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📐 Matrix Factorization
SVD, NMF, Low-Rank Approximation, Dimensionality Reduction
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112169
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489.4
ms
Low-rank approximation of
Rippa
method for
RBF
interpolation
arxiv.org
·
1d
⛰️
Gradient Descent
A New Angle on Quantum
Subspace
Diagonalization
for Quantum Chemistry
arxiv.org
·
9h
🗺️
Manifold Learning
Issue 638
datascienceweekly.substack.com
·
19h
·
Discuss:
Substack
📊
Differential Expression
Document
Clustering
with LLM Embeddings in
Scikit-learn
machinelearningmastery.com
·
3d
🗺️
UMAP
Informational
Inequivalence
of Formally Equivalent Mathematical Frameworks: A Multi-Framework Analysis of the Inverse Square Law
zenodo.org
·
9h
·
Discuss:
Hacker News
📊
Empirical Bayes
LSEnet
: A Smarter Way to Organize Data Using
Curved
Space
hackernoon.com
·
9h
🔲
Zarr
Atomistic
, but non-complete
lattices
dominiczypen.wordpress.com
·
4h
🦠
Whole cell model
Diffusion Models for
ARC-AGI
: A
Retrospective
christopherhwood.com
·
1d
·
Discuss:
Hacker News
🗺️
UMAP
Building an Embedding API with Rust, Arm, and
EmbeddingGemma
on AWS
Lambda
sobolev.substack.com
·
3h
·
Discuss:
Substack
🔲
Zarr
MAformer
: A multivariate prediction framework with adaptive multi-scale decomposition and phase correction for water quality in
aquaculture
environments
sciencedirect.com
·
3h
🔥
ComplexHeatmap
Partners
savagetaiga.bearblog.dev
·
1d
🧬
Bioconductor
Designing
Polynomials
Using the Sum of
Gradients
at the Roots
pub.towardsai.net
·
23h
⛰️
Gradient Descent
A History of Large Language Models
gregorygundersen.com
·
1d
🔢
Embeddings
Reconstructing
into a
denser
phase
nature.com
·
2h
🦠
Whole cell model
Generalized
Lanczos
method for systematic optimization of neural-network quantum states
link.aps.org
·
1d
⛰️
Gradient Descent
UbiquitousLearning/mllm
: Fast Multimodal LLM on Mobile Devices
github.com
·
1d
⛰️
Gradient Descent
Choosing Between
PCA
and
t-SNE
for Visualization
machinelearningmastery.com
·
1d
🗺️
Manifold Learning
Space Alignment Matters: The Missing Piece for
Inducing
Neural Collapse in
Long-Tailed
Learning
sonomarpa.sonoma.lib.ca.us
·
15h
⛰️
Gradient Descent
v6 (Code 2 here) — Most complete architecture. This version is faster than my old v5,
statistically
correct, has all the advanced psychology/network features, and produces stunning
visualizations
gist.github.com
·
20h
·
Discuss:
r/C_Programming
🗺️
Network Visualization
high
error
rate in
text-embedding-3-small
status.openai.com
·
1d
🔢
Embeddings
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