Equivariant neural networks produce dipole and polarizability surfaces for methanol that enable variational computation of vibrational IR and Raman spectra agreeing with experiment to 2.2 cm^{-1} RMSD on fundamentals.
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits
7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
The Spatial Adapter equips frozen predictors with a spatially regularized orthonormal basis for residuals and derives a closed-form low-rank-plus-noise covariance for spatial prediction and kriging.
VISTA adaptively tunes consistency thresholds in decentralized SGD so that the system converges asymptotically like standard SGD even when adversaries dominate the worker pool.
MC-GenRef performs annotation-free microcalcification segmentation via synthetic data from a lightweight image formation model plus test-time generative posterior refinement with a rectified-flow generator, yielding top Dice on INbreast and gains on an external cohort.
LayerPipe2 derives per-layer delay assignments for multistage pipelined training and uses an improved moving average to recompute past weights without explicit storage.
A domain-adapted diffusion model synthesizes heterogeneous PET images from uniform organ activity maps, achieving high quantitative accuracy (CCC > 0.92) and visual realism comparable to real scans.
citing papers explorer
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Vibrational infrared and Raman spectra of the methanol molecule with equivariant neural-network property surfaces
Equivariant neural networks produce dipole and polarizability surfaces for methanol that enable variational computation of vibrational IR and Raman spectra agreeing with experiment to 2.2 cm^{-1} RMSD on fundamentals.
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A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
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Spatial Adapter: Structured Spatial Decomposition and Closed-Form Covariance for Frozen Predictors
The Spatial Adapter equips frozen predictors with a spatially regularized orthonormal basis for residuals and derives a closed-form low-rank-plus-noise covariance for spatial prediction and kriging.
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\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments
VISTA adaptively tunes consistency thresholds in decentralized SGD so that the system converges asymptotically like standard SGD even when adversaries dominate the worker pool.
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MC-GenRef: Annotation-free mammography microcalcification segmentation with generative posterior refinement
MC-GenRef performs annotation-free microcalcification segmentation via synthetic data from a lightweight image formation model plus test-time generative posterior refinement with a rectified-flow generator, yielding top Dice on INbreast and gains on an external cohort.
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LayerPipe2: Multistage Pipelining and Weight Recompute via Improved Exponential Moving Average for Training Neural Networks
LayerPipe2 derives per-layer delay assignments for multistage pipelined training and uses an improved moving average to recompute past weights without explicit storage.
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Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model
A domain-adapted diffusion model synthesizes heterogeneous PET images from uniform organ activity maps, achieving high quantitative accuracy (CCC > 0.92) and visual realism comparable to real scans.