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In this picture, the learner's","authors":["Castiglioni, Matteo","Nuara, Alessandro","Romano, Giulia","Spadaro, Giorgio","Trovò, Francesco","Gatti, Nicola"],"url":"https://arxiv.org/abs/2201.07139","arxiv_id":"2201.07139","published":"2025-12-05"},{"title":"Efficient stereo matching on embedded GPUs with zero-means cross correlation","abstract":"Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile plat","authors":["Chang, Qiong","Zha, Aolong","Wang, Weimin","Liu, Xin","Onishi, Masaki","Lei, Lei"],"url":"https://arxiv.org/abs/2212.00476","arxiv_id":"2212.00476","published":"2025-12-05"},{"title":"Learning from Stochastic Labels","abstract":"Annotating multi-class instances is a crucial task in the field of machine learning. Unfortunately, identifying the correct class label from a long sequence of candidate labels is time-consuming and laborious. To alleviate this problem, we design a novel labeling mechanism called stochastic label. 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In this paper, I will discuss some analogies of the operation of MemComputing machines-in genera","authors":["Di Ventra, Massimiliano"],"url":"https://arxiv.org/abs/2302.11403","arxiv_id":"2302.11403","published":"2025-12-05"},{"title":"Polygon Intersection-over-Union Loss for Viewpoint-Agnostic Monocular 3D Vehicle Detection","abstract":"Monocular 3D object detection is a challenging task because depth information is difficult to obtain from 2D images. A subset of viewpoint-agnostic monocular 3D detection methods also do not explicitly leverage scene homography or geometry during training, meaning that a model trained thusly can detect objects in images from arbitrary viewpoints. Such works predict the projections of the 3D bounding boxes on the imag","authors":["Lu, Xinxuan","Gloudemans, Derek","Xia, Shepard","Work, Daniel B."],"url":"https://arxiv.org/abs/2309.07104","arxiv_id":"2309.07104","published":"2025-12-05"},{"title":"Towards an end-to-end artificial intelligence driven global weather forecasting system","abstract":"The weather forecasting system is important for science and society, and significant achievements have been made in applying artificial intelligence (AI) to medium-range weather forecasting. However, existing AI-based weather forecasting models rely on analysis or reanalysis products from traditional numerical weather prediction (NWP) systems as initial conditions for making predictions. The initial states are typica","authors":["Chen, Kun","Bai, Lei","Ling, Fenghua","Ye, Peng","Chen, Tao","Fan, Hang"],"url":"https://arxiv.org/abs/2312.12462","arxiv_id":"2312.12462","published":"2025-12-05"},{"title":"The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions","abstract":"Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learn","authors":["Chuang, Yun-Shiuan","Zhu, Jerry","Rogers, Timothy T."],"url":"https://arxiv.org/abs/2402.13927","arxiv_id":"2402.13927","published":"2025-12-05"},{"title":"Boosting Skull-Stripping Performance for Pediatric Brain Images","abstract":"Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the b","authors":["Kelley, William","Ngo, Nathan","Dalca, Adrian V.","Fischl, Bruce","Zöllei, Lilla","Hoffmann, Malte"],"url":"https://arxiv.org/abs/2402.16634","arxiv_id":"2402.16634","published":"2025-12-05"},{"title":"The Autonomy-Alignment Problem in Open-Ended Learning Robots: Formalising the Purpose Framework","abstract":"The rapid advancement of artificial intelligence is enabling the development of increasingly autonomous robots capable of operating beyond engineered factory settings and into the unstructured environments of human life. This shift raises a critical autonomy-alignment problem: how to ensure that a robot's autonomous learning focuses on acquiring knowledge and behaviours that serve human practical objectives while","authors":["Baldassarre, Gianluca","Duro, Richard J.","Cartoni, Emilio","Khamassi, Mehdi","Romero, Alejandro","Santucci, Vieri Giuliano"],"url":"https://arxiv.org/abs/2403.02514","arxiv_id":"2403.02514","published":"2025-12-05"},{"title":"ImageNot: A contrast with ImageNet preserves model rankings","abstract":"We introduce ImageNot, a dataset constructed explicitly to be drastically different than ImageNet while matching its scale. ImageNot is designed to test the external validity of deep learning progress on ImageNet. We show that key model architectures developed for ImageNet over the years rank identically to how they rank on ImageNet when trained from scratch and evaluated on ImageNot. Moreover, the relative improveme","authors":["Salaudeen, Olawale","Hardt, Moritz"],"url":"https://arxiv.org/abs/2404.02112","arxiv_id":"2404.02112","published":"2025-12-05"},{"title":"Surface-Based Visibility-Guided Uncertainty for Continuous Active 3D Neural Reconstruction","abstract":"View selection is critical in active 3D neural reconstruction as it impacts the contents of training set and resulting final output quality. Recent view selection strategies emphasize the visibility when evaluating model uncertainty in active 3D reconstruction. However, existing approaches estimate visibility only after the model fully converges, which has confined their application primarily to non-continuous active","authors":["Kim, Hyunseo","Yang, Hyeonseo","Kim, Taekyung","Kim, YoonSung","Lee, Minsu","Kim, Jin-Hwa"],"url":"https://arxiv.org/abs/2405.02568","arxiv_id":"2405.02568","published":"2025-12-05"},{"title":"A re-calibration method for object detection with multi-modal alignment bias in autonomous driving","abstract":"Multi-modal object detection in autonomous driving has achieved great breakthroughs due to the usage of fusing complementary information from different sensors. The calibration in fusion between sensors such as LiDAR and camera was always supposed to be precise in previous work. However, in reality, calibration matrices are fixed when the vehicles leave the factory, but mechanical vibration, road bumps, and data lags","authors":["Song, Zhihang","Yao, Dingyi","Ming, Ruibo","Peng, Lihui","Yao, Danya","Zhang, Yi"],"url":"https://arxiv.org/abs/2405.16848","arxiv_id":"2405.16848","published":"2025-12-05"},{"title":"FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion","abstract":"Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and data-efficient manner. Although a variety of deep model fusion techniques have been introduced, their evaluations tend to be inconsistent and often inadequate to validate their effectiveness and robustness. We present FusionBench, the first","authors":["Tang, Anke","Shen, Li","Luo, Yong","Yang, Enneng","Hu, Han","Zhang, Lefei"],"url":"https://arxiv.org/abs/2406.03280","arxiv_id":"2406.03280","published":"2025-12-05"},{"title":"Generalizability of experimental studies","abstract":"Experimental studies are a cornerstone of Machine Learning (ML) research. A common and often implicit assumption is that the study's results will generalize beyond the study itself, e.g., to new data. That is, repeating the same study under different conditions will likely yield similar results. Existing frameworks to measure generalizability, borrowed from the casual inference literature, cannot capture the comp","authors":["Matteucci, Federico","Arzamasov, Vadim","Cribeiro-Ramallo, Jose","Heyden, Marco","Ntounas, Konstantin","Böhm, Klemens"],"url":"https://arxiv.org/abs/2406.17374","arxiv_id":"2406.17374","published":"2025-12-05"},{"title":"NITRO-D: Native Integer-only Training of Deep Convolutional Neural Networks","abstract":"Quantization is a pivotal technique for managing the growing computational and memory demands of Deep Neural Networks (DNNs). By reducing the number of bits used to represent weights and activations (typically from 32-bit Floating-Point (FP) to 16-bit or 8-bit integers), quantization reduces memory footprint, energy consumption, and execution time of DNNs. However, most existing methods typically target DNN inference","authors":["Pirillo, Alberto","Colombo, Luca","Roveri, Manuel"],"url":"https://arxiv.org/abs/2407.11698","arxiv_id":"2407.11698","published":"2025-12-05"},{"title":"Visuospatial navigation from the bottom-up: without vestibular integration, distance prediction, or maps","abstract":"Navigation is believed to be controlled by at least two partially dissociable systems in the brain. The cognitive map informs an organism of its location and bearing, updated by integrating vestibular self-motion or predicting distances to landmarks. Route-based navigation, on the other hand, directly evaluate sequential movement decisions from immediate percepts. Here we demonstrate the sufficiency of visual route-b","authors":["Govoni, Patrick","Romanczuk, Pawel"],"url":"https://arxiv.org/abs/2407.13535","arxiv_id":"2407.13535","published":"2025-12-05"},{"title":"Sustainable broadcasting in Blockchain Networks with Reinforcement Learning","abstract":"Recent estimates put the carbon footprint of Bitcoin and Ethereum at an average of 64 and 26 million tonnes of CO2 per year, respectively. To address this growing problem, several possible approaches have been proposed in the literature: creating alternative blockchain consensus mechanisms, applying redundancy reduction techniques, utilizing renewable energy sources, and employing energy-efficient devices, etc. In th","authors":["Valko, Danila","Kudenko, Daniel"],"url":"https://arxiv.org/abs/2407.15616","arxiv_id":"2407.15616","published":"2025-12-05"},{"title":"KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models","abstract":"This paper investigates visual analogical reasoning in large multimodal models (LMMs) compared to human adults and children. A "visual analogy" is an abstract rule inferred from one image and applied to another. While benchmarks exist for testing visual reasoning in LMMs, they require advanced skills and omit basic visual analogies that even young children can make. Inspired by developmental psychology, we pr","authors":["Yiu, Eunice","Qraitem, Maan","Majhi, Anisa Noor","Wong, Charlie","Bai, Yutong","Ginosar, Shiry"],"url":"https://arxiv.org/abs/2407.17773","arxiv_id":"2407.17773","published":"2025-12-05"},{"title":"Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks","abstract":"In this work, we explore the intersection of sparse coding theory and deep learning to enhance our understanding of feature extraction capabilities in advanced neural network architectures. We begin by introducing a novel class of Deep Sparse Coding (DSC) models and establish a thorough theoretical analysis of their uniqueness and stability properties. By applying iterative algorithms to these DSC models, we derive c","authors":["Li, Jianfei","Feng, Han","Zhou, Ding-Xuan"],"url":"https://arxiv.org/abs/2408.05540","arxiv_id":"2408.05540","published":"2025-12-05"},{"title":"Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction","abstract":"Modeling high-order feature interactions is crucial for click-through rate (CTR) prediction, yet traditional approaches typically predefine a maximum interaction order and exhaustively enumerate feature combinations up to that order. This paradigm depends heavily on prior domain knowledge to delimit the interaction space and incurs substantial computational overhead. As a result, conventional CTR models face a persis","authors":["Shi, Yunxiao","Xu, Wujiang","Zhang, Haimin","Wu, Qiang","Xu, Min"],"url":"https://arxiv.org/abs/2408.08713","arxiv_id":"2408.08713","published":"2025-12-05"},{"title":"A Gray-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse","abstract":"Recent advancements in Latent Diffusion Models (LDMs) have revolutionized image synthesis and manipulation, raising significant concerns about data misappropriation and intellectual property infringement. While adversarial attacks have been extensively explored as a protective measure against such misuse of generative AI, current approaches are severely limited by their heavy reliance on model-specific knowledge and ","authors":["Guo, Zhongliang","Lei, Chun Tong","Fang, Lei","Zhao, Shuai","Qian, Yifei","Lin, Jingyu"],"url":"https://arxiv.org/abs/2408.10901","arxiv_id":"2408.10901","published":"2025-12-05"},{"title":"Addressing common misinterpretations of KART and UAT in neural network literature","abstract":"This note addresses the Kolmogorov-Arnold Representation Theorem (KART) and the Universal Approximation Theorem (UAT), focusing on their frequent misinterpretations found in the neural network literature. Our remarks aim to support a more accurate understanding of KART and UAT among neural network specialists. In addition, we explore the minimal number of neurons required for universal approximation, showing that the","authors":["Ismailov, Vugar"],"url":"https://arxiv.org/abs/2408.16389","arxiv_id":"2408.16389","published":"2025-12-05"},{"title":"OP-Align: Object-level and Part-level Alignment for Self-supervised Category-level Articulated Object Pose Estimation","abstract":"Category-level articulated object pose estimation focuses on the pose estimation of unknown articulated objects within known categories. Despite its significance, this task remains challenging due to the varying shapes and poses of objects, expensive dataset annotation costs, and complex real-world environments. In this paper, we propose a novel self-supervised approach that leverages a single-frame point cloud to so","authors":["Che, Yuchen","Furukawa, Ryo","Kanezaki, Asako"],"url":"https://arxiv.org/abs/2408.16547","arxiv_id":"2408.16547","published":"2025-12-05"},{"title":"EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models","abstract":"In this work, we introduce EMMA-500, a large-scale multilingual language model continue-trained on texts across 546 languages designed for enhanced multilingual performance, focusing on improving language coverage for low-resource languages. To facilitate continual pre-training, we compile the MaLA corpus, a comprehensive multilingual dataset enriched with curated datasets across diverse domains. Leveraging this corp","authors":["Ji, Shaoxiong","Li, Zihao","Paavola, Jaakko","Lin, Peiqin","Chen, Pinzhen","O'Brien, Dayyán"],"url":"https://arxiv.org/abs/2409.17892","arxiv_id":"2409.17892","published":"2025-12-05"},{"title":"Align$^2$LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation","abstract":"Recent advances in Multi-modal Large Language Models (MLLMs), such as LLaVA-series models, are driven by massive machine-generated instruction-following data tuning. Such automatic instruction collection pipelines, however, inadvertently introduce significant variability in data quality. This paper introduces a novel instruction curation algorithm, derived from two unique perspectives, human and LLM preference alignm","authors":["Huang, Hongzhe","Liu, Jiang","Yu, Zhewen","Cai, Li","Jiao, Dian","Zhang, Wenqiao"],"url":"https://arxiv.org/abs/2409.18541","arxiv_id":"2409.18541","published":"2025-12-05"}],"history":{"events":[{"year":2007,"text":"A gunman killed nine people at a shopping mall in Omaha, Nebraska.","url":"https://en.wikipedia.org/wiki/Westroads_Mall_shooting"},{"year":1972,"text":"Gough Whitlam took office as the 21st Prime Minister of Australia and formed a duumvirate with his deputy Lance Barnard, ending 23 years of Liberal–Country Party government.","url":"https://en.wikipedia.org/wiki/Gough_Whitlam"},{"year":1965,"text":"The \"glasnost meeting\" took place in Moscow, becoming the first demonstration in the Soviet Union after World War II and marking the beginning of the civil rights movement in the country.","url":"https://en.wikipedia.org/wiki/Glasnost_meeting"},{"year":1958,"text":"Britain's first motorway, the Preston Bypass, opened to the public.","url":"https://en.wikipedia.org/wiki/Controlled-access_highway"},{"year":1952,"text":"The \"Great Smog of London\" (pictured) began and lasted for five days, causing 12,000 deaths and leading to the Clean Air Act 1956.","url":"https://en.wikipedia.org/wiki/Great_Smog_of_London"},{"year":1936,"text":"The 1936 Soviet constitution, also known as the \"Stalin constitution\", was adopted.","url":"https://en.wikipedia.org/wiki/1936_Constitution_of_the_Soviet_Union"},{"year":1918,"text":"National Guards and Sokol volunteers protested in Zagreb, leading to an armed clash with regiments of the Home Guard and former Common Army.","url":"https://en.wikipedia.org/wiki/Sokol_movement"},{"year":1914,"text":"The Imperial Trans-Antarctic Expedition began in an attempt to make the first land crossing of Antarctica.","url":"https://en.wikipedia.org/wiki/Imperial_Trans-Antarctic_Expedition"},{"year":1807,"text":"Napoleonic Wars: British ships began a raid on Griessie after the Dutch captain refused a British demand for surrender.","url":"https://en.wikipedia.org/wiki/Napoleonic_Wars"},{"year":1775,"text":"American Revolutionary War: Continental Army colonel Henry Knox arrived at Fort Ticonderoga in New York to arrange the transport of 60 tons of artillery (depicted) to support the siege of Boston.","url":"https://en.wikipedia.org/wiki/American_Revolutionary_War"},{"year":1757,"text":"Seven Years' War: Prussian troops under Frederick the Great defeated Austrian forces at the Battle of Leuthen (pictured).","url":"https://en.wikipedia.org/wiki/Seven_Years'_War"},{"year":1484,"text":"Pope Innocent VIII issued the papal bull Summis desiderantes affectibus, which gave the Dominican inquisitor Heinrich Kramer the explicit authority to prosecute witchcraft in Germany.","url":"https://en.wikipedia.org/wiki/Pope_Innocent_VIII"},{"year":1456,"text":"The first of two major earthquakes struck the Kingdom of Naples, killing up to 70,000 people.","url":"https://en.wikipedia.org/wiki/1456_Central_Italy_earthquakes"}],"births":[{"year":2009,"text":"Owen Cooper, English actor","url":"https://en.wikipedia.org/wiki/Owen_Cooper"},{"year":2008,"text":"Ediz Gürel, Turkish chess grandmaster","url":"https://en.wikipedia.org/wiki/Ediz_G%C3%BCrel"},{"year":2000,"text":"Soobin, South Korean singer-songwriter","url":"https://en.wikipedia.org/wiki/Soobin_(singer)"},{"year":1998,"text":"Conan Gray, American singer-songwriter","url":"https://en.wikipedia.org/wiki/Conan_Gray"},{"year":1998,"text":"Randal Kolo Muani, French footballer","url":"https://en.wikipedia.org/wiki/Randal_Kolo_Muani"},{"year":1997,"text":"Maddie Poppe, American singer-songwriter and musician","url":"https://en.wikipedia.org/wiki/Maddie_Poppe"},{"year":1997,"text":"Quinnen Williams, American football player","url":"https://en.wikipedia.org/wiki/Quinnen_Williams"},{"year":1995,"text":"Danny Levi, New Zealand rugby league player","url":"https://en.wikipedia.org/wiki/Danny_Levi"}],"deaths":[{"year":2025,"text":"Frank Gehry, Canadian-American architect and designer (b. 1929)","url":"https://en.wikipedia.org/wiki/Frank_Gehry"},{"year":2025,"text":"Michael Annett, American Former NASCAR Driver (b. 1986)","url":"https://en.wikipedia.org/wiki/Michael_Annett"},{"year":2024,"text":"Jacques Roubaud, French poet, writer, and mathematician (born 1932)","url":"https://en.wikipedia.org/wiki/Jacques_Roubaud"},{"year":2023,"text":"Norman Lear, American screenwriter and producer (born 1922)","url":"https://en.wikipedia.org/wiki/Norman_Lear"},{"year":2022,"text":"Kirstie Alley, American actress and producer (born 1951)","url":"https://en.wikipedia.org/wiki/Kirstie_Alley"},{"year":2021,"text":"Bob Dole, American politician (born 1923)","url":"https://en.wikipedia.org/wiki/Bob_Dole"},{"year":2020,"text":"Peter Alliss, English professional golfer (born 1931)","url":"https://en.wikipedia.org/wiki/Peter_Alliss"},{"year":2019,"text":"Robert Walker, American actor (born 1940)","url":"https://en.wikipedia.org/wiki/Robert_Walker_(actor%2C_born_1940)"}]},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}