CHASM introduces a cross-frequency harmonized axis-separable spectral mixer using a shared channel eigenbasis plus per-frequency positive gains, yielding consistent gains over same-backbone baselines in medical and natural image tasks.
hub
Guibas , author M
14 Pith papers cite this work. Polarity classification is still indexing.
14
Pith papers citing it
hub tools
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 14roles
background 2polarities
background 2representative citing papers
LatentFT uses latent-space Fourier transforms and frequency masking in diffusion autoencoders to enable timescale-specific manipulation of musical structure in generative models.
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
ObsCast produces skillful short-term high-resolution weather analyses and forecasts over the contiguous US and Europe using only observational data, outperforming operational NWP without relying on NWP-derived data for training or inference.
AOT-POT adaptively reshapes complex PDE solution operators via input-dependent transformations and parallel stream mixing to enable effective large-scale pre-training, yielding SOTA results on 12 benchmarks with minimal added parameters.
U-HNO uses adaptive per-point routing in a U-shaped hybrid architecture to achieve state-of-the-art accuracy on PDE benchmarks with sharp localized features.
ABLE learns a spatially adaptive Parseval frame from data via an ancillary density to replace fixed bases in spectral neural operators for PDEs.
LESnets integrates LES equations and the law of the wall into F-FNO to enable data-free, stable long-term predictions of wall-bounded turbulence at Re_tau up to 1000 on coarse grids, matching traditional LES accuracy at higher efficiency.
ESFM is a single open foundation model that unifies heterogeneous Earth data sources and forecasts missing regions while preserving inter-variable physical relationships.
Multi-scale wavelet transformers learn operator dynamics of chaotic systems in the wavelet domain, yielding lower errors and higher spectral fidelity on benchmarks and ERA5 climate data.
Neural operators supply warm-start guesses that cut iteration counts and runtime by up to 90% in Krylov solvers for PDEs while retaining the original methods' convergence guarantees.
Neptune infers spatiotemporal parameter fields in PDEs from as few as 45 sparse measurements using independent coordinate neural networks, outperforming PINNs and neural operators with lower errors and better extrapolation.
Caracal is a Fourier-based sequence mixing architecture that achieves causal autoregressive modeling with standard operators and competitive performance on long sequences.
EDNO redefines pansharpening as a frequency-domain functional mapping that decouples fusion via Euler-inspired polar coordinates into explicit phase-rotation simulation and implicit spectral modeling for improved efficiency.
citing papers explorer
-
CHASM: Cross-frequency Harmonized Axis-Separable Mixing for Spectral Token Operators
CHASM introduces a cross-frequency harmonized axis-separable spectral mixer using a shared channel eigenbasis plus per-frequency positive gains, yielding consistent gains over same-backbone baselines in medical and natural image tasks.
-
Latent Fourier Transform
LatentFT uses latent-space Fourier transforms and frequency masking in diffusion autoencoders to enable timescale-specific manipulation of musical structure in generative models.
-
Non-markovian neural quantum propagator and its application to the simulation of ultrafast nonlinear spectra
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
-
Skillful high-resolution weather forecasting independent of physical models
ObsCast produces skillful short-term high-resolution weather analyses and forecasts over the contiguous US and Europe using only observational data, outperforming operational NWP without relying on NWP-derived data for training or inference.
-
AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training
AOT-POT adaptively reshapes complex PDE solution operators via input-dependent transformations and parallel stream mixing to enable effective large-scale pre-training, yielding SOTA results on 12 benchmarks with minimal added parameters.
-
U-HNO: A U-shaped Hybrid Neural Operator with Sparse-Point Adaptive Routing for Non-stationary PDE Dynamics
U-HNO uses adaptive per-point routing in a U-shaped hybrid architecture to achieve state-of-the-art accuracy on PDE benchmarks with sharp localized features.
-
Don't Fix the Basis -- Learn It: Spectral Representation with Adaptive Basis Learning for PDEs
ABLE learns a spatially adaptive Parseval frame from data via an ancillary density to replace fixed bases in spectral neural operators for PDEs.
-
Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence
LESnets integrates LES equations and the law of the wall into F-FNO to enable data-free, stable long-term predictions of wall-bounded turbulence at Re_tau up to 1000 on coarse grids, matching traditional LES accuracy at higher efficiency.
-
Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting
ESFM is a single open foundation model that unifies heterogeneous Earth data sources and forecasts missing regions while preserving inter-variable physical relationships.
-
Multi-Scale Wavelet Transformers for Operator Learning of Dynamical Systems
Multi-scale wavelet transformers learn operator dynamics of chaotic systems in the wavelet domain, yielding lower errors and higher spectral fidelity on benchmarks and ERA5 climate data.
-
NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers
Neural operators supply warm-start guesses that cut iteration counts and runtime by up to 90% in Krylov solvers for PDEs while retaining the original methods' convergence guarantees.
-
Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
Neptune infers spatiotemporal parameter fields in PDEs from as few as 45 sparse measurements using independent coordinate neural networks, outperforming PINNs and neural operators with lower errors and better extrapolation.
-
Caracal: Causal Architecture via Spectral Mixing
Caracal is a Fourier-based sequence mixing architecture that achieves causal autoregressive modeling with standard operators and competitive performance on long sequences.
-
Euler-inspired Decoupling Neural Operator for Efficient Pansharpening
EDNO redefines pansharpening as a frequency-domain functional mapping that decouples fusion via Euler-inspired polar coordinates into explicit phase-rotation simulation and implicit spectral modeling for improved efficiency.