GPTQ-intrinsic LoRA augments GPTQ with intrinsic low-rank compensation via Hessian modification to achieve layer-wise reconstruction bounds that match information-theoretic lower bounds under structural assumptions.
arXiv preprint 2306.12418 (2023)
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
verdicts
UNVERDICTED 10representative citing papers
Establishes non-identifiability results and query lower bounds showing transpose-free matvec access provides limited information for core linear algebra tasks.
A framework maps Boltzmann-weighted lattice configurations to correlated random matrix ensembles via real-space to momentum-space variance profiles, deriving spectral moments and resolvent densities benchmarked on Ising and Edwards-Anderson models.
Nyström's method always yields higher-accuracy leading eigenvalues than Rayleigh-Ritz for positive semi-definite matrices given a subspace approximation, with improvements that can be arbitrarily large.
CSULoRA decomposes LoRA updates into fully aligned, partially aligned, and off-subspace components and solves a closed-form penalized minimum-change problem to preserve safe parts while attenuating unsafe directions.
Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
A linear-combination conditioning strategy for GPs achieves exponential convergence to machine precision with r≈100 combinations at O(Tr²) cost for smooth kernels and simple domains.
The filter echo generalizes diffusion echoes for visualizing nonlinear filters beyond adaptive smoothing and adds a compression method that cuts storage needs by a factor of 20 to 100.
Randomized subspace iteration improves low-rank approximation quality over randomized SVD for pretrained models by using power iterations to enhance spectral separation, preserving predictive accuracy better under aggressive compression.
Direct SVD solves coupled decompositions; randomized versions with novel balanced subspace selection improve efficiency and apply to face recognition.
citing papers explorer
-
Transpose-free linear algebra
Establishes non-identifiability results and query lower bounds showing transpose-free matvec access provides limited information for core linear algebra tasks.
-
Finding accurate eigenvalues and eigenvectors of positive semi-definite matrices given a subspace
Nyström's method always yields higher-accuracy leading eigenvalues than Rayleigh-Ritz for positive semi-definite matrices given a subspace approximation, with improvements that can be arbitrarily large.
-
Accelerating Power Method with Fast Sketching for Stronger Low-Rank Approximation
Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
-
Randomized coupled decompositions
Direct SVD solves coupled decompositions; randomized versions with novel balanced subspace selection improve efficiency and apply to face recognition.