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This paper revisits the fundamental assumptions underlying image classification and suggests that representing data as one-hot labels is a key factor that leads to vulnerabilities. However, in real-world datasets, data ambiguity often arises, with samples exhibiting characteristics of mult","authors":["Chen, Erh-Chung","Lee, Che-Rung"],"url":"https://arxiv.org/abs/2111.02331","arxiv_id":"2111.02331","published":"2025-11-27"},{"title":"ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition","abstract":"Prototypical part network (ProtoPNet) has drawn wide attention and boosted many follow-up studies due to its self-explanatory property for explainable artificial intelligence (XAI). However, when directly applying ProtoPNet on vision transformer (ViT) backbones, learned prototypes have a "distraction" problem: they have a relatively high probability of being activated by the background and pay less attention ","authors":["Xue, Mengqi","Huang, Qihan","Zhang, Haofei","Hu, Jingwen","Song, Jie","Song, Mingli"],"url":"https://arxiv.org/abs/2208.10431","arxiv_id":"2208.10431","published":"2025-11-27"},{"title":"Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation","abstract":"In recent years, deep neural networks have defined the state-of-the-art in semantic segmentation where their predictions are constrained to a predefined set of semantic classes. They are to be deployed in applications such as automated driving, although their categorically confined expressive power runs contrary to such open world scenarios. Thus, the detection and segmentation of objects from outside their predefine","authors":["Maag, Kira","Riedlinger, Tobias"],"url":"https://arxiv.org/abs/2303.06920","arxiv_id":"2303.06920","published":"2025-11-27"},{"title":"Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation","abstract":"State-of-the-art deep neural networks have proven to be highly powerful in a broad range of tasks, including semantic image segmentation. However, these networks are vulnerable against adversarial attacks, i.e., non-perceptible perturbations added to the input image causing incorrect predictions, which is hazardous in safety-critical applications like automated driving. Adversarial examples and defense strategies are","authors":["Maag, Kira","Fischer, Asja"],"url":"https://arxiv.org/abs/2305.12825","arxiv_id":"2305.12825","published":"2025-11-27"},{"title":"Machine Reading Comprehension using Case-based Reasoning","abstract":"We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our method (CBR-MRC) builds upon the hypothesis that contextualized answers to similar questions share semantic similarities with each other. Given a test question, CBR-MRC first retrieves a set of similar cases from a nonparametric memory and then","authors":["Thai, Dung","Agarwal, Dhruv","Chaudhary, Mudit","Zhao, Wenlong","Das, Rajarshi","Zaheer, Manzil"],"url":"https://arxiv.org/abs/2305.14815","arxiv_id":"2305.14815","published":"2025-11-27"},{"title":"Semi-supervised Multimodal Representation Learning through a Global Workspace","abstract":"Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations, or to translate signals from one domain to another (as in image captioning, or text-to-image generation). However, current approaches mainly rely on brute-force supervised training over large multimodal datasets. In contrast, humans (and other animals) can learn","authors":["Devillers, Benjamin","Maytié, Léopold","VanRullen, Rufin"],"url":"https://arxiv.org/abs/2306.15711","arxiv_id":"2306.15711","published":"2025-11-27"},{"title":"Evaluating Large Language Models for Radiology Natural Language Processing","abstract":"The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proficient in both English and Chinese. However, a comprehensive evaluation of","authors":["Liu, Zhengliang","Zhong, Tianyang","Li, Yiwei","Zhang, Yutong","Pan, Yi","Zhao, Zihao"],"url":"https://arxiv.org/abs/2307.13693","arxiv_id":"2307.13693","published":"2025-11-27"},{"title":"Dual-Balancing for Multi-Task Learning","abstract":"Multi-task learning aims to learn multiple related tasks simultaneously and has achieved great success in various fields. However, the disparity in loss and gradient scales among tasks often leads to performance compromises, and the balancing of tasks remains a significant challenge. In this paper, we propose Dual-Balancing Multi-Task Learning (DB-MTL) to achieve task balancing from both the loss and gradient perspec","authors":["Lin, Baijiong","Jiang, Weisen","Ye, Feiyang","Zhang, Yu","Chen, Pengguang","Chen, Ying-Cong"],"url":"https://arxiv.org/abs/2308.12029","arxiv_id":"2308.12029","published":"2025-11-27"},{"title":"AMLP: Adjustable Masking Lesion Patches for Self-Supervised Medical Image Segmentation","abstract":"Self-supervised masked image modeling (MIM) methods have shown promising performances on analyzing natural images. However, directly applying such methods to medical image segmentation tasks still cannot achieve satisfactory results. The challenges arise from the facts that (i) medical images are inherently more complex compared to natural images, and the subjects in medical images often exhibit more distinct contour","authors":["Wang, Xiangtao","Wang, Ruizhi","Lukasiewicz, Thomas","Xu, Zhenghua"],"url":"https://arxiv.org/abs/2309.04312","arxiv_id":"2309.04312","published":"2025-11-27"},{"title":"Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping","abstract":"While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to the challenges in training large transformer models. Specifically, large models further exacerbate issues in FL as they are particularly susceptible to gradient heterogeneity across layers, unlike the relatively uniform gradient behavior o","authors":["Pelikan, Martin","Azam, Sheikh Shams","Feldman, Vitaly","Silovsky, Jan "Honza"","Talwar, Kunal","Brinton, Christopher G."],"url":"https://arxiv.org/abs/2310.00098","arxiv_id":"2310.00098","published":"2025-11-27"},{"title":"TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices","abstract":"Developing deep learning models on tiny devices (e.g. Microcontroller units, MCUs) has attracted much attention in various embedded IoT applications. However, it is challenging to efficiently design and deploy recent advanced models (e.g. transformers) on tiny devices due to their severe hardware resource constraints. In this work, we propose TinyFormer, a framework specifically designed to develop and deploy resourc","authors":["Yang, Jianlei","Liao, Jiacheng","Lei, Fanding","Liu, Meichen","Long, Lingkun","Chen, Junyi"],"url":"https://arxiv.org/abs/2311.01759","arxiv_id":"2311.01759","published":"2025-11-27"},{"title":"Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA","abstract":"We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within a day -- attributes that are out-of-scope for current KGQA systems. BYOKG draws inspiration from the remarkable ability of humans to comprehend information present in an unseen KG through exploration -- starting at random nodes, inspecting","authors":["Agarwal, Dhruv","Das, Rajarshi","Khosla, Sopan","Gangadharaiah, Rashmi"],"url":"https://arxiv.org/abs/2311.07850","arxiv_id":"2311.07850","published":"2025-11-27"},{"title":"Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data","abstract":"Federated learning is an emerging distributed machine learning framework aiming at protecting data privacy. Data heterogeneity is one of the core challenges in federated learning, which could severely degrade the convergence rate and prediction performance of deep neural networks. To address this issue, we develop a novel personalized federated learning framework for heterogeneous data, which we refer to as FedSplit.","authors":["Wang, Feifei","Tang, Huiyun","Li, Yang"],"url":"https://arxiv.org/abs/2312.04281","arxiv_id":"2312.04281","published":"2025-11-27"},{"title":"Natural Strategic Ability in Stochastic Multi-Agent Systems","abstract":"Strategies synthesized using formal methods can be complex and often require infinite memory, which does not correspond to the expected behavior when trying to model Multi-Agent Systems (MAS). To capture such behaviors, natural strategies are a recently proposed framework striking a balance between the ability of agents to strategize with memory and the model-checking complexity, but until now has been restricted to ","authors":["Berthon, Raphaël","Katoen, Joost-Pieter","Mittelmann, Munyque","Murano, Aniello"],"url":"https://arxiv.org/abs/2401.12170","arxiv_id":"2401.12170","published":"2025-11-27"},{"title":"Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity","abstract":"Stochastic bilevel optimization (SBO) is becoming increasingly essential in machine learning due to its versatility in handling nested structures. To address large-scale SBO, decentralized approaches have emerged as effective paradigms in which nodes communicate with immediate neighbors without a central server, thereby improving communication efficiency and enhancing algorithmic robustness. However, most decentraliz","authors":["Kong, Boao","Zhu, Shuchen","Lu, Songtao","Huang, Xinmeng","Yuan, Kun"],"url":"https://arxiv.org/abs/2402.03167","arxiv_id":"2402.03167","published":"2025-11-27"},{"title":"Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing","abstract":"Bike-sharing systems (BSS) provide a sustainable urban mobility solution, but ensuring their reliability requires effective rebalancing strategies to address stochastic demand and prevent station imbalances. This paper proposes reinforcement learning (RL) algorithms for dynamic rebalancing problem with multiple vehicles, introducing and comparing two RL approaches: Single-policy RL and Dual-policy RL. We formulate th","authors":["Liang, Jiaqi","Liu, Defeng","Jena, Sanjay Dominik","Lodi, Andrea","Vidal, Thibaut"],"url":"https://arxiv.org/abs/2402.03589","arxiv_id":"2402.03589","published":"2025-11-27"},{"title":"Federated Learning: A Stochastic Approximation Approach","abstract":"This paper considers the Federated learning (FL) in a stochastic approximation (SA) framework. Here, each client $i$ trains a local model using its dataset $\\mathcal{D}^{(i)}$ and periodically transmits the model parameters $w^{(i)}_n$ to a central server, where they are aggregated into a global model parameter $\\bar{w}_n$ and sent back. The clients continue their training by re-initializing their local models with t","authors":["P V, Srihari","Paul, Anik Kumar","Bhikkaji, Bharath"],"url":"https://arxiv.org/abs/2402.12945","arxiv_id":"2402.12945","published":"2025-11-27"},{"title":"A Simple Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation","abstract":"Traffic signal control (TSC) is crucial for reducing traffic congestion leading to smoother traffic flow, reduced idle time, and mitigated CO2 emissions. In this paper, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. Unlike traditional feature-based approaches, vision-based methods depend much less on heuristics and predefined features, bringing promisi","authors":["He, Pan","Li, Quanyi","Yuan, Xiaoyong","Zhou, Bolei"],"url":"https://arxiv.org/abs/2403.06884","arxiv_id":"2403.06884","published":"2025-11-27"},{"title":"Activator: GLU Activation Function as the Core Component of a Vision Transformer","abstract":"The transformer architecture has driven many successes in a variety of tasks within the field of deep learning, in particular the recent advances in natural language processing (NLP) culminating with large language models (LLM). Adding to that success, transformer architecture has found widespread interest from computer vision (CV) researchers and practitioners, allowing for many advancements in vision-related tasks ","authors":["Abdullah, Abdullah Nazhat","Aydin, Tarkan"],"url":"https://arxiv.org/abs/2405.15953","arxiv_id":"2405.15953","published":"2025-11-27"},{"title":"Data Valuation by Fusing Global and Local Statistical Information","abstract":"Data valuation has garnered increasing attention in recent years, given the critical role of high-quality data in various applications. Among diverse data valuation approaches, Shapley value-based methods are predominant due to their strong theoretical grounding. However, the exact computation of Shapley values is often computationally prohibitive, prompting the development of numerous approximation techniques. Despi","authors":["Zhou, Xiaoling","Wu, Ou","Ng, Michael K.","Jiang, Hao"],"url":"https://arxiv.org/abs/2405.17464","arxiv_id":"2405.17464","published":"2025-11-27"},{"title":"Safety Control of Service Robots with LLMs and Embodied Knowledge Graphs","abstract":"Safety limitations in service robotics across various industries have raised significant concerns about the need for robust mechanisms ensuring that robots adhere to safe practices, thereby preventing actions that might harm humans or cause property damage. Despite advances, including the integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), challenges in ensuring consistent safety in autonomous ro","authors":["Qi, Yong","Kyebambo, Gabriel","Xie, Siyuan","Shen, Wei","Wang, Shenghui","Xie, Bitao"],"url":"https://arxiv.org/abs/2405.17846","arxiv_id":"2405.17846","published":"2025-11-27"},{"title":"CTSyn: A Foundation Model for Cross Tabular Data Generation","abstract":"Generative Foundation Models (GFMs) have achieved remarkable success in producing high-quality synthetic data for images and text. However, their application to tabular data presents significant challenges due to the heterogeneous nature of table features. Current cross-table learning frameworks struggle because they lack a generative model backbone and an effective mechanism to decode heterogeneous feature values. T","authors":["Lin, Xiaofeng","Xu, Chenheng","Yang, Matthew","Cheng, Guang"],"url":"https://arxiv.org/abs/2406.04619","arxiv_id":"2406.04619","published":"2025-11-27"},{"title":"Gram2Vec: An Interpretable Document Vectorizer","abstract":"We present Gram2Vec, a grammatical style embedding system that embeds documents into a higher dimensional space by extracting the normalized relative frequencies of grammatical features present in the text. Compared to neural approaches, Gram2Vec offers inherent interpretability based on how the feature vectors are generated. In this paper, we use authorship verification and AI detection as two applications to show h","authors":["Zeng, Peter","Stortz, Hannah","Sclafani, Eric","Shabaeva, Alina","Garza, Maria Elizabeth","Greeson, Daniel"],"url":"https://arxiv.org/abs/2406.12131","arxiv_id":"2406.12131","published":"2025-11-27"},{"title":"A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm","abstract":"Solving systems of linear equations is a fundamental problem, but it can be computationally intensive for classical algorithms in high dimensions. Existing quantum algorithms can achieve exponential speedups for the quantum linear system problem (QLSP) in terms of the problem dimension, but the advantage is bottlenecked by condition number of the coefficient matrix. In this work, we propose a new quantum algorithm fo","authors":["Kim, Junhyung Lyle","Chia, Nai-Hui","Kyrillidis, Anastasios"],"url":"https://arxiv.org/abs/2406.13879","arxiv_id":"2406.13879","published":"2025-11-27"},{"title":"Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness","abstract":"As deep neural networks (DNNs) are increasingly deployed in sensitive applications, ensuring their security and robustness has become critical. A major threat to DNNs arises from adversarial attacks, where small input perturbations can lead to incorrect predictions. Recent advances in adversarial training improve robustness by incorporating additional examples from external datasets or generative models. However, the","authors":["Chen, Erh-Chung","Chen, Pin-Yu","Chung, I-Hsin","Lee, Che-Rung"],"url":"https://arxiv.org/abs/2406.19622","arxiv_id":"2406.19622","published":"2025-11-27"}],"history":{"events":[{"year":2020,"text":"Nuclear physicist Mohsen Fakhrizadeh, regarded as the chief of Iran's nuclear program, was assassinated, allegedly by Mossad.","url":"https://en.wikipedia.org/wiki/Mohsen_Fakhrizadeh"},{"year":2009,"text":"A bomb exploded under, and derailed, a Russian high-speed train travelling between Moscow and Saint Petersburg, killing 28 passengers.","url":"https://en.wikipedia.org/wiki/2009_Nevsky_Express_bombing"},{"year":2009,"text":"Lady Gaga performed the first concert of The Monster Ball Tour, which became the highest-grossing tour in history for a debut headlining artist.","url":"https://en.wikipedia.org/wiki/Lady_Gaga"},{"year":2001,"text":"Astronomers announced the detection of sodium in the atmosphere of the extrasolar planet HD 209458 b (artist's impression pictured), the first exoplanet atmosphere to be measured.","url":"https://en.wikipedia.org/wiki/Sodium"},{"year":1999,"text":"The Labour Party defeated the governing National Party in the New Zealand general election, with Labour's Helen Clark becoming the country's first female prime minister to have won office at an election.","url":"https://en.wikipedia.org/wiki/New_Zealand_Labour_Party"},{"year":1991,"text":"Operations at the Reagan Test Site were disrupted due to the nearby formation of Tropical Storm Zelda.","url":"https://en.wikipedia.org/wiki/Ronald_Reagan_Space_and_Missile_Test_Range"},{"year":1989,"text":"A bomb placed by the Medellín Cartel in an attempt to kill Colombian presidential candidate César Gaviria destroyed Avianca Flight 203, killing all 107 people on board, excluding Gaviria, who was not on the flight.","url":"https://en.wikipedia.org/wiki/Medell%C3%ADn_Cartel"},{"year":1963,"text":"President Lyndon B. Johnson delivered the \"Let Us Continue\" speech, in which he advocated for civil-rights legislation and national cohesion, to a joint session of the U.S. Congress five days after the assassination of John F. Kennedy.","url":"https://en.wikipedia.org/wiki/Lyndon_B._Johnson"},{"year":1950,"text":"Korean War: The Chinese People's Volunteer Army launched multiple attacks against United Nations forces, beginning the Battle of Chosin Reservoir.","url":"https://en.wikipedia.org/wiki/Korean_War"},{"year":1945,"text":"A consortium of twenty-two U.S. charities founded CARE with the mission of delivering food aid to Europe in the aftermath of World War II.","url":"https://en.wikipedia.org/wiki/CARE_International"},{"year":1944,"text":"Between 3,500 and 4,000 tonnes of ordnance exploded at the RAF Fauld underground munitions storage depot in the largest non-nuclear explosion in the United Kingdom.","url":"https://en.wikipedia.org/wiki/RAF_Fauld_explosion"},{"year":1943,"text":"World War II: Australian forces began the Battle of Wareo by conducting clearing operations between Bonga and North Hill.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1895,"text":"Swedish chemist and industrialist Alfred Nobel (pictured) signed his last will and testament, setting aside the bulk of his estate to establish the Nobel Prize after his death.","url":"https://en.wikipedia.org/wiki/Alfred_Nobel"},{"year":1856,"text":"William III (pictured) unilaterally revised the constitution of Luxembourg, greatly expanding his powers as grand duke.","url":"https://en.wikipedia.org/wiki/William_III_of_the_Netherlands"},{"year":1835,"text":"James Pratt and John Smith became the last people to be executed in England for sodomy.","url":"https://en.wikipedia.org/wiki/James_Pratt_and_John_Smith"},{"year":1703,"text":"The great storm of 1703, one of the most severe storms to strike southern Great Britain, destroyed the first Eddystone Lighthouse off Plymouth.","url":"https://en.wikipedia.org/wiki/Great_storm_of_1703"},{"year":1161,"text":"A Song dynasty fleet defeated Jin ships in a naval engagement on the Yangtze river during the Jin–Song Wars.","url":"https://en.wikipedia.org/wiki/Song_dynasty"}],"births":[{"year":2003,"text":"Adéla Jergová, Slovak singer-songwriter","url":"https://en.wikipedia.org/wiki/Ad%C3%A9la_(singer)"},{"year":2001,"text":"Zoe Colletti, American actress","url":"https://en.wikipedia.org/wiki/Zoe_Colletti"},{"year":1996,"text":"Mike Williams, Dutch DJ and record producer","url":"https://en.wikipedia.org/wiki/Mike_Williams_(DJ)"},{"year":1995,"text":"Suliasi Vunivalu, Fijian rugby league player","url":"https://en.wikipedia.org/wiki/Suliasi_Vunivalu"},{"year":1993,"text":"Aubrey Peeples, American actress and singer","url":"https://en.wikipedia.org/wiki/Aubrey_Peeples"},{"year":1992,"text":"Ala Boratyn, Polish singer-songwriter","url":"https://en.wikipedia.org/wiki/Ala_Boratyn"},{"year":1992,"text":"Chanyeol, South Korean rapper, singer, songwriter, actor and model","url":"https://en.wikipedia.org/wiki/Chanyeol"},{"year":1992,"text":"Bradley Zimmer, American baseball player","url":"https://en.wikipedia.org/wiki/Bradley_Zimmer"}],"deaths":[{"year":2024,"text":"Mary McGee, American motorcycle racer (born 1936)","url":"https://en.wikipedia.org/wiki/Mary_McGee"},{"year":2021,"text":"Apetor, Norwegian YouTuber (born 1964)","url":"https://en.wikipedia.org/wiki/Apetor"},{"year":2020,"text":"Mohsen Fakhrizadeh, Iranian nuclear scientist (born 1958)","url":"https://en.wikipedia.org/wiki/Mohsen_Fakhrizadeh"},{"year":2016,"text":"Ioannis Grivas, Greek statesman (born 1923)","url":"https://en.wikipedia.org/wiki/Ioannis_Grivas"},{"year":2015,"text":"Mark Behr, Tanzanian-South African author and academic (born 1963)","url":"https://en.wikipedia.org/wiki/Mark_Behr"},{"year":2015,"text":"Maurice Strong, Canadian businessman and diplomat (born 1929)","url":"https://en.wikipedia.org/wiki/Maurice_Strong"},{"year":2015,"text":"Garrett Swasey, American figure skater and coach (born 1971)","url":"https://en.wikipedia.org/wiki/Garrett_Swasey"},{"year":2015,"text":"Philippe Washer, Belgian tennis player and golfer (born 1924)","url":"https://en.wikipedia.org/wiki/Philippe_Washer"}]},"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."}