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🗺️ Manifold Learning
t-SNE, Diffusion Maps, Nonlinear Reduction, Geometry
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112806
posts in
375.3
ms
Grassmannian
Manifold
Learning: Optimization and Deep Learning Architectures
hackernoon.com
·
1d
🗺️
UMAP
Learning on the
Manifold
: Unlocking Standard Diffusion Transformers with Representation
Encoders
arxiv.org
·
1d
🔢
Embeddings
Choosing Between
PCA
and
t-SNE
for Visualization
machinelearningmastery.com
·
14h
🗺️
UMAP
Granulometric
Smoothing
on Manifolds
arxiv.org
·
20h
🗺️
UMAP
Unsupervised Learning on Manifolds:
Spherical
Clustering and
Kuramoto
Ensembles
hackernoon.com
·
2d
🗺️
UMAP
Gibbs Measures from Deep Shaped
Multilayer
Perceptrons
link.aps.org
·
12h
📊
Empirical Bayes
BetaZero
V2: A Diffusion Model for Setting
Boulder
Problems
evmojo37.substack.com
·
2h
·
Discuss:
Substack
⛰️
Gradient Descent
The feature space for
drifting
models
breno.bearblog.dev
·
1d
⛰️
Gradient Descent
Architectural and Mathematical
Foundations
of Machine Learning: A
Rigorous
Synthesis of Theory, Geometry, and Implementation
chizkidd.github.io
·
1d
·
Discuss:
Hacker News
⛰️
Gradient Descent
Show HN: Solving
Sudoku
reasoning via Energy
Geometric
models
davisgeometric.com
·
16h
·
Discuss:
Hacker News
📐
Computational Geometry
Diffusion Models for
ARC-AGI
: A
Retrospective
christopherhwood.com
·
1d
·
Discuss:
Hacker News
🗺️
UMAP
Transferable
enantioselectivity
models from sparse data
nature.com
·
1d
🔥
ComplexHeatmap
Greenhouse gas rebound effects on
disruptive
events via
SHAP-assisted
neural networks
sciencedirect.com
·
13h
⛰️
Gradient Descent
Show HN: A
segmentation
model client-side via
WASM
qtoolkit.dev
·
12h
·
Discuss:
Hacker News
🗺️
UMAP
Carnegie
Mellon at
NeurIPS
2025
blog.ml.cmu.edu
·
1d
🗺️
UMAP
Product
Forecasting
through Time Series Analysis (
Modelling
)
pub.towardsai.net
·
1h
📈
Linear Models
Statistics-encoded
tensor network approach in
disordered
quantum many-body spin chains
link.aps.org
·
15h
📐
Matrix Factorization
Navigating high-dimensional processing parameters in organic
photovoltaics
via a
multitier
machine learning framework
science.org
·
1d
🗺️
UMAP
Image Classification with
CNNs
– Part 3: Understanding Max
Pooling
and Results
dev.to
·
5h
·
Discuss:
DEV
🗺️
UMAP
A training
principle
for
drifting
models
breno.bearblog.dev
·
14h
⛰️
Gradient Descent
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