Tootfinder

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Gon\c{c}alo Faria, Noah A. Smith
https://arxiv.org/abs/2504.03790 https://mastoxiv.page/@arXiv_csCL_bot/114301112970577326
- A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten M{\o}rup
https://arxiv.org/abs/2504.12392 https://mastoxiv.page/@arXiv_statME_bot/114357826909813483
- The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
https://arxiv.org/abs/2505.11622 https://mastoxiv.page/@arXiv_statML_bot/114539065460187982
- BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
https://arxiv.org/abs/2505.12289 https://mastoxiv.page/@arXiv_mathNA_bot/114539035462135281
- Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
https://arxiv.org/abs/2505.15215 https://mastoxiv.page/@arXiv_statML_bot/114550346291754635
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of ph...
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi, Alison L. Marsden
https://arxiv.org/abs/2506.11683 https://mastoxiv.page/@arXiv_statML_bot/114692410563481289
- Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Maheshwari, Tang, Ock, Kolluru, Farimani, Kitchin
https://arxiv.org/abs/2506.14850 https://mastoxiv.page/@arXiv_physicschemph_bot/114709402590755731
- Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
https://arxiv.org/abs/2506.18283 https://mastoxiv.page/@arXiv_statML_bot/114738165218533987
- ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
https://arxiv.org/abs/2506.20915 https://mastoxiv.page/@arXiv_csCR_bot/114754394485208892
- SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
Zhao, Huang, Xue, Kong, Liu, Tang, Beers, Ting, Luo
https://arxiv.org/abs/2507.01939 https://mastoxiv.page/@arXiv_astrophIM_bot/114788369702591337
- Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based I...
Ko Watanabe, Stanislav Frolov, Aya Hassan, David Dembinsky, Adriano Lucieri, Andreas Dengel
https://arxiv.org/abs/2507.17860 https://mastoxiv.page/@arXiv_csCV_bot/114912976717523345
- PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501 https://mastoxiv.page/@arXiv_csAI_bot/115032101532614110
- Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice
Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed
https://arxiv.org/abs/2508.20717 https://mastoxiv.page/@arXiv_csSD_bot/115111255835875066
- Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, et al.
https://arxiv.org/abs/2509.11512 https://mastoxiv.page/@arXiv_csDC_bot/115213444524490263
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
Ali Reza Ibrahimzada, Brandon Paulsen, Reyhaneh Jabbarvand, Joey Dodds, Daniel Kroening
https://arxiv.org/abs/2509.16187 https://mastoxiv.page/@arXiv_csSE_bot/115247172280557686
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation ...
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115286737423175311
- Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma
https://arxiv.org/abs/2509.21791 https://mastoxiv.page/@arXiv_csCL_bot/115287166674809413
- The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, S\'egol\`ene Martin, R\'emi Gribonval, Mathurin Massias
https://arxiv.org/abs/2510.24830 https://mastoxiv.page/@arXiv_csCV_bot/115462527449411627
- Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Han...
Giorgio Palma, Andrea Serani, Matteo Diez
https://arxiv.org/abs/2511.04461 https://mastoxiv.page/@arXiv_eessSY_bot/115507785247809767
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
toXiv_bot_toot

Australia just hit a major EV milestone: electric vehicles captured 8.3% of the car market in December 2025, a record high.
Battery EVs reached 9% market share for the year, with plug-in hybrids at 4.6%. Chinese brands like BYD and Zeekr are leading the charge, with even more models coming in 2026.
This shift could reshape Australia's entire automotive landscape.

2025 Ends with an Uptick in Australian Plugin Vehicle Sales - CleanTechnica
2025 ends with an uptick of EV sales into the Australian market, led by Chinese brands, and a review of EVs launched during year.

L’Union Européenne manque de courage pour innover son industrie automobile. La Chine n’attendra pas et en profitera.
Automobile: l’UE renonce au tout-électrique en 2035
L’Union européenne a renoncé mardi Š imposer aux constructeurs automobiles de passer au tout-électrique en 2035, une mesure environnementale phare, face Š la crise que traverse le secteur en Europe.
https://www.letemps.ch/monde/automobile-l-ue-renonce-au-tout-electrique-en-2035?shouldPreventPurchase=true&inAppView=3.19.0&didomiConfig.user.externalConsent.value={"purposes":{"consent":{"enabled":["abtesting-p6FL2BYL","audiencem-hJxaeGrR","cookies","create_ads_profile","create_content_profile","device_characteristics","geolocation_data","improve_products","market_research","measure_ad_performance","measure_content_performance","parselyne-Ya2EC4dG","select_basic_ads","select_personalized_ads","select_personalized_content","use_limited_data_to_select_content"],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"vendors":{"consent":{"enabled":["amazon","c:aws-cloudfront","c:bannerwise-EeHwBjLT","c:chartbeat","c:cloudflare","c:echobox-GqD96L9K","c:forecast-ZRJPAUT7","c:googleana-4TXnJigR","c:hotjar","c:jsdelivr-xdD77X6w","c:jw-player","c:kameleoon-experiment","c:kameleoon-personalization","c:keycdn","c:linkedin","c:magnitein-ePEpbCQ7","c:meta-43HDmFRa","c:microsoft","c:nonli-jnWf8kqD","c:pianohybr-R3VKC2r4","c:pinterest","c:spyri-XAtBPx3i","c:tapadinc-VDTUUcKw","c:tiktok-KZAUQLZ9","c:unrulygro-nKyLqdKi","c:youtube","facebook","google","twitter",1,10,1020,1021,1028,1046,1069,108,109,11,1126,12,120,1269,128,13,130,131,1310,132,136,137,138,142,143,1449,154,156,16,164,202,209,21,211,23,238,24,241,243,25,253,264,28,284,290,30,301,32,36,39,4,412,422,45,469,475,493,50,519,52,559,58,61,612,617,68,684,69,73,76,77,783,790,793,80,81,811,84,85,855,898,91,92,95,965,98],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"user_id":"36B03261-8BC8-4FB4-A026-0543FF748710","created":"2025-05-28T08:36:56.058Z","updated":"2025-11-08T21:05:15.048Z","source":{"type":"app","domain":"ch.letemps.LT"},"action":"webview"}

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
https://arxiv.org/abs/2506.10892 https://mastoxiv.page/@arXiv_csLG_bot/114675526577078472
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
https://arxiv.org/abs/2506.20494 https://mastoxiv.page/@arXiv_csLG_bot/114749113025183688
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
https://arxiv.org/abs/2506.20584 https://mastoxiv.page/@arXiv_csLG_bot/114749118923266356
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
https://arxiv.org/abs/2506.22376 https://mastoxiv.page/@arXiv_csLG_bot/114771735361664528
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
https://arxiv.org/abs/2507.18242 https://mastoxiv.page/@arXiv_csLG_bot/114913322736512937
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
https://arxiv.org/abs/2508.17702 https://mastoxiv.page/@arXiv_csLG_bot/115095014405732247
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
https://arxiv.org/abs/2508.19009 https://mastoxiv.page/@arXiv_csLG_bot/115100269482762688
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011 https://mastoxiv.page/@arXiv_csLG_bot/115100270137397046
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
https://arxiv.org/abs/2508.20705 https://mastoxiv.page/@arXiv_csLG_bot/115111565155687451
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
https://arxiv.org/abs/2509.25977 https://mastoxiv.page/@arXiv_csLG_bot/115298721327100391
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
https://arxiv.org/abs/2510.01384 https://mastoxiv.page/@arXiv_csLG_bot/115309690976554356
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
https://arxiv.org/abs/2510.09775 https://mastoxiv.page/@arXiv_csLG_bot/115372387779061015
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
https://arxiv.org/abs/2510.14386 https://mastoxiv.page/@arXiv_csLG_bot/115389079527543821
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
https://arxiv.org/abs/2510.16882 https://mastoxiv.page/@arXiv_csLG_bot/115412243355962887
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
https://arxiv.org/abs/2510.23117 https://mastoxiv.page/@arXiv_csLG_bot/115451891042176876
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
https://arxiv.org/abs/2510.23501 https://mastoxiv.page/@arXiv_csLG_bot/115451942159737549
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
https://arxiv.org/abs/2511.00040 https://mastoxiv.page/@arXiv_csLG_bot/115490555013124989
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
https://arxiv.org/abs/2511.04469 https://mastoxiv.page/@arXiv_csLG_bot/115507943827841017
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
https://arxiv.org/abs/2511.09902 https://mastoxiv.page/@arXiv_csLG_bot/115547587245365920
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
toXiv_bot_toot

«Nous sommes dans une période difficile sur le plan financier. Pour autant, il ne faut pas démanteler ce qui fonctionne. Dans la formation, la politique du «stop-and-go» est contre-productive. Couper dans notre capacité Š innover, Š nous trouver parmi les meilleurs du monde, serait un signal néfaste.» ~ Isabelle Chassot https://www.letemps.ch/suisse/le-conseil-des-etats-attenue-le-plan-budgetaire-de-karin-keller-sutter?shouldPreventPurchase=true&inAppView=3.19.0&didomiConfig.user.externalConsent.value={"purposes":{"consent":{"enabled":["abtesting-p6FL2BYL","audiencem-hJxaeGrR","cookies","create_ads_profile","create_content_profile","device_characteristics","geolocation_data","improve_products","market_research","measure_ad_performance","measure_content_performance","parselyne-Ya2EC4dG","select_basic_ads","select_personalized_ads","select_personalized_content","use_limited_data_to_select_content"],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"vendors":{"consent":{"enabled":["amazon","c:aws-cloudfront","c:bannerwise-EeHwBjLT","c:chartbeat","c:cloudflare","c:echobox-GqD96L9K","c:forecast-ZRJPAUT7","c:googleana-4TXnJigR","c:hotjar","c:jsdelivr-xdD77X6w","c:jw-player","c:kameleoon-experiment","c:kameleoon-personalization","c:keycdn","c:linkedin","c:magnitein-ePEpbCQ7","c:meta-43HDmFRa","c:microsoft","c:nonli-jnWf8kqD","c:pianohybr-R3VKC2r4","c:pinterest","c:spyri-XAtBPx3i","c:tapadinc-VDTUUcKw","c:tiktok-KZAUQLZ9","c:unrulygro-nKyLqdKi","c:youtube","facebook","google","twitter",1,10,1020,1021,1028,1046,1069,108,109,11,1126,12,120,1269,128,13,130,131,1310,132,136,137,138,142,143,1449,154,156,16,164,202,209,21,211,23,238,24,241,243,25,253,264,28,284,290,30,301,32,36,39,4,412,422,45,469,475,493,50,519,52,559,58,61,612,617,68,684,69,73,76,77,783,790,793,80,81,811,84,85,855,898,91,92,95,965,98],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"user_id":"36B03261-8BC8-4FB4-A026-0543FF748710","created":"2025-05-28T08:36:56.058Z","updated":"2025-11-08T21:05:15.048Z","source":{"type":"app","domain":"ch.letemps.LT"},"action":"webview"}

Oracle perd 20 milliards en attente d’éclatement de la bulle IA. Cela pourrait affaiblir l’OPA inamicale de son propriétaire sur Warner. https://www.letemps.ch/economie/oracle-oiseau-de-mauvais-augure-de-l-ia?shouldPreventPurchase=true&inAppView=3.19.0&didomiConfig.user.externalConsent.value={"purposes":{"consent":{"enabled":["abtesting-p6FL2BYL","audiencem-hJxaeGrR","cookies","create_ads_profile","create_content_profile","device_characteristics","geolocation_data","improve_products","market_research","measure_ad_performance","measure_content_performance","parselyne-Ya2EC4dG","select_basic_ads","select_personalized_ads","select_personalized_content","use_limited_data_to_select_content"],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"vendors":{"consent":{"enabled":["amazon","c:aws-cloudfront","c:bannerwise-EeHwBjLT","c:chartbeat","c:cloudflare","c:echobox-GqD96L9K","c:forecast-ZRJPAUT7","c:googleana-4TXnJigR","c:hotjar","c:jsdelivr-xdD77X6w","c:jw-player","c:kameleoon-experiment","c:kameleoon-personalization","c:keycdn","c:linkedin","c:magnitein-ePEpbCQ7","c:meta-43HDmFRa","c:microsoft","c:nonli-jnWf8kqD","c:pianohybr-R3VKC2r4","c:pinterest","c:spyri-XAtBPx3i","c:tapadinc-VDTUUcKw","c:tiktok-KZAUQLZ9","c:unrulygro-nKyLqdKi","c:youtube","facebook","google","twitter",1,10,1020,1021,1028,1046,1069,108,109,11,1126,12,120,1269,128,13,130,131,1310,132,136,137,138,142,143,1449,154,156,16,164,202,209,21,211,23,238,24,241,243,25,253,264,28,284,290,30,301,32,36,39,4,412,422,45,469,475,493,50,519,52,559,58,61,612,617,68,684,69,73,76,77,783,790,793,80,81,811,84,85,855,898,91,92,95,965,98],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"user_id":"36B03261-8BC8-4FB4-A026-0543FF748710","created":"2025-05-28T08:36:56.058Z","updated":"2025-11-08T21:05:15.048Z","source":{"type":"app","domain":"ch.letemps.LT"},"action":"webview"}