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📊 propensity models
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186232
posts in
22.6
ms
Think About Control
Variables
🎲
Probability
cremieux.xyz
·
2d
Embedding multilayer RNA networks for lncRNA-miRNA interaction prediction via
LMI-MHGAT
🧮
Vector Databases
link.springer.com
·
21h
The Half-Life of a
Moat
(Part 1)
🛠️
Feature Engineering
semistructured.substack.com
·
4d
·
Substack
To Train or Not to Train (10 minute read)
🛠️
Feature Engineering
tanayj.com
·
2d
“Introduction to R, Regression, and the
rms
Package”: short course by Frank
Harrell
🤖
Machine Learning
r-bloggers.com
·
1d
How to Keep Your Brain
Sharp
: A Practical Playbook Beyond the
Basics
🧠
ADHD Productivity
tim.blog
·
6d
·
Hacker News
DNNs
, Dataset Statistics, and
Correlation
Functions
🤖
Machine Learning
philsci-archive.pitt.edu
·
1d
[ASAP] A Comparative Study of
QSPR
Methods on a Unique Multitask
PAMPA
Data Set
📊
Data Science
dx.doi.org
·
6d
How to build custom reasoning agents with a
fraction
of the
compute
🤖
Transformers
venturebeat.com
·
1d
Vibe
Training - Auto Train a Small Language Model for Your Use Case
🤖
Transformers
diamantai.substack.com
·
2d
·
Substack
,
r/LocalLLaMA
Tracing
the rise of
biomedical
foundation models
📊
Data Science
nature.com
·
10h
What are
embeddings
?
🧮
Vector Databases
vickiboykis.com
·
4d
Discrepancies
between empirical and
theoretical
probability in human binary choices within the game of Go
🎲
Probability
frontiersin.org
·
16h
Learning zero, and what
SLT
gets wrong about it
🤖
Machine Learning
lesswrong.com
·
1d
Stabilizing higher-order self-organized operational neural networks via adaptive order-aware constraints for ECG-based
myocardial
infarction
detection
🛠️
Feature Engineering
sciencedirect.com
·
6h
Distilling
a Tiny Model for Fast
Interpretability
🧮
Vector Databases
ethanfast.substack.com
·
2d
·
Substack
The
Metropolis-Hastings
algorithm
🎲
Probability
blog.djnavarro.net
·
5d
Extending Evidence
Accumulation
Models to
Bounded
Continuous Self-report Data
🎲
Probability
arxiv.org
·
17h
Large Language Models in
Communications
🛠️
Feature Engineering
databricks.com
·
1d
[ASAP]
Pseudodata-Guided
Invariant Representation Learning Boosts the Out-of-Distribution Generalization in
Enzymatic
Kinetic Parameter Prediction
🧩
Sparse Autoencoders
dx.doi.org
·
3d
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