ML Fundamentals Journal Club
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ML Fundamentals Journal Club
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Last edited by
fhuszar
on
Nov 2, 2021
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Új témák MLJC-re
Contrastive Learning
Contrastive Learning Inverts the Data Generating Process - Roland/Yash/Wieland
CPC
Contrastive Representation Learning
blogpost
Meta-learning
(Feri)
MAML and Reptile [
1
,
2
]
implicit function theorem and iMAML [
1
,
2
]
Natural Neural Networks and WarpGrad [
1
,
2
]
invite: Chelsea Finn
Natural Gradient Descent
(Feri)
NGD
K-FAC and Exact NGD in Linear Networks
Wasserstein natural gradients
invite: Michael Arbel
Differential Privacy
Algorithmic foundations of Differential Privacy [
1
: approx. Chapters 1-3]
DP-SGD [
1
]
?
invite: ??/Andrew Trask
Geometric Deep Learning
Group Equivariant CNNs
Spherical CNNs
Gauge Invariant CNNs
invited speaker: Kondor Risi, Taco Cohen
Inference in Graphical Models
Belief Propagation
Viterbi, forward-backward
ADF and EM
Variational Message Passing
invite: Tom Minka
Continual Learning
EWC
Learning without Forgetting
Variational Continual Learning
invite: Raia Hadsell
PCA Extended
(Patrik)
Contrastive PCA
paper
Probabilistic Contrastive PCA
paper
Variational Autoencoders Pursue PCA Directions (by Accident)
paper
Reinforcement Learning
Neu Gergely
Causal Inference
Önreklám - Patrik blogja
Few-shot/small data
Domain adaptation
VAEs & Co
VAEBM
PCA-VAE
iVAE
EBMs
MCMC
Score Matching
VAEBM
How to train your EBM
Network & Data visualization
Saliency map
Feature visualization
Interpretability
UMAP :
On UMAP's true loss function
t-SNE
post
Laplacian eigenmaps
Locally linear embeddig
Isomaps
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