#autoencoders — Public Fediverse posts

Natural Language Autoencoders: Turning Claude's Thoughts into Text

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

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

Natural Language Autoencoders: Turning Claude's Thoughts into Text

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

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

Natural Language Autoencoders: Turning Claude's Thoughts into Text

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

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

Natural Language Autoencoders: Turning Claude's Thoughts into Text

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

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

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

Sparse Coding with Multi-layer Decoders using Variance Regularization

Katrina Evtimova, Yann LeCun

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

#autoencoders #sparse #sparser

Sparse Coding with Multi-layer Decoders using Variance Regularization

Katrina Evtimova, Yann LeCun

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

#autoencoders #sparse #sparser

Sparse Coding with Multi-layer Decoders using Variance Regularization

Katrina Evtimova, Yann LeCun

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

#autoencoders #sparse #sparser

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