@netzschleuder@social.skewed.delivemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…
@netzschleuder@social.skewed.delivemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…
@adulau@infosec.exchangeWe’ve published new research from the EU co-funded project NGSOTI: “Learning from large-scale IPv4 blackhole: Behavioral analysis of SNMP traffic”.
Over a 12-month period (Nov 2024–Oct 2025), our network telescope captured ~634 million unsolicited SNMP queries from more than 153,000 unique IPv4 sources scanning an unused /18 block.
The origins of the traffic are globally distributed, with notable concentrations from Indonesia, China, the United States, Germany, Chile and others, …
@pbloem@sigmoid.socialI need to read it properly, but this looks 🔥 https://arxiv.org/abs/2511.16652
@mariyadelano@hachyderm.ioThank you all for 3,000 followers on here! Here’s a photo of Danny to celebrate the occasion ☺️
3 years into being part of Mastodon, I continue to be impressed with how wonderful the people here are and how much this social network actually FEELS social. People replying to one another, having conversations, learning things, sharing moments of joy, making friends.
Mastodon brought my business clients, helped me gain confidence in my own voice, freed me from dependence on big tech and algorithms, rekindled my interests, introduced me to incredible people and projects, and served as a source of hope in humanity in the times when cynicism and nihilism felt all but inevitable.
I love our little corner of the internet, and am so glad that it’s still here despite everyone who professed it was doomed to fade into irrelevance.
Thank you to everyone reading these words for being here on the Fedi. The world is a little better thanks to your choice to support an independent web.
@arXiv_csLG_bot@mastoxiv.pageReplaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot
@mszll@datasci.socialMind the gap: Revealing sidewalk networks at scale
pdf: https://iopscience.iop.org/article/10.1088/2632-072X/ae35b9/pdf
Nice to see important research on the both most important and neglected mode of transport 🚶
@brichapman@mastodon.socialOver 1,300 people from 130 countries just spent a month mastering climate action together.
The En-ROADS Climate Ambassador Camp brought participants through 13,800 hours of training on the Climate Solutions Simulator—diving into climate dynamics, testing solutions, and roleplaying real-world scenarios.
Now they're leading climate events in their communities and working toward certification.
@arXiv_physicsoptics_bot@mastoxiv.pageReplaced article(s) found for physics.optics. https://arxiv.org/list/physics.optics/new
[1/1]:
- LLM4Laser: Large Language Models Automate the Design of Lasers
Renjie Li, Ceyao Zhang, Sixuan Mao, Xiyuan Zhou, Feng Yin, Sergios Theodoridis, Zhaoyu Zhang
https://arxiv.org/abs/2104.12145
- Room-temperature valley-selective emission in Si-MoSe2 heterostructures enabled by high-quality-f...
Feng Pan, et al.
https://arxiv.org/abs/2409.09806 https://mastoxiv.page/@arXiv_physicsoptics_bot/113152185040115763
- 1T'-MoTe$_2$ as an integrated saturable absorber for photonic machine learning
Maria Carolina Volpato, Henrique G. Rosa, Tom Reep, Pierre-Louis de Assis, Newton Cesario Frateschi
https://arxiv.org/abs/2507.16140 https://mastoxiv.page/@arXiv_physicsoptics_bot/114901571498004090
- NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering
Yunong Sun, Fei Xia
https://arxiv.org/abs/2507.22328 https://mastoxiv.page/@arXiv_physicsoptics_bot/114947052118796753
- Structured Random Models for Phase Retrieval with Optical Diffusers
Zhiyuan Hu, Fakhriyya Mammadova, Juli\'an Tachella, Michael Unser, Jonathan Dong
https://arxiv.org/abs/2510.14490 https://mastoxiv.page/@arXiv_physicsoptics_bot/115388901264416806
- Memory Effects in Time-Modulated Radiative Heat Transfer
Riccardo Messina, Philippe Ben-Abdallah
https://arxiv.org/abs/2510.19378 https://mastoxiv.page/@arXiv_physicsoptics_bot/115422659227231796
- Mie-tronics supermodes and symmetry breaking in nonlocal metasurfaces
Thanh Xuan Hoang, Ayan Nussupbekov, Jie Ji, Daniel Leykam, Jaime Gomez Rivas, Yuri Kivshar
https://arxiv.org/abs/2511.03560 https://mastoxiv.page/@arXiv_physicsoptics_bot/115502066008543828
- Integrated soliton microcombs beyond the turnkey limit
Wang, Xu, Wang, Zhu, Luo, Luo, Wang, Ni, Yang, Gong, Xiao, Li, Yang
https://arxiv.org/abs/2511.06909 https://mastoxiv.page/@arXiv_physicsoptics_bot/115530791701071777
- Ising accelerator with a reconfigurable interferometric photonic processor
Rausell-Campo, Al Kayed, P\'erez-L\'ppez, Aadhi, Shastri, Francoy
https://arxiv.org/abs/2511.13284 https://mastoxiv.page/@arXiv_physicsoptics_bot/115570439939074488
- Superradiance in dense atomic samples
I. M. de Ara\'ujo, H. Sanchez, L. F. Alves da Silva, M. H. Y. Moussa
https://arxiv.org/abs/2504.20242 https://mastoxiv.page/@arXiv_quantph_bot/114425762810828336
- Fluctuation-induced Hall-like lateral forces in a chiral-gain environment
Daigo Oue, M\'ario G. Silveirinha
https://arxiv.org/abs/2507.14754 https://mastoxiv.page/@arXiv_condmatmeshall_bot/114896308178114535
- Tensor-network approach to quantum optical state evolution beyond the Fock basis
Nikolay Kapridov, Egor Tiunov, Dmitry Chermoshentsev
https://arxiv.org/abs/2511.15295 https://mastoxiv.page/@arXiv_quantph_bot/115581390666689204
- OmniLens : Blind Lens Aberration Correction via Large LensLib Pre-Training and Latent PSF Repres...
Jiang, Qian, Gao, Sun, Yang, Yi, Li, Yang, Van Gool, Wang
https://arxiv.org/abs/2511.17126 https://mastoxiv.page/@arXiv_eessIV_bot/115603729319581340
toXiv_bot_toot
@arXiv_csLG_bot@mastoxiv.pageSpatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
toXiv_bot_toot
@netzschleuder@social.skewed.delivemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…
@netzschleuder@social.skewed.delivemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…
@arXiv_csLG_bot@mastoxiv.pageReplaced 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
@arXiv_csGT_bot@mastoxiv.pageReplaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- Cumulative Games: Who is the current player?
Urban Larsson, Reshef Meir, Yair Zick
https://arxiv.org/abs/2005.06326
- Contest Design with Threshold Objectives
Edith Elkind, Abheek Ghosh, Paul W. Goldberg
https://arxiv.org/abs/2109.03179
- Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications
V. Udaya Sankar, Vishisht Srihari Rao, Y. Narahari
https://arxiv.org/abs/2401.05683 https://mastoxiv.page/@arXiv_csGT_bot/111741115483021453
- Charting the Shapes of Stories with Game Theory
Daskalakis, Gemp, Jiang, Leme, Papadimitriou, Piliouras
https://arxiv.org/abs/2412.05747 https://mastoxiv.page/@arXiv_csGT_bot/113627246220336424
- Computing Evolutionarily Stable Strategies in Multiplayer Games
Sam Ganzfried
https://arxiv.org/abs/2511.20859 https://mastoxiv.page/@arXiv_csGT_bot/115620508246637361
- Autodeleveraging: Impossibilities and Optimization
Tarun Chitra
https://arxiv.org/abs/2512.01112 https://mastoxiv.page/@arXiv_csGT_bot/115649040881525135
- Static Pricing Guarantees for Queueing Systems
Jacob Bergquist, Adam N. Elmachtoub
https://arxiv.org/abs/2305.09168 https://mastoxiv.page/@arXiv_csDS_bot/110382625621173269
- Game of arrivals at a two queue network with heterogeneous customer routes
Agniv Bandyopadhyay, Sandeep Juneja
https://arxiv.org/abs/2310.18149 https://mastoxiv.page/@arXiv_csPF_bot/111322112226936579
- Characterization of Priority-Neutral Matching Lattices
Clayton Thomas
https://arxiv.org/abs/2404.02142 https://mastoxiv.page/@arXiv_econTH_bot/112205968984928881
- Seven kinds of equivalent models for generalized coalition logics
Zixuan Chen, Fengkui Ju
https://arxiv.org/abs/2501.05466 https://mastoxiv.page/@arXiv_csLO_bot/113819715349259373
- Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences
Hadi Hosseini, Samarth Khanna, Ronak Singh
https://arxiv.org/abs/2506.04478 https://mastoxiv.page/@arXiv_csAI_bot/114635186215388479
toXiv_bot_toot
@netzschleuder@social.skewed.delivemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…
@netzschleuder@social.skewed.delivemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…
@arXiv_csLG_bot@mastoxiv.pageCrosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/3]:
- Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
Jo\~ao Marcos Cavalcanti de Albuquerque Neto, Gustavo Castro do Amaral, Guilherme Penello Tempor\~ao
https://arxiv.org/abs/2512.17660 https://mastoxiv.page/@arXiv_quantph_bot/115762703945731580
- Vidarc: Embodied Video Diffusion Model for Closed-loop Control
Feng, Xiang, Mao, Tan, Zhang, Huang, Zheng, Liu, Su, Zhu
https://arxiv.org/abs/2512.17661 https://mastoxiv.page/@arXiv_csRO_bot/115762650859932523
- Imputation Uncertainty in Interpretable Machine Learning Methods
Pegah Golchian, Marvin N. Wright
https://arxiv.org/abs/2512.17689 https://mastoxiv.page/@arXiv_statML_bot/115762577479255577
- Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo ...
Shengdu Chai, Chen Lin, Xinyang Dong, Yuqiang Li, Wanli Ouyang, Lei Wang, X. C. Xie
https://arxiv.org/abs/2512.17703 https://mastoxiv.page/@arXiv_condmatstrel_bot/115762481116668454
- Breast Cancer Neoadjuvant Chemotherapy Treatment Response Prediction Using Aligned Longitudinal M...
Rahul Ravi, Ruizhe Li, Tarek Abdelfatah, Stephen Chan, Xin Chen
https://arxiv.org/abs/2512.17759 https://mastoxiv.page/@arXiv_eessIV_bot/115762481771898369
- MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Im...
Roy, Kirchhoff, Ulrich, Rokuss, Wald, Isensee, Maier-Hein
https://arxiv.org/abs/2512.17774 https://mastoxiv.page/@arXiv_eessIV_bot/115762492258209812
- Domain-Aware Quantum Circuit for QML
Gurinder Singh, Thaddeus Pellegrini, Kenneth M. Merz, Jr
https://arxiv.org/abs/2512.17800 https://mastoxiv.page/@arXiv_quantph_bot/115762723607200478
- Visually Prompted Benchmarks Are Surprisingly Fragile
Feng, Lian, Dunlap, Shu, Wang, Wang, Darrell, Suhr, Kanazawa
https://arxiv.org/abs/2512.17875 https://mastoxiv.page/@arXiv_csCV_bot/115762781936221554
- Learning vertical coordinates via automatic differentiation of a dynamical core
Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo, Alejandro Di Luca, Alex Bihlo
https://arxiv.org/abs/2512.17877 https://mastoxiv.page/@arXiv_physicsaoph_bot/115762405092703069
- RadarGen: Automotive Radar Point Cloud Generation from Cameras
Tomer Borreda, Fangqiang Ding, Sanja Fidler, Shengyu Huang, Or Litany
https://arxiv.org/abs/2512.17897 https://mastoxiv.page/@arXiv_csCV_bot/115762783246540528
- Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Gahlawat, Aboudonia, Banik, Hovakimyan, Matni, Ames, Zardini, Speranzon
https://arxiv.org/abs/2512.17899 https://mastoxiv.page/@arXiv_eessSY_bot/115762532257741954
- Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting
Ananta R. Bhattarai, Helge Rhodin
https://arxiv.org/abs/2512.17908 https://mastoxiv.page/@arXiv_csCV_bot/115762785868778349
toXiv_bot_toot
@netzschleuder@social.skewed.delivemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…
@netzschleuder@social.skewed.delivemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://ne…
