#autoencoders — Public Fediverse posts

Autoencoders are a type of artificial neural network used for learning efficient codings of input data. They have been a subject of interest in the field of machine learning and artificial intelligence due to their ability to extract and compress the most relevant features from data, making them particularly useful for dimensionality reduction and denoising tasks.

#autoencoders #ai #machine #learning #python

Full article:
https://www.ml-nn.eu/a1/33.html

Natural Language Autoencoders: Turning Claude's Thoughts into Text

https://www.anthropic.com/research/natural-language-autoencoders

#HackerNews #NaturalLanguageProcessing #Autoencoders #Claude #AIResearch #TextGeneration

When Dimensionality Hurts: The Role of #LLM Embedding Compression for Noisy Regression Tasks https://d.repec.org/n?u=RePEc:arx:papers:2502.02199&r=&r=cmp
"… suggest that the optimal dimensionality is dependent on the signal-to-noise ratio, exposing the necessity of feature compression in high noise environments. The implication of the result is that researchers should consider the #noise of a task when making decisions about the dimensionality of text.

… findings indicate that sentiment and emotion-based representations do not provide inherent advantages over learned latent features, implying that their previous success in similar tasks may be attributed to #regularisation effects rather than intrinsic informativeness."
#ML #autoencoders #Overfitting

I just added some extra chapters on #ANN. Since we are using #autoencoders, I thought it could be useful to provide some general introduction on #NeuralNetworks and how they can be tuned.

'Manifold Learning by Mixture Models of VAEs for Inverse Problems', by Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto.

http://jmlr.org/papers/v25/23-0396.html

#autoencoders #manifold #manifolds

'The Power of Contrast for Feature Learning: A Theoretical Analysis', by Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang.

http://jmlr.org/papers/v24/21-1501.html

#autoencoders #supervised #generative

'Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders', by Lisa Bonheme, Marek Grzes.

http://jmlr.org/papers/v24/21-1145.html

#autoencoders #disentangled #representations

New preprint from our group ! 🧠 💻

*Whole-brain modelling of low-dimensional manifold modes reveals organising principle of brain dynamics*
https://www.biorxiv.org/content/10.1101/2023.11.20.567824v1

#brain #modeling #autoEncoders #variationalAutoEncoder #restingStateNetworks #manifold

real-time #anomaly detection
#python #algorithm
#autoencoders #machinelarning TensorFlow User Group (TFUG) Keras scikit-learn #neuralnetwork
#NAB #dataset ambient #temperature from a system that experienced a failure.
#risk #monitoring #maintenance

http://newdigitals.org/2023/10/23/real-time-anomaly-detection-of-nab-ambient-temperature-readings-using-the-tensorflow-keras-autoencoder/

'Lifted Bregman Training of Neural Networks', by Xiaoyu Wang, Martin Benning.

http://jmlr.org/papers/v24/22-0934.html

#autoencoders #classifiers #denoising

The Robustness Limits of SoTA Vision Models to Natural Variation

Mark Ibrahim, Quentin Garrido, Ari S. Morcos, Diane Bouchacourt

Action editor: Dumitru Erhan.

https://openreview.net/forum?id=QhHLwn3D0Y

#autoencoders #robust #vision

Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications

#CVAE #autoencoders

https://arxiv.org/abs/2302.08329

Integrating Bayesian Network Structure into Residual Flows and Variational Autoencoders

Jacobie Mouton, Rodney Stephen Kroon

https://openreview.net/forum?id=OsKXlWamTQ

#autoencoders #generative #flow

'Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data', by Shaowu Pan, Steven L. Brunton, J. Nathan Kutz.

http://jmlr.org/papers/v24/22-0365.html

#shapenet #autoencoders #flow

Sparse Coding with Multi-layer Decoders using Variance Regularization

Katrina Evtimova, Yann LeCun

https://openreview.net/forum?id=4GuIi1jJ74

#autoencoders #sparse #sparser

Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization

David Peer, Bart Keulen, Sebastian Stabinger, Justus Piater, Antonio Rodriguez-sanchez

https://openreview.net/forum?id=LJohl5DnZf

#autoencoders #deep #entropy

'Cauchy–Schwarz Regularized Autoencoder', by Linh Tran, Maja Pantic, Marc Peter Deisenroth.

http://jmlr.org/papers/v23/21-0681.html

#autoencoders #autoencoder #generative