiss-roll, ImageNet-256 and Oxford Flowers-102.
DiLAST optimizes 3D latents via guidance from a 2D diffusion model to enable generalizable style transfer for OOD styles in 3D asset generation.
FluidFlow uses conditional flow-matching with U-Net and DiT architectures to predict pressure and friction coefficients on airfoils and 3D aircraft meshes, outperforming MLP baselines with better generalization.
An MCMC framework enforces empirical transition laws on GAN outputs to reduce temporal drift in synthetic multivariate time series.
The paper surveys 3D asset generation methods and organizes them around the full production pipeline to assess which outputs meet engine-level requirements for interactive applications.
A radiomics-guided hybrid Vision Transformer integrates pixel embeddings with interpretable radiomic features in a multimodal Cox model for survival analysis, yielding competitive discrimination and clinically meaningful attention maps on COVID-19 chest X-ray data.
LLMs memorize citations hierarchically: titles and first authors are recalled at lower redundancy levels than venues or years, with accuracy scaling log-linearly and saturating near verbatim reproduction above roughly 1200 citations.
citing papers explorer
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Disentanglement Beyond Generative Models with Riemannian ICA
RICA replaces ICA's global generative model with local Riemannian geometry, introducing a disentanglement tensor based on the Hessian of the log-likelihood and Ricci curvature to measure pointwise disentanglement, which recovers sources across manifolds in controlled tests.
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The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
Probabilistic circuits have an output bottleneck with convex probability combinations and a context bottleneck limited to fixed vtree-aligned partitions, making them less expressive than transformers for language data with heterogeneous dependencies, though decomposable PCs are strictly more capable
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IRIS: Interpolative R\'enyi Iterative Self-play for Large Language Model Fine-Tuning
IRIS unifies self-play fine-tuning under an interpolative Rényi objective with adaptive alpha scheduling and reports better benchmark scores than baselines while surpassing full supervised fine-tuning with only 13% of the annotated data.
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Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection
SEF introduces GAN upsampling for diverse artifacts and expert fusion to reduce domain interference, yielding stronger generalization on 13 benchmarks for AI-generated image detection.
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Neural Fields for NV-Center Inverse Sensing
NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
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LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection
LiBaGS scores and selects synthetic data near decision boundaries using proximity, uncertainty, density, and validity, with boundary-gap allocation and marginal stopping to improve training accuracy.
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Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems
Penalty-based first-order methods find ε-KKT points in bilevel minimax problems with Õ(ε^{-4}) deterministic and Õ(ε^{-9}) stochastic oracle complexity, improving prior bounds for constrained lower-level cases via Lagrangian duality.
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Conservative Flows: A New Paradigm of Generative Models
Conservative flows generate by running probability-preserving stochastic dynamics initialized at data points rather than noise, using corrected Langevin or predictor-corrector mechanisms on top of any pretrained flow model and showing gains on Swiss-roll, ImageNet-256 and Oxford Flowers-102.
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Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion
DiLAST optimizes 3D latents via guidance from a 2D diffusion model to enable generalizable style transfer for OOD styles in 3D asset generation.
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FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes
FluidFlow uses conditional flow-matching with U-Net and DiT architectures to predict pressure and friction coefficients on airfoils and 3D aircraft meshes, outperforming MLP baselines with better generalization.
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Preserving Temporal Dynamics in Time Series Generation
An MCMC framework enforces empirical transition laws on GAN outputs to reduce temporal drift in synthetic multivariate time series.
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From Visual Synthesis to Interactive Worlds: Toward Production-Ready 3D Asset Generation
The paper surveys 3D asset generation methods and organizes them around the full production pipeline to assess which outputs meet engine-level requirements for interactive applications.
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Radiomics-Guided Vision Transformers for Survival Analysis
A radiomics-guided hybrid Vision Transformer integrates pixel embeddings with interpretable radiomic features in a multimodal Cox model for survival analysis, yielding competitive discrimination and clinically meaningful attention maps on COVID-19 chest X-ray data.
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Hierarchical Memorization in Large Language Models: Evidence from Citation Generation
LLMs memorize citations hierarchically: titles and first authors are recalled at lower redundancy levels than venues or years, with accuracy scaling log-linearly and saturating near verbatim reproduction above roughly 1200 citations.