#generativemodel — Public Fediverse posts

I invented a new generative model and got accepted to ICLR

https://discrete-distribution-networks.github.io/

#HackerNews #generativemodel #ICLR #invention #machinelearning #hackernews

Spectral Labs releases SGS-1: the first generative model for structured CAD

https://www.spectrallabs.ai/research/SGS-1

#HackerNews #SpectralLabs #SGS1 #GenerativeModel #CAD #Technology #Innovation

Once again it’s time for a quick update of activity at the Open Journal of Astrophysics. Since the last update a week ago we have published  four papers, which takes the count in Volume 7 (2024) up to 102 and the total published altogether by OJAp up to 217.   This means not only that we have reached a century for the year but also that so far in 2024 we have published more than double the number of papers that we published in all of 2023. I blogged about the significance of the figure 217 here.

In chronological order, the four papers published this week, with their overlays, are as follows. You can click on the images of the overlays to make them larger should you wish to do so.

First one up is “A generative model for Gaia astrometric orbit catalogs: selection functions for binary stars, giant planets, and compact object companions” by Kareem El-Badry (Caltech, USA), Casey Lam (Carnegie Observatories), Berry Holl & Jean-Louis Halbwachs (U. Geneva), Hans-Walter Rix (MPA Heidelberg, Germany), Tsevi Mazeh (Tel Aviv, Israel) and Sahar Shahaf (Weizmann Institute of Science, Israel). This one is in the folder Solar and Stellar Astrophysics. The paper presents a forward method for estimating the selection function (i.e. the probability of a system with a given set of parameters being included in a catalog). It was published on November 4th 2024.

Here is a screen grab of the overlay, which includes the abstract:

You can find the officially accepted version of the paper on the arXiv here.

The second paper to announce, published on 5th November 2024. is “Primordial magnetogenesis in a bouncing model with dark energy” by Marcus V. Bomfim (Rio de Janeiro, Brazil), Emmanuel Frion (Western U. Canada), Nelson Pinto-Neto (Espírito Santo, Brazil), and Sandro D. P. Vitenti (Paraná, Brazil). This paper, in the section on Cosmology and NonGalactic Astrophysics, presents a discussion of the possible generation of magnetic fields on cosmological scales by in a model involving a scalar field coupled to electromagnetism

You can see the overlay here:

The accepted version of this paper can be found on the arXiv here.

The third paper, published on 6th November 2024 in the folder marked Astrophysics of Galaxies, is called  “Evidence for large scale compressible turbulence in the ISM of CSWA13, a star-Forming Lensed Galaxy at z = 1.87 with outflowing wind” by Itzhak Goldman (Tel Aviv, Israel). It presents a statistical analysis of the spatial distribution and kinematics of nebular gas with discussion of the nature of the turbulence present.

Here is the overlay

The final version accepted on arXiv is here.

Last in this batch is “Star formation in the high-extinction Planck cold clump PGCC G120.69+2.66” by Anlaug Amanda Djupvik (Aarhus, Denmark), João L. Yun (Lisbon, Portugal), and Fernando Comerón (ESO, Garching, Germany). It was published on 7th November 2024 in the folder marked Astrophysics of Galaxies. The paper uses imaging and spectroscopy  information to identify sites of star formation in a molecular cloud. This is the overlay:

You can find the official accepted version on the arXiv here.

That’s all for now. I will post another update in a week.

https://telescoper.blog/2024/11/09/four-new-publications-at-the-open-journal-of-astrophysics-9/

#240905329v3 #arXiv240515469v2 #arXiv241100088v1 #astrometry #AstrophysicsOfGalaxies #CosmologyAndNonGalacticAstrophysics #DiamondOpenAccess #GAIA #generativeModel #GravitationalLensing #Orbits #PlanckColdClump #primordialMagneticFields #SolarAndStellarAstrophysics #srtarFormation #starFormingGalaxy #TheOpenJournalOfAstrophysics

Once again it’s time for a quick update of activity at the Open Journal of Astrophysics. Since the last update a week ago we have published  four papers, which takes the count in Volume 7 (2024) up to 102 and the total published altogether by OJAp up to 217.   This means not only that we have reached a century for the year but also that so far in 2024 we have published more than double the number of papers that we published in all of 2023. I blogged about the significance of the figure 217 here.

In chronological order, the four papers published this week, with their overlays, are as follows. You can click on the images of the overlays to make them larger should you wish to do so.

First one up is “A generative model for Gaia astrometric orbit catalogs: selection functions for binary stars, giant planets, and compact object companions” by Kareem El-Badry (Caltech, USA), Casey Lam (Carnegie Observatories), Berry Holl & Jean-Louis Halbwachs (U. Geneva), Hans-Walter Rix (MPA Heidelberg, Germany), Tsevi Mazeh (Tel Aviv, Israel) and Sahar Shahaf (Weizmann Institute of Science, Israel). This one is in the folder Solar and Stellar Astrophysics. The paper presents a forward method for estimating the selection function (i.e. the probability of a system with a given set of parameters being included in a catalog). It was published on November 4th 2024.

Here is a screen grab of the overlay, which includes the abstract:

You can find the officially accepted version of the paper on the arXiv here.

The second paper to announce, published on 5th November 2024. is “Primordial magnetogenesis in a bouncing model with dark energy” by Marcus V. Bomfim (Rio de Janeiro, Brazil), Emmanuel Frion (Western U. Canada), Nelson Pinto-Neto (Espírito Santo, Brazil), and Sandro D. P. Vitenti (Paraná, Brazil). This paper, in the section on Cosmology and NonGalactic Astrophysics, presents a discussion of the possible generation of magnetic fields on cosmological scales by in a model involving a scalar field coupled to electromagnetism

You can see the overlay here:

The accepted version of this paper can be found on the arXiv here.

The third paper, published on 6th November 2024 in the folder marked Astrophysics of Galaxies, is called  “Evidence for large scale compressible turbulence in the ISM of CSWA13, a star-Forming Lensed Galaxy at z = 1.87 with outflowing wind” by Itzhak Goldman (Tel Aviv, Israel). It presents a statistical analysis of the spatial distribution and kinematics of nebular gas with discussion of the nature of the turbulence present.

Here is the overlay

The final version accepted on arXiv is here.

Last in this batch is “Star formation in the high-extinction Planck cold clump PGCC G120.69+2.66” by Anlaug Amanda Djupvik (Aarhus, Denmark), João L. Yun (Lisbon, Portugal), and Fernando Comerón (ESO, Garching, Germany). It was published on 7th November 2024 in the folder marked Astrophysics of Galaxies. The paper uses imaging and spectroscopy  information to identify sites of star formation in a molecular cloud. This is the overlay:

You can find the official accepted version on the arXiv here.

That’s all for now. I will post another update in a week.

https://telescoper.blog/2024/11/09/four-new-publications-at-the-open-journal-of-astrophysics-9/

#240905329v3 #arXiv240515469v2 #arXiv241100088v1 #astrometry #AstrophysicsOfGalaxies #CosmologyAndNonGalacticAstrophysics #DiamondOpenAccess #GAIA #generativeModel #GravitationalLensing #Orbits #PlanckColdClump #primordialMagneticFields #SolarAndStellarAstrophysics #srtarFormation #starFormingGalaxy #TheOpenJournalOfAstrophysics

Loopy, an AI model that generates lifelike portrait motion from audio input alone! 🎥✨ Loopy uses advanced temporal modules to sync natural movements like eye, eyebrow, and head motions with different audio types, whether it's speech, sighs, or even singing.
#AI #DeepLearning #AudioDrivenAI #GenerativeModel #VideoSynthesis #Bytedance
https://loopyavatar.github.io/

Great post to follow along to learn diffusion model by implementing it from scratch. The distance function based perspective is new to me and makes a lot of sense with the visuals. https://www.chenyang.co/diffusion.html #Diffusion #GenerativeModel #FromScratch (via https://message.haoxiang.org)

Distillation on top of k-rectified Flow makes a one-step SD called InstaFlow. Nice work. The reasoning makes sense to me, although I didn't fully get why it works. The overall workflow looks like a sophisticated distillation pipeline to me. Now this paper shows that it is possible to do it in one step, there must be some other way to get a similar linearly coupled target distribution. https://arxiv.org/abs/2309.06380 #Diffusion #GenerativeModel (via https://message.haoxiang.org)

A nice way to model how one distribution transforms into another. The time-dependent vector field defines the flow and hence the probabilistic path across distributions. Interestingly diffusion model can be viewed as a special instance of this flow matching framework and one can alternatively define the path following optimal transportation theory as well. https://arxiv.org/abs/2210.02747 #Diffusion #GenerativeModel (via https://message.haoxiang.org)

GZIP is all you need ...

Language modeling is compression

https://arxiv.org/abs/2309.10668

"... Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model."

#compression #generativemodel

There will be two interesting #ComputationalNeuroscience seminars this Wednesday, both online:

Simon Danner, "#ComputationalModels of spinal locomotor circuitry"
⏰ Wednesday, June 14, 2023 10:00 AM CET
🌎 https://www.world-wide.org/seminar/9337/

Neil Burgess, "Understanding our #memory system as a #GenerativeModel"
⏰ Wednesday, June 14, 2023 3:00 PM CET
🌎 https://www.ucl.ac.uk/research/domains/events/2023/jun/ucl-neuroai-talk-series-professor-neil-burgess

#compneuro #neuroscience

A #deeplearning model predicting unmeasured transcriptomic responses of single cells to single or combinatorial perturbations (drugs, gene knockouts etc.) ➡️ https://www.embopress.org/doi/full/10.15252/msb.202211517
from Fabian Theis @HelmholtzMunich
#machinelearning #GenerativeModel #systemsbiology #singlecell

For anyone interested in AI-generated assets you might want to take a look at this paper from NVIDIA:

"GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images"

https://nv-tlabs.github.io/GET3D/

The code is available on GitHub:

https://github.com/nv-tlabs/GET3D

#AI #AIArt #3D #3DModel #GET3D #GenerativeModel #GameDev #NVIDIA