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Here are some goals we set at the 4/15 meeting:
- "Nearby" function to get a close Toeplitz, Hankel, Cauchy, etc. approximation to a given matrix
- "Norms" fast norm computations for our structure…
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The following paper presented a novel approach for adapting the metric:
Ben Bales, Arya Pourzanjani, Aki Vehtari, Linda Petzold, Selecting the Metric in Hamiltonian Monte Carlo, 2019. https://arxiv…
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Hi there,
Among the classical denoising technique, low-rank approximation (a.k.a PCA) is a widely known technique, and the so-called singular value threshold corresponds to the proximal operator of…
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The low-rank update is implemented for the `VFE` sparse approximation, but this should of course be just as easy for an exact `FiniteGP`.:)
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In [1] a general low-rank inducing framework was introduced and showed many benefits compared to e.g. the nuclear norm when applied to common computer vision tasks. This was later generalized in [2].
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Paper references (Thanks @evanmiller)
- [Numerical Optimizations for Weighted Low-rank Estimation on Language Model](https://arxiv.org/abs/2211.09718) - looks like these authors have rediscovered …
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I have read your paper "Enhanced Nonconvex Low-Rank Approximation ofTensor Multi-Modes for Tensor Completion", can you offer me some code on your work?
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When I ran scMetabolism to quantify metabolic activity ,I met some problem:
library(Seurat)
library(scMetabolism)
scRNA dense coercion: allocating vector of size 5.9 GiB
Is there anyone encounter …
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### 🚀 The feature, motivation and pitch
Hi,
Currently, the `torch.svd_lowrank` function implements a very basic version of Halko, et al. (2009)'s algorithm. The `sklearn` version, `sklearn.utils.e…
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