Tootfinder

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Kristi Topollai, Anna Choromanska
https://arxiv.org/abs/2510.04988 https://

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
The vast majority of modern deep learning models are trained with momentum-based first-order optimizers. The momentum term governs the optimizer's memory by determining how much each past gradient contributes to the current convergence direction. Fundamental momentum methods, such as Nesterov Accelerated Gradient and the Heavy Ball method, as well as more recent optimizers such as AdamW and Lion, all rely on the momentum coefficient that is customarily set to $β= 0.9$ and kept constant during …

i got a bit obsessed with the modern lovers this year & attempted to assemble a complete-as-possible 1970-1974 live chronology. it's far from complete, but found lots of fun stuff, including a setlist for what might be the 1st gig & ads for their shows in bermuda. https://jessejarnow.com/2025/12/the-mo<…

Still thinking about this on the way home...
"maybe we should just give up entirely on optimism or pessimism—we have to do this work no matter how we feel about it. So by force of will or the sheer default of emergency we make ourselves have utopian thoughts and ideas. "
#NCKF25 with an astoundingly profound talk by @mikkelkfrantzen.bsky.social who reminds us of Kim Stanley Robinson's essay dystopias now. The work starts here.
  https://communemag.com/dystopias-now/

Das US-Unternehmen #Zanskar hat mit „Big Blind“ in Nevada das erste kommerziell nutzbare, "blinde" #Geothermie-System seit über 30 Jahren entdeckt.
Die KI-gestützte Erkundung ermöglichte Bohrungen, die bereits in 2.700 Metern Tiefe ein heißes, durchlässiges Reservoir aufzeigten. D…

Zanskar Reveals ‘Big Blind’ - The Discovery of the First Blind Geothermal System in the U.S. by Industry in Over 30 Years
Located in the western Nevada desert, ‘Big Blind’ is Zanskar’s third geothermal discovery in the past year alone, demonstrating the speed and repeatability of its AI-native platform

"Aber dem Kindelein so klein wird in der Messe zur Mitternacht die Brust gegeben werden" sprach der Prophet - und so geschah es.
#WarumAuchNicht #FirstTimes #HoHoHo

"Europa wird im Digitalen nur souverän sein, wenn es seine eigene Rolle richtig versteht: nicht als Festung, nicht als Kopie amerikanischer Tech-Konzerne, sondern als Gestalter industriellen Deep Techs, als Produzent von Präzision, Energieeffizienz und strategischen Technologien, die niemand ersetzen kann. Souveränität wäre dann nicht das Pathos des Ausnahmezustands, sondern die Nüchternheit der Gestaltungskraft." @…

Die verwechselte Souveränität – Warum Europa im Digitalen nicht herrschen, sondern handeln lernen muss - ichsagmal.com
Souveränität ist zu einem Wort geworden, das in Europa auf jeder Bühne erklingt: auf dem Digitalgipfel in Berlin, in vertraulichen Runden in Brüssel, in Ministerreden und Strategiepapiersätzen, die klingen wie Durchhalteformeln einer verunsicherten Moderne. Doch je häufiger der Begriff ertönt, desto leerer wirkt er. Fragt man genauer nach, heißt es beschwichtigend: Natürlich ist nicht Autarkie … Die verwechselte Souveränität – Warum Europa im Digitalen nicht herrschen, sondern ha…

Rounding off the first day of #NCKF25 with an astoundingly profound talk by @mikkelkfrantzen.bsky.social who reminds us of Kim Stanley Robinson's essay dystopias now. The work starts here.
https://communemag.com/dystopias-now/

Beyond the stars: Linking H$\alpha$ sizes, kinematics, and star formation in galaxies at $z\approx 4-6$ with JWST grism surveys and $\texttt{geko}$
A. Lola Danhaive, Sandro Tacchella, William McClymont, Brant Robertson, Stefano Carniani, Courtney Carreira, Eiichi Egami, Andrew J. Bunker, Emma Curtis-Lake, Daniel J. Eisenstein, Zhiyuan Ji, Benjamin D. Johnson, Marcia Rieke, Natalia C. Villanueva, Christopher N. A. Willmer, Chris Willot, Zihao Wu, Yongda Zhu

Beyond the stars: Linking H$α$ sizes, kinematics, and star formation in galaxies at $z\approx 4-6$ with JWST grism surveys and $\texttt{geko}$
Understanding how galaxies assemble their mass during the first billion years of cosmic time is a central goal of extragalactic astrophysics, yet joint constraints on their sizes and kinematics remain scarce. We present one of the first statistical studies of the $\mathrm{H}α$ size-mass relation at high redshift with a sample of 213 galaxies at spectroscopic redshifts of $z\approx 4-6$ from the FRESCO and CONGRESS NIRCam grism surveys. We measure the $\mathrm{H}α$ morphology and kinematics of…

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Jack Jackman, David Ryan, Arun Narayanan, Pedro Nardelli, Indrakshi Dey
https://arxiv.org/abs/2510.11286

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Modern power grids face an acute mismatch between where data is generated and where it can be processed: protection relays, EV (Electric Vehicle) charging, and distributed renewables demand millisecond analytics at the edge, while energy-hungry workloads often sit in distant clouds leading to missed real-time deadlines and wasted power. We address this by proposing, to our knowledge, the first-ever SDEN (Software Defined Energy Network) for CaaS (Computations-as-a-Service) that unifies edge, fo…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
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