Tootfinder

Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance
Roy M. Gabriel, Mohammadreza Zandehshahvar, Marly van Assen, Nattakorn Kittisut, Kyle Peters, Carlo N. De Cecco, Ali Adibi
https://arxiv.org/abs/2508.21263

Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance
To reduce the amount of required labeled data for lung disease severity classification from chest X-rays (CXRs) under class imbalance, this study applied deep active learning with a Bayesian Neural Network (BNN) approximation and weighted loss function. This retrospective study collected 2,319 CXRs from 963 patients (mean age, 59.2 $\pm$ 16.6 years; 481 female) at Emory Healthcare affiliated hospitals between January and November 2020. All patients had clinically confirmed COVID-19. Each CXR wa…

RANA: Robust Active Learning for Noisy Network Alignment
Yixuan Nan, Xixun Lin, Yanmin Shang, Zhuofan Li, Can Zhao, Yanan Cao
https://arxiv.org/abs/2507.22434 https://

RANA: Robust Active Learning for Noisy Network Alignment
Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of noise in network alignment, which can substantially undermine model performance. Such noise mainly includes structural noise from noisy edges and labeling noise caused by human-induced and process-driven errors. To address these problems, we propose RANA, a Robust Active learning framework for noisy Network Alignment. …

A GENERIC-guided active learning SPH method for viscoelastic fluids using Gaussian process regression
Xuekai Dong, David Nieto Simavilla, Jie Ouyang, Xiaodong Wang, Marco Ellero
https://arxiv.org/abs/2506.21877

A GENERIC-guided active learning SPH method for viscoelastic fluids using Gaussian process regression
When applying machine learning methods to learn viscoelastic constitutive relations, the polymer history dependence in viscoelastic fluids and the generalization ability of machine learning models are challenging. In this paper, guided by the general equation for nonequilibrium reversible-irreversible coupling (GENERIC) framework, a novel GENERIC-guided active learning smoothed particle hydrodynamics (${\rm{G^2ALSPH}}$) method is proposed to obtain effective constitutive relations for reliable …

Online design of experiments by active learning for nonlinear system identification
Kui Xie, Alberto Bemporad
https://arxiv.org/abs/2506.21754 https://

Online design of experiments by active learning for nonlinear system identification
We investigate the use of active-learning (AL) strategies to generate the input excitation signal at runtime for system identification of linear and nonlinear autoregressive and state-space models. We adapt various existing AL approaches for static model regression to the dynamic context, coupling them with a Kalman filter to update the model parameters recursively, and also cope with the presence of input and output constraints. We show the increased sample efficiency of the proposed approache…

StepAL: Step-aware Active Learning for Cataract Surgical Videos
Nisarg A. Shah, Bardia Safaei, Shameema Sikder, S. Swaroop Vedula, Vishal M. Patel
https://arxiv.org/abs/2507.22059

StepAL: Step-aware Active Learning for Cataract Surgical Videos
Active learning (AL) can reduce annotation costs in surgical video analysis while maintaining model performance. However, traditional AL methods, developed for images or short video clips, are suboptimal for surgical step recognition due to inter-step dependencies within long, untrimmed surgical videos. These methods typically select individual frames or clips for labeling, which is ineffective for surgical videos where annotators require the context of the entire video for annotation. To addre…

Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov, Alexey Skrynnik
https://arxiv.org/abs/2506.23793

Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment. While solving MAPF optimally has been proven to be NP-hard, scalable, and efficient, solvers are vital for real-world applications like logistics, search-and-rescue, etc. To this end, decentralized suboptimal MAPF solvers that leverage machine learning have come on stage. Building on the success of the recently intro…

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
Jing Wang, Amar Sra, Jeremy C. Weiss
https://arxiv.org/abs/2506.22444

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
The long-term effects of Postacute Sequelae of SARS-CoV-2, known as PASC, pose a significant challenge to healthcare systems worldwide. Accurate identification of progression events, such as hospitalization and reinfection, is essential for effective patient management and resource allocation. However, traditional models trained on structured data struggle to capture the nuanced progression of PASC. In this study, we introduce the first publicly available cohort of 18 PASC patients, with text t…

Active Learning for Manifold Gaussian Process Regression
Yuanxing Cheng, Lulu Kang, Yiwei Wang, Chun Liu
https://arxiv.org/abs/2506.20928 https://

Active Learning for Manifold Gaussian Process Regression
This paper introduces an active learning framework for manifold Gaussian Process (GP) regression, combining manifold learning with strategic data selection to improve accuracy in high-dimensional spaces. Our method jointly optimizes a neural network for dimensionality reduction and a Gaussian process regressor in the latent space, supervised by an active learning criterion that minimizes global prediction error. Experiments on synthetic data demonstrate superior performance over randomly sequen…

Finding Similar Objects and Active Inference for Surprise in Numenta Neocortex Model
Hajime Kawakami
https://arxiv.org/abs/2506.21554 https://

Finding Similar Objects and Active Inference for Surprise in Numenta Neocortex Model
Jeff Hawkins and his colleagues in Numenta have proposed the thousand-brains system. This is a model of the structure and operation of the neocortex and is under investigation as a new form of artificial intelligence. In their study, learning and inference algorithms running on the system are proposed, where the prediction is an important function. The author believes that one of the most important capabilities of the neocortex in addition to prediction is the ability to make association, that …

Angle-distance decomposition based on deep learning for active sonar detection
Jichao Zhang, Xiao-Lei Zhang, Kunde Yang
https://arxiv.org/abs/2507.20651 https://

Angle-distance decomposition based on deep learning for active sonar detection
Underwater target detection using active sonar constitutes a critical research area in marine sciences and engineering. However, traditional signal processing methods face significant challenges in complex underwater environments due to noise, reverberation, and interference. To address these issues, this paper presents a deep learning-based active sonar target detection method that decomposes the detection process into separate angle and distance estimation tasks. Active sonar target detection…

Persistence Paradox in Dynamic Science
Honglin Bao, Kai Li
https://arxiv.org/abs/2506.22729 https://arxiv.org/pdf/2506.22729

Persistence Paradox in Dynamic Science
Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during periods of paradigm shift. We focus on the deep learning revolution catalyzed by AlexNet in 2012. Analyzing the 20-year career trajectories of over 5,000 scientists who were active in top machine learning venues during the preceding decade, we examine how their research focus and output…

Average-case complexity in statistical inference: A puzzle-driven research seminar
Anastasia Kireeva, Afonso S. Bandeira
https://arxiv.org/abs/2506.22182 h…

Average-case complexity in statistical inference: A puzzle-driven research seminar
These notes describe our experience with running a student seminar on average-case complexity in statistical inference using the jigsaw learning format at ETH Zurich in Fall of 2024. The jigsaw learning technique is an active learning technique where students work in groups on independent parts of the task and then reassemble the groups to combine all the parts together. We implemented this technique for the proofs of various recent research developments, combined with a presentation by one of …

ActLoc: Learning to Localize on the Move via Active Viewpoint Selection
Jiajie Li, Boyang Sun, Luca Di Giammarino, Hermann Blum, Marc Pollefeys
https://arxiv.org/abs/2508.20981 …

ActLoc: Learning to Localize on the Move via Active Viewpoint Selection
Reliable localization is critical for robot navigation, yet most existing systems implicitly assume that all viewing directions at a location are equally informative. In practice, localization becomes unreliable when the robot observes unmapped, ambiguous, or uninformative regions. To address this, we present ActLoc, an active viewpoint-aware planning framework for enhancing localization accuracy for general robot navigation tasks. At its core, ActLoc employs a largescale trained attention-base…

Active Learning for Predicting the Enthalpy of Mixing inBinary Liquids Based on Ab Initio Molecular Dynamics
Quentin Bizot, Ryo Tamura, Guillaume Deffrennes
https://arxiv.org/abs/2507.20885

Active Learning for Predicting the Enthalpy of Mixing inBinary Liquids Based on Ab Initio Molecular Dynamics
The enthalpy of mixing in the liquid phase is an important property for predicting phase formation in alloys. It can be estimated in a large compositional space from pair wise interactions between elements, for which machine learning has recently provided the most accurate predictions. Further improvements requires acquiring high quality data in liquids where models are poorly constrained. In this study, we propose an active learning approach to identify in which liquids additional data are mos…

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Xiang Lan, Tim Menzies, Bowen Xu
https://arxiv.org/abs/2506.20444 http…

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Vulnerability detection is crucial for identifying security weaknesses in software systems. However, the effectiveness of machine learning models in this domain is often hindered by low-quality training datasets, which contain noisy, mislabeled, or imbalanced samples. This paper proposes a novel dataset maps-empowered approach that systematically identifies and mitigates hard-to-learn outliers, referred to as "bad seeds", to improve model training efficiency. Our approach can categorize trainin…

Active Learning for Neurosymbolic Program Synthesis
Celeste Barnaby, Qiaochu Chen, Ramya Ramalingam, Osbert Bastani, Isil Dillig
https://arxiv.org/abs/2508.15750 https://…

Active Learning for Neurosymbolic Program Synthesis
The goal of active learning for program synthesis is to synthesize the desired program by asking targeted questions that minimize user interaction. While prior work has explored active learning in the purely symbolic setting, such techniques are inadequate for the increasingly popular paradigm of neurosymbolic program synthesis, where the synthesized program incorporates neural components. When applied to the neurosymbolic setting, such techniques can -- and, in practice, do -- return an uninte…

Automated simulation-based design via multi-fidelity active learning and optimisation for laser direct drive implosions
A. J. Crilly, P. W. Moloney, D. Shi, E. A. Ferdinandi
https://arxiv.org/abs/2508.20878

Automated simulation-based design via multi-fidelity active learning and optimisation for laser direct drive implosions
The design of inertial fusion experiments is a complex task as driver energy must be delivered in a precise manner to a structured target to achieve a fast, but hydrodynamically stable, implosion. Radiation-hydrodynamics simulation codes are an essential tool in this design process. However, multi-dimensional simulations that capture hydrodynamic instabilities are more computationally expensive than optimistic, 1D, spherically symmetric simulations which are often the primary design tool. In th…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Active learning for level set estimation under input uncertainty and its extensions
Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi

Balancing the exploration-exploitation trade-off in active learning for surrogate model-based reliability analysis via multi-objective optimization
Jonathan A. Moran, Pablo G. Morato
https://arxiv.org/abs/2508.18170

Balancing the exploration-exploitation trade-off in active learning for surrogate model-based reliability analysis via multi-objective optimization
Reliability assessment of engineering systems is often hindered by the need to evaluate limit-state functions through computationally expensive simulations, rendering standard sampling impractical. An effective solution is to approximate the limit-state function with a surrogate model iteratively refined through active learning, thereby reducing the number of expensive simulations. At each iteration, an acquisition strategy selects the next sample by balancing two competing goals: exploration, …

Attackers Strike Back? Not Anymore - An Ensemble of RL Defenders Awakens for APT Detection
Sidahmed Benabderrahmane, Talal Rahwan
https://arxiv.org/abs/2508.19072 https://

Attackers Strike Back? Not Anymore - An Ensemble of RL Defenders Awakens for APT Detection
Advanced Persistent Threats (APTs) represent a growing menace to modern digital infrastructure. Unlike traditional cyberattacks, APTs are stealthy, adaptive, and long-lasting, often bypassing signature-based detection systems. This paper introduces a novel framework for APT detection that unites deep learning, reinforcement learning (RL), and active learning into a cohesive, adaptive defense system. Our system combines auto-encoders for latent behavioral encoding with a multi-agent ensemble of …

Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
Alexander Yeung, Peter DelMastro, Arjun Karuvally, Hava Siegelmann, Edward Rietman, Hananel Hazan
https://arxiv.org/abs/2507.21377

Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
Reservoir Computing is a machine learning approach that uses the rich repertoire of complex system dynamics for function approximation. Current approaches to reservoir computing use a network of coupled integrating neurons that require a steady current to maintain activity. Here, we introduce a small world graph of differentiating neurons that are active only when there are changes in input as an alternative to integrating neurons as a reservoir computing substrate. We find the coupling strengt…

Controlling Topological Defects in Polar Fluids via Reinforcement Learning
Abhinav Singh, Petros Koumoutsakos
https://arxiv.org/abs/2507.19298 https://arxi…

Controlling Topological Defects in Polar Fluids via Reinforcement Learning
Topological defects in active polar fluids exhibit complex dynamics driven by internally generated stresses, reflecting the deep interplay between topology, flow, and non-equilibrium hydrodynamics. Feedback control offers a powerful means to guide such systems, enabling transitions between dynamic states. We investigated closed-loop steering of integer-charged defects in a confined active fluid by modulating the spatial profile of activity. Using a continuum hydrodynamic model, we show that loc…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Active learning for level set estimation under input uncertainty and its extensions
Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi

TS-Insight: Visualizing Thompson Sampling for Verification and XAI
Parsa Vares, \'Eloi Durant, Jun Pang, Nicolas M\'edoc, Mohammad Ghoniem
https://arxiv.org/abs/2507.19898

TS-Insight: Visualizing Thompson Sampling for Verification and XAI
Thompson Sampling (TS) and its variants are powerful Multi-Armed Bandit algorithms used to balance exploration and exploitation strategies in active learning. Yet, their probabilistic nature often turns them into a ``black box'', hindering debugging and trust. We introduce TS-Insight, a visual analytics tool explicitly designed to shed light on the internal decision mechanisms of Thompson Sampling-based algorithms, for model developers. It comprises multiple plots, tracing for each arm the evol…

Euclid: A machine-learning search for dual and lensed AGN at sub-arcsec separations
L. Ulivi, F. Mannucci, M. Scialpi, C. Marconcini, G. Cresci, A. Marconi, A. Feltre, M. Ginolfi, F. Ricci, D. Sluse, F. Belfiore, E. Bertola, C. Bracci, E. Cataldi, M. Ceci, Q. D'Amato, I. Lamperti, R. B. Metcalf, B. Moreschini, M. Perna, G. Tozzi, G. Venturi, M. V. Zanchettin, Y. Fu, M. Huertas-Company, M. Mezcua, M. P"ontinen, V. Scottez, M. Siudek, H. Teimoorinia, I. T. Andika, J. A. Acevedo …

Euclid: A machine-learning search for dual and lensed AGN at sub-arcsec separations
Cosmological models of hierarchical structure formation predict the existence of a widespread population of dual accreting supermassive black holes (SMBHs) on kpc-scale separations, corresponding to projected distances < 0".8 at redshifts higher than 0.5. However, close companions to known active galactic nuclei (AGN) or quasars (QSOs) can also be multiple images of the object itself, strongly lensed by a foreground galaxy, as well as foreground stars in a chance superposition. Thanks to its la…

Automata Learning -- Expect Delays!
Gabriel Dengler, Sven Apel, Holger Hermanns
https://arxiv.org/abs/2508.16384 https://arxiv.org/pdf/2508.16384

Automata Learning -- Expect Delays!
This paper studies active automata learning (AAL) in the presence of stochastic delays. We consider Mealy machines that have stochastic delays associated with each transition and explore how the learner can efficiently arrive at faithful estimates of those machines, the precision of which crucially relies on repetitive sampling of transition delays. While it is possible to naïvely integrate the delay sampling into AAL algorithms such as $L^*$, this leads to considerable oversampling near the r…

Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos, Antonio A. Gentile, Osvaldo Simeone
https://arxiv.org/abs/2506.19714 …

Conservative quantum offline model-based optimization
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experimentation. Recent work has introduced quantum extremal learning (QEL), which leverages the expressive power of variational quantum circuits to learn accurate surrogate functions by training on a few data points. However, as widely studied in the classical machine learning literature, predictive models may incorrectly e…

Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics
Yu Bao, Keke Song, Jiahui Liu, Yanzhou Wang, Yifei Ning, Penghua Ying, Ping Qian
https://arxiv.org/abs/2508.20350

Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics
Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we present an accurate and transferable machine-learned potential (MLP) for the tungsten-hydrogen binary system within the neuroevolution potential (NEP) framework, trained through active learning on extensive density functional theory data. The developed NEP-WH model reproduces a wide range of lattice and defect properties…

LoUQAL: Low-fidelity informed Uncertainty Quantification for Active Learning in the chemical configuration space
Vivin Vinod, Peter Zaspel
https://arxiv.org/abs/2508.15577 https…

LoUQAL: Low-fidelity informed Uncertainty Quantification for Active Learning in the chemical configuration space
Uncertainty quantification is an important scheme in active learning techniques, including applications in predicting quantum chemical properties. In quantum chemical calculations, there exists the notion of a fidelity, a less accurate computation is accessible at a cheaper computational cost. This work proposes a novel low-fidelity informed uncertainty quantification for active learning with applications in predicting diverse quantum chemical properties such as excitation energies and \textit{…

*I bought an actioncam.*
Finally!
I'm learning how to use it properly. I tried to capture some impressions from our #cycling ride last weekend near #Tegernsee. (And learned that I should really buy an appropriate SD Card.)
Don't worry, I'm still commited to photograph…

Active Learning via Heteroskedastic Rational Kriging
Shangkun Wang, V. Roshan Joseph
https://arxiv.org/abs/2507.10952 https://arxiv.o…

Active Learning via Heteroskedastic Rational Kriging
Active learning methods for emulating complex computer models that rely on stationary Gaussian processes tend to produce design points that uniformly fill the entire experimental region, which can be wasteful for functions which vary only in small regions. In this article, we propose a new Gaussian process model that captures the heteroskedasticity of the function. Active learning using this new model can place design points in the more interesting regions of the response surface, and thus obta…

Learning Approach to Efficient Vision-based Active Tracking of a Flying Target by an Unmanned Aerial Vehicle
Jagadeswara PKV Pothuri, Aditya Bhatt, Prajit KrisshnaKumar, Manaswin Oddiraju, Souma Chowdhury
https://arxiv.org/abs/2506.18264

Learning Approach to Efficient Vision-based Active Tracking of a Flying Target by an Unmanned Aerial Vehicle
Autonomous tracking of flying aerial objects has important civilian and defense applications, ranging from search and rescue to counter-unmanned aerial systems (counter-UAS). Ground based tracking requires setting up infrastructure, could be range limited, and may not be feasible in remote areas, crowded cities or in dense vegetation areas. Vision based active tracking of aerial objects from another airborne vehicle, e.g., a chaser unmanned aerial vehicle (UAV), promises to fill this important …

An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify
Shivam Verma, Vivian Chen, Darren Mei
https://arxiv.org/abs/2506.18735

An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify
Spotify, a large-scale multimedia platform, attracts over 675 million monthly active users who collectively consume millions of hours of music, podcasts, audiobooks, and video content. This diverse content consumption pattern introduces unique challenges for computational advertising, which must effectively integrate a variety of ad modalities, including audio, video, and display, within a single user experience. Traditional ad recommendation models, primarily designed for foregrounded experien…

Findings of MEGA: Maths Explanation with LLMs using the Socratic Method for Active Learning
Tosin Adewumi, Foteini Simistira Liwicki, Marcus Liwicki, Viktor Gardelli, Lama Alkhaled, Hamam Mokayed
https://arxiv.org/abs/2507.12079

Findings of MEGA: Maths Explanation with LLMs using the Socratic Method for Active Learning
This paper presents an intervention study on the effects of the combined methods of (1) the Socratic method, (2) Chain of Thought (CoT) reasoning, (3) simplified gamification and (4) formative feedback on university students' Maths learning driven by large language models (LLMs). We call our approach Mathematics Explanations through Games by AI LLMs (MEGA). Some students struggle with Maths and as a result avoid Math-related discipline or subjects despite the importance of Maths across many fie…

Systematic Review Of Collaborative Learning Activities For Promoting AI Literacy
Ashish Hingle, Aditya Johri
https://arxiv.org/abs/2508.15111 https://arxiv…

Systematic Review Of Collaborative Learning Activities For Promoting AI Literacy
Improving artificial intelligence (AI) literacy has become an important consideration for academia and industry with the widespread adoption of AI technologies. Collaborative learning (CL) approaches have proven effective for information literacy, and in this study, we investigate the effectiveness of CL in improving AI knowledge and skills. We systematically collected data to create a corpus of nine studies from 2015-2023. We used the Interactive-Constructive-Active-Passive (ICAP) framework to…

Metric Matters: A Formal Evaluation of Similarity Measures in Active Learning for Cyber Threat Intelligence
Sidahmed Benabderrahmane, Talal Rahwan
https://arxiv.org/abs/2508.19019

Metric Matters: A Formal Evaluation of Similarity Measures in Active Learning for Cyber Threat Intelligence
Advanced Persistent Threats (APTs) pose a severe challenge to cyber defense due to their stealthy behavior and the extreme class imbalance inherent in detection datasets. To address these issues, we propose a novel active learning-based anomaly detection framework that leverages similarity search to iteratively refine the decision space. Built upon an Attention-Based Autoencoder, our approach uses feature-space similarity to identify normal-like and anomaly-like instances, thereby enhancing mod…

A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources
Hamid Varmazyari, Masoud H. Nazari
https://arxiv.org/abs/2508.18500

A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources
This paper presents a machine-learning based Stochastic Hybrid System (SHS) modeling framework to detect contingencies in active distribution networks populated with inverter-based resources (IBRs). In particular, this framework allows detecting unobservable contingencies, which cannot be identified by normal sensing systems. First, a state-space SHS model combining conventional and IRB-based resources is introduced to formulate the dynamic interaction between continuous states of distribution …

Tractable Metric Spaces and the Continuity of Magnitude
Sara Kali\v{s}nik, Davorin Le\v{s}nik
https://arxiv.org/abs/2506.21128 https://

Tractable Metric Spaces and the Continuity of Magnitude
Magnitude is an isometric invariant of metric spaces introduced by Leinster. Since its inception, it has inspired active research into its connections with integral geometry, geometric measure theory, fractal dimensions, persistent homology, and applications in machine learning. In particular, when it comes to applications, continuity and stability of invariants play an important role. Although it has been shown that magnitude is nowhere continuous on the Gromov--Hausdorff space of finite metri…

Hamiltonian parameter inference from RIXS spectra with active learning
Marton K. Lajer, Xin Dai, Kipton Barros, Matthew R. Carbone, S. Johnston, M. P. M. Dean
https://arxiv.org/abs/2507.16021

Hamiltonian parameter inference from RIXS spectra with active learning
Identifying model Hamiltonians is a vital step toward creating predictive models of materials. Here, we combine Bayesian optimization with the EDRIXS numerical package to infer Hamiltonian parameters from resonant inelastic X-ray scattering (RIXS) spectra within the single atom approximation. To evaluate the efficacy of our method, we test it on experimental RIXS spectra of NiPS3, NiCl2, Ca3LiOsO6, and Fe2O3, and demonstrate that it can reproduce results obtained from hand-fitted parameters to …

Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki, Hiroshi Saruwatari
https://arxiv.org/abs/2507.08319

Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
The construction of high-quality datasets is a cornerstone of modern text-to-speech (TTS) systems. However, the increasing scale of available data poses significant challenges, including storage constraints. To address these issues, we propose a TTS corpus construction method based on active learning. Unlike traditional feed-forward and model-agnostic corpus construction approaches, our method iteratively alternates between data collection and model training, thereby focusing on acquiring data …

Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter
Diptabrata Paul, Nikola Milosevic, Nico Scherf, Frank Cichos
https://arxiv.org/abs/2508.17921

Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter
Living microorganisms have evolved dedicated sensory machinery to detect environmental perturbations, processing these signals through biochemical networks to guide behavior. Replicating such capabilities in synthetic active matter remains a fundamental challenge. Here, we demonstrate that synthetic active particles can adapt to hidden hydrodynamic perturbations through physical embodiment alone, without explicit sensing mechanisms. Using reinforcement learning to control self-thermophoretic pa…

Active Query Selection for Crowd-Based Reinforcement Learning
Jonathan Erskine, Taku Yamagata, Ra\'ul Santos-Rodr\'iguez
https://arxiv.org/abs/2508.19132 https://…

Active Query Selection for Crowd-Based Reinforcement Learning
Preference-based reinforcement learning has gained prominence as a strategy for training agents in environments where the reward signal is difficult to specify or misaligned with human intent. However, its effectiveness is often limited by the high cost and low availability of reliable human input, especially in domains where expert feedback is scarce or errors are costly. To address this, we propose a novel framework that combines two complementary strategies: probabilistic crowd modelling to …

Multi-institutional assessment of Peer Instruction implementation and impacts using the Framework for Interactive Learning in Lectures
Ibukunoluwa Bukola, Meagan Sundstrom, Justin Gambrell, Olive Ross, Adrienne L. Traxler, Eric Brewe
https://arxiv.org/abs/2508.08422

Multi-institutional assessment of Peer Instruction implementation and impacts using the Framework for Interactive Learning in Lectures
Substantial research indicates that active learning methods improve student learning more than traditional lecturing. Accordingly, current studies aim to characterize and evaluate different instructors' implementations of active learning methods. Peer Instruction is one of the most commonly used active learning methods in undergraduate physics instruction and typically involves the use of classroom response systems (e.g., clickers) where instructors pose conceptual questions that students answe…

Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization
Shayan S. Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius, Cara Cronin, Jason E. Hein, Jason Hattrick-Simpers
https://arxiv.org/abs/2507.19316

Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization
As demand for high-purity lithium surges with the growth of the electric vehicle (EV) industry, cost-effective extraction from lower-grade North American sources like the Smackover Formation is critical. These resources, unlike high-purity South American brines, require innovative purification techniques to be economically viable. Continuous crystallization is a promising method for producing battery-grade lithium carbonate, but its optimization is challenged by a complex parameter space and li…

CLEVER: Stream-based Active Learning for Robust Semantic Perception from Human Instructions
Jongseok Lee, Timo Birr, Rudolph Triebel, Tamim Asfour
https://arxiv.org/abs/2507.15499

CLEVER: Stream-based Active Learning for Robust Semantic Perception from Human Instructions
We propose CLEVER, an active learning system for robust semantic perception with Deep Neural Networks (DNNs). For data arriving in streams, our system seeks human support when encountering failures and adapts DNNs online based on human instructions. In this way, CLEVER can eventually accomplish the given semantic perception tasks. Our main contribution is the design of a system that meets several desiderata of realizing the aforementioned capabilities. The key enabler herein is our Bayesian for…

OmViD: Omni-supervised active learning for video action detection
Aayush Rana, Akash Kumar, Vibhav Vineet, Yogesh S Rawat
https://arxiv.org/abs/2508.13983 https://

OmViD: Omni-supervised active learning for video action detection
Video action detection requires dense spatio-temporal annotations, which are both challenging and expensive to obtain. However, real-world videos often vary in difficulty and may not require the same level of annotation. This paper analyzes the appropriate annotation types for each sample and their impact on spatio-temporal video action detection. It focuses on two key aspects: 1) how to obtain varying levels of annotation for videos, and 2) how to learn action detection from different annotati…

From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning
Lixiang Yan
https://arxiv.org/abs/2508.14825 https://

From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning
The role of Artificial Intelligence (AI) in education is undergoing a rapid transformation, moving beyond its historical function as an instructional tool towards a new potential as an active participant in the learning process. This shift is driven by the emergence of agentic AI, autonomous systems capable of proactive, goal-directed action. However, the field lacks a robust conceptual framework to understand, design, and evaluate this new paradigm of human-AI interaction in learning. This pap…

Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
Aaron Ho (MIT Plasma Science and Fusion Center, Cambridge, USA), Lorenzo Zanisi (UKAEA Culham Centre for Fusion Energy, Abingdon, UK), Bram de Leeuw (Radboud University, Nijmegen, Netherlands), Vincent Galvan (MIT Plasma Science and Fusion Center, Cambridge, USA), Pablo Rodriguez-Fernandez (MIT Plasma Science and Fusion Center, Cambridge, USA), Nath…

Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
This work demonstrates a proof-of-principle for using uncertainty-aware architectures, in combination with active learning techniques and an in-the-loop physics simulation code as a data labeller, to construct efficient datasets for data-driven surrogate model generation. Building off of a previous proof-of-principle successfully demonstrating training set reduction on static pre-labelled datasets, using the ADEPT framework, this strategy was applied again to the plasma turbulent transport prob…

Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids
Sarra Bouchkati, Ramil Sabirov, Steffen Kortmann, Andreas Ulbig
https://arxiv.org/abs/2506.19353

Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids
This paper introduces an efficient Residual Reinforcement Learning (RRL) framework for voltage control in active distribution grids. Voltage control remains a critical challenge in distribution grids, where conventional Reinforcement Learning (RL) methods often suffer from slow training convergence and inefficient exploration. To overcome these challenges, the proposed RRL approach learns a residual policy on top of a modified Sequential Droop Control (SDC) mechanism, ensuring faster convergenc…

Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
Chun-Yi Chang, Chih-Li Sung
https://arxiv.org/abs/2508.16434 https://

Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings remains challenging due to the need for efficient dependency modeling. We propose the Deep Intrinsic Coregionalization Multi-Output Gaussian Process (deepICMGP) surrogate for computer simulation experiments involving multiple outputs, which extends the Intrinsic Coregionalization Model (ICM) by introducing hierarchica…

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Shae McFadden, Myles Foley, Mario D'Onghia, Chris Hicks, Vasilios Mavroudis, Nicola Paoletti, Fabio Pierazzi
https://arxiv.org/abs/2508.18839

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation cannot optimize when to defer decisions to manual labeling and adaptation. Modern malware detection pipelines combine classifiers with monthly active learning (AL) and rejection mechanisms to mitigate t…

aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields
Tobias Henkes, Shubham Sharma, Alexandre Tkatchenko, Mariana Rossi, Igor Poltavskyi
https://arxiv.org/abs/2508.12888

aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields
Recent advances in machine learning force fields (MLFF) have significantly extended the capabilities of atomistic simulations. This progress highlights the critical need for reliable reference datasets, accurate MLFFs, and, crucially, efficient active learning strategies to enable robust modeling of complex chemical and materials systems. Here, we introduce aims-PAX, an automated, multi-trajectory active learning framework that streamlines the development of MLFFs. Designed for both experts and…

Active {\Delta}-learning with universal potentials for global structure optimization
Joe Pitfield, Mads-Peter Verner Christiansen, Bj{\o}rk Hammer
https://arxiv.org/abs/2507.18485

Active Δ-learning with universal potentials for global structure optimization
Universal machine learning interatomic potentials (uMLIPs) have recently been formulated and shown to generalize well. When applied out-of-sample, further data collection for improvement of the uMLIPs may, however, be required. In this work we demonstrate that, whenever the envisaged use of the MLIPs is global optimization, the data acquisition can follow an active learning scheme in which a gradually updated uMLIP directs the finding of new structures, which are subsequently evaluated at the d…

Selection-Based Vulnerabilities: Clean-Label Backdoor Attacks in Active Learning
Yuhan Zhi, Longtian Wang, Xiaofei Xie, Chao Shen, Qiang Hu, Xiaohong Guan
https://arxiv.org/abs/2508.05681

Selection-Based Vulnerabilities: Clean-Label Backdoor Attacks in Active Learning
Active learning(AL), which serves as the representative label-efficient learning paradigm, has been widely applied in resource-constrained scenarios. The achievement of AL is attributed to acquisition functions, which are designed for identifying the most important data to label. Despite this success, one question remains unanswered: is AL safe? In this work, we introduce ALA, a practical and the first framework to utilize the acquisition function as the poisoning attack surface to reveal the w…

Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers
Ian Chuang, Andrew Lee, Dechen Gao, Jinyu Zou, Iman Soltani
https://arxiv.org/abs/2507.15833

Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers
Human vision is a highly active process driven by gaze, which directs attention and fixation to task-relevant regions and dramatically reduces visual processing. In contrast, robot learning systems typically rely on passive, uniform processing of raw camera images. In this work, we explore how incorporating human-like active gaze into robotic policies can enhance both efficiency and performance. We build on recent advances in foveated image processing and apply them to an Active Vision robot sy…

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method
Matteo Ba\`u (Ricerca sul Sistema Energetico), Luca Perbellini (Politecnico di Milano), Samuele Grillo (Politecnico di Milano)
https://arxiv.org/abs/2508.19083

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method
Several methods have been proposed in the literature to improve the quality of AC optimal power flow (AC-OPF) datasets used in machine learning (ML) models. Yet, scalability to large power systems remains unaddressed and comparing generation approaches is still hindered by the absence of widely accepted metrics quantifying AC-OPF dataset quality. In this work, we tackle both these limitations. We provide a simple heuristic that samples load setpoints uniformly in total load active power, rather…

Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl, G\"oran Kauermann, Barbara Plank, Matthias A{\ss}enmacher
https://arxiv.org/abs/2507.02593

Revisiting Active Learning under (Human) Label Variation
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, especially in natural language processing, annotation frameworks often still rest on the assumption of a single ground truth. This overlooks human label variation (HLV), the occurrence of plausible differences in annotations, as an informative signal. Similarly, active learning (AL), a popular approach to optimizing th…

VQA support to Arabic Language Learning Educational Tool
Khaled Bachir Delassi (LIM Lab, Amar Telidji University, Laghouat, Algeria), Lakhdar Zeggane (LIM Lab, Amar Telidji University, Laghouat, Algeria), Hadda Cherroun (LIM Lab, Amar Telidji University, Laghouat, Algeria), Abdelhamid Haouhat (LIM Lab, Amar Telidji University, Laghouat, Algeria), Kaoutar Bouzouad (Computer Science Dept., USTHB, Algiers, Algeria)

VQA support to Arabic Language Learning Educational Tool
We address the problem of scarcity of educational Arabic Language Learning tools that advocate modern pedagogical models such as active learning which ensures language proficiency. In fact, we investigate the design and evaluation of an AI-powered educational tool designed to enhance Arabic language learning for non-native speakers with beginner-to-intermediate proficiency level. The tool leverages advanced AI models to generate interactive visual quizzes, deploying Visual Question Answering as…

A Novel Active Learning Approach to Label One Million Unknown Malware Variants
Ahmed Bensaoud, Jugal Kalita
https://arxiv.org/abs/2507.02959 https://

A Novel Active Learning Approach to Label One Million Unknown Malware Variants
Active learning for classification seeks to reduce the cost of labeling samples by finding unlabeled examples about which the current model is least certain and sending them to an annotator/expert to label. Bayesian theory can provide a probabilistic view of deep neural network models by asserting a prior distribution over model parameters and estimating the uncertainties by posterior distribution over these parameters. This paper proposes two novel active learning approaches to label one milli…

ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds
Shanle Yao, Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
https://arxiv.org/abs/2508.09058

ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds
Video Anomaly Detection (VAD) can play a key role in spotting unusual activities in video footage. VAD is difficult to use in real-world settings due to the dynamic nature of human actions, environmental variations, and domain shifts. Traditional evaluation metrics often prove inadequate for such scenarios, as they rely on static assumptions and fall short of identifying a threshold that distinguishes normal from anomalous behavior in dynamic settings. To address this, we introduce an active le…

Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification
Romain Ait Abdelmalek-Lomenech (L2S, RT-UQ), Julien Bect (L2S, RT-UQ), Emmanuel Vazquez (L2S, RT-UQ)
https://arxiv.org/abs/2506.13211

Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification
Given a multivariate function taking deterministic and uncertain inputs, we consider the problem of estimating a quantile set: a set of deterministic inputs for which the probability that the output belongs to a specific region remains below a given threshold. To solve this problem in the context of expensive-to-evaluate black-box functions, we propose a Bayesian active learning strategy based on Gaussian process modeling. The strategy is driven by a novel sampling criterion, which belongs to a…

Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
Seongmin Kim, In-Saeng Suh
https://arxiv.org/abs/2506.03329

Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-…

Replaced article(s) found for cond-mat.soft. https://arxiv.org/list/cond-mat.soft/new
[1/1]:
- Inferring activity from the flow field around active colloidal particles using deep learning
Aditya Mohapatra, Aditya Kumar, Mayurakshi Deb, Siddharth Dhomkar, Rajesh Singh
…

This https://arxiv.org/abs/2412.20925 has been replaced.
link: https://scholar.google.com/scholar?q=a

Active Learning with Variational Quantum Circuits for Quantum Process Tomography
Quantum process tomography (QPT) is a fundamental tool for fully characterizing quantum systems. It relies on querying a set of quantum states as input to the quantum process. Previous QPT methods typically employ a straightforward strategy for randomly selecting quantum states, overlooking differences in informativeness among them. In this work, we propose a general active learning (AL) framework that adaptively selects the most informative subset of quantum states for reconstruction. We desig…

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Ning Zhu, Xiaochuan Ma, Shaoting Zhang, Guotai Wang
https://arxiv.org/abs/2508.03441

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model performance under a limited annotation budget in medical image analysis. Most existing CSAL methods rely on Self-Supervised Learning (SSL) on the target dataset for feature extraction, which is inefficient and limited by insufficient feature representation. Recently, pre-trained Foundation Models (FMs) have shown powerful f…

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
Nitik Bhatia, Patrick Rinke, Ondrej Krejci
https://arxiv.org/abs/2506.13486

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in situ. However, interpreting IR spectra often requires high-fidelity simulations, such as density functional theory based ab-initio molecular dynamics, which are computationally expensive and therefore limited in the tractable system size and complexity. In this work, we present a novel active learning-based framework…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Active Learning For Repairable Hardware Systems With Partial Coverage
Michael Potter, Beyza Kalkanl{\i}, Deniz Erdo\u{g}mu\c{s}, Michael Everett

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/3]:
- Sophisticated Learning: A novel algorithm for active learning during model-based planning
Hodson, Bassett, van Hoof, Rosman, Solms, Shock, Smith

Identifying Radio Active Galactic Nuclei with Machine Learning and Large-Area Surveys
Xu-Liang Fan, Jie Li
https://arxiv.org/abs/2508.01596 https://arxiv.o…

Identifying Radio Active Galactic Nuclei with Machine Learning and Large-Area Surveys
Context. Active galactic nuclei (AGNs) and star forming galaxies (SFGs) are the primary sources of extragalactic radio sky. But it is difficult to distinguish the radio emission produced by AGNs from that by SFGs, especially when the radio sources are faint. Best et al. (2023) classified the radio sources in LoTSS Deep Fields DR1 through multiwavelength SED fitting. With the classification results of them, we perform a supervised machine learning to distinguish radio AGNs and radio SFGs. Aims…

Neuromorphic Online Clustering and Its Application to Spike Sorting
James E. Smith
https://arxiv.org/abs/2506.12555 https://arxiv.org…

Neuromorphic Online Clustering and Its Application to Spike Sorting
Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. …

Replaced article(s) found for cs.FL. https://arxiv.org/list/cs.FL/new
[1/1]:
- Active Learning of Mealy Machines with Timers
V\'eronique Bruy\`ere, Bharat Garhewal, Guillermo A. P\'erez, Ga\"etan Staquet, Frits W. Vaandrager

AL-SPCE -- Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning
A. Pires, M. Moustapha, S. Marelli, B. Sudret
https://arxiv.org/abs/2507.04553

AL-SPCE -- Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning
Reliability analysis typically relies on deterministic simulators, which yield repeatable outputs for identical inputs. However, many real-world systems display intrinsic randomness, requiring stochastic simulators whose outputs are random variables. This inherent variability must be accounted for in reliability analysis. While Monte Carlo methods can handle this, their high computational cost is often prohibitive. To address this, stochastic emulators have emerged as efficient surrogate models…

Learning general pair interactions between self-propelled particles
J\'er\^ome Hem, Alexis Poncet, Pierre Ronceray, Daiki Nishiguchi, Vincent D\'emery
https://arxiv.org/abs/2507.13667

Learning general pair interactions between self-propelled particles
Synthetic active matter systems, such as active colloids, often have complex interactions, which can be of hydrodynamic, chemical or electrostatic origin and cannot be computed from first principles. Here, we use Stochastic Force Inference to learn general pair interactions, including transverse forces and torques, between self-propelled Janus particles from experimental trajectories. We use data from two experiments: one where the particles flock, and one where the system remains disordered. T…

Critical dynamics governs deep learning
Simon Vock, Christian Meisel
https://arxiv.org/abs/2507.08527 https://arxiv.org/pdf/2507.0852…

Critical dynamics governs deep learning
Artificial intelligence has advanced rapidly through larger and deeper neural networks, yet fundamental questions remain about how to optimize network dynamics for performance and adaptability. This study shows that deep neural networks (DNNs), like biological brains, perform optimally when operating near a critical phase transition - poised between active and inactive dynamics. Drawing from physics and neuroscience, we demonstrate that criticality provides a unifying principle linking structur…

Learn Like Feynman: Developing and Testing an AI-Driven Feynman Bot
Akshaya Rajesh, Sumbul Khan
https://arxiv.org/abs/2506.09055 https://

Learn Like Feynman: Developing and Testing an AI-Driven Feynman Bot
The Feynman learning technique is an active learning strategy that helps learners simplify complex information through student-led teaching and discussion. In this paper, we present the development and usability testing of the Feynman Bot, which uses the Feynman technique to assist self-regulated learners who lack peer or instructor support. The Bot embodies the Feynman learning technique by encouraging learners to discuss their lecture material in a question-answer-driven discussion format. Th…

CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning
Yi-Shiuan Tung, Bradley Hayes, Alessandro Roncone
https://arxiv.org/abs/2507.05458

CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning
For effective real-world deployment, robots should adapt to human preferences, such as balancing distance, time, and safety in delivery routing. Active preference learning (APL) learns human reward functions by presenting trajectories for ranking. However, existing methods often struggle to explore the full trajectory space and fail to identify informative queries, particularly in long-horizon tasks. We propose CRED, a trajectory generation method for APL that improves reward estimation by join…

AFABench: A Generic Framework for Benchmarking Active Feature Acquisition
Valter Sch\"utz, Han Wu, Reza Rezvan, Linus Aronsson, Morteza Haghir Chehreghani
https://arxiv.org/abs/2508.14734

AFABench: A Generic Framework for Benchmarking Active Feature Acquisition
In many real-world scenarios, acquiring all features of a data instance can be expensive or impractical due to monetary cost, latency, or privacy concerns. Active Feature Acquisition (AFA) addresses this challenge by dynamically selecting a subset of informative features for each data instance, trading predictive performance against acquisition cost. While numerous methods have been proposed for AFA, ranging from greedy information-theoretic strategies to non-myopic reinforcement learning appro…

DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning
Kunal Swami, Debtanu Gupta, Amrit Kumar Muduli, Chirag Jaiswal, Pankaj Kumar Bajpai
https://arxiv.org/abs/2506.14709

DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning
Depth estimation is crucial for intelligent systems, enabling applications from autonomous navigation to augmented reality. While traditional stereo and active depth sensors have limitations in cost, power, and robustness, dual-pixel (DP) technology, ubiquitous in modern cameras, offers a compelling alternative. This paper introduces DiFuse-Net, a novel modality decoupled network design for disentangled RGB and DP based depth estimation. DiFuse-Net features a window bi-directional parallax atte…

Deep Learning Based Antenna Selection Technique for RIS-Empowered RQSM System
Burak Ahmet Ozden, Fatih Cogen, Erdogan Aydin
https://arxiv.org/abs/2507.05071

Deep Learning Based Antenna Selection Technique for RIS-Empowered RQSM System
Reconfigurable intelligent surface (RIS) technology has attracted considerable interest due to its ability to control wireless propagation with minimal power usage. Receive quadrature spatial modulation (RQSM) scheme transmits data bits in both in-phase ($I$) and quadrature ($Q$) channels, doubling the number of active receive antenna indices and improving spectral efficiency compared to the traditional receive spatial modulation (RSM) technique. Also, capacity-optimized antenna selection (COAS…

Random Walk Learning and the Pac-Man Attack
Xingran Chen, Parimal Parag, Rohit Bhagat, Zonghong Liu, Salim El Rouayheb
https://arxiv.org/abs/2508.05663 https://

Random Walk Learning and the Pac-Man Attack
Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing applications in decentralized learning. However, their reliance on local interactions makes them inherently vulnerable to malicious behavior. In this work, we investigate an adversarial threat that we term the ``Pac-Man'' attack, in which a malicious node probabilistically terminates any RW that visits it. This stealthy behavior gradually eliminates active RW…

BLIPs: Bayesian Learned Interatomic Potentials
Dario Coscia, Pim de Haan, Max Welling
https://arxiv.org/abs/2508.14022 https://arxiv.org/pdf/2508.14022

BLIPs: Bayesian Learned Interatomic Potentials
Machine Learning Interatomic Potentials (MLIPs) are becoming a central tool in simulation-based chemistry. However, like most deep learning models, MLIPs struggle to make accurate predictions on out-of-distribution data or when trained in a data-scarce regime, both common scenarios in simulation-based chemistry. Moreover, MLIPs do not provide uncertainty estimates by construction, which are fundamental to guide active learning pipelines and to ensure the accuracy of simulation results compared …

Manipulate-to-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors
Yuying Zhang, Joni Pajarinen
https://arxiv.org/abs/2508.13151 https://

Manipulate-to-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors
Mobile manipulation in dynamic environments is challenging due to movable obstacles blocking the robot's path. Traditional methods, which treat navigation and manipulation as separate tasks, often fail in such 'manipulate-to-navigate' scenarios, as obstacles must be removed before navigation. In these cases, active interaction with the environment is required to clear obstacles while ensuring sufficient space for movement. To address the manipulate-to-navigate problem, we propose a reinforcemen…

Replaced article(s) found for cs.FL. https://arxiv.org/list/cs.FL/new
[1/1]:
- Active Learning of Deterministic Transducers with Outputs in Arbitrary Monoids
Quentin Aristote (Universit\'e Paris Cit\'e, CNRS, Inria, IRIF, Paris, France)

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning
Yao Lu, Hongyu Gao, Zhuangzhi Chen, Dongwei Xu, Yun Lin, Qi Xuan, Guan Gui
https://arxiv.org/abs/2507.12011

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning
Although deep neural networks have made remarkable achievements in the field of automatic modulation recognition (AMR), these models often require a large amount of labeled data for training. However, in many practical scenarios, the available target domain data is scarce and difficult to meet the needs of model training. The most direct way is to collect data manually and perform expert annotation, but the high time and labor costs are unbearable. Another common method is data augmentation. Al…

HAC-LOCO: Learning Hierarchical Active Compliance Control for Quadruped Locomotion under Continuous External Disturbances
Xiang Zhou, Xinyu Zhang, Qingrui Zhang
https://arxiv.org/abs/2507.02447

HAC-LOCO: Learning Hierarchical Active Compliance Control for Quadruped Locomotion under Continuous External Disturbances
Despite recent remarkable achievements in quadruped control, it remains challenging to ensure robust and compliant locomotion in the presence of unforeseen external disturbances. Existing methods prioritize locomotion robustness over compliance, often leading to stiff, high-frequency motions, and energy inefficiency. This paper, therefore, presents a two-stage hierarchical learning framework that can learn to take active reactions to external force disturbances based on force estimation. In the…

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
Matthew C. Kuner, Aaron D. Kaplan, Kristin A. Persson, Mark Asta, Daryl C. Chrzan
https://arxiv.org/abs/2507.05559

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibri…

This https://arxiv.org/abs/2505.18924 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning
We present a novel active learning framework for 3D point cloud semantic segmentation that, for the first time, integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection. Unlike prior methods that treat labels as flat and independent, our approach leverages LLM prompting to automatically generate multi-level semantic taxonomies and introduces a recursive uncertainty projection mechanism that propagates uncertainty across hier…

Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow
Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband
https://arxiv.org/abs/2508.16403

Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow
Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due to highly nonlinear, layout-sensitive behavior and the high computational cost of traditional simulation tools. Existing machine learning (ML) surrogates often require large datasets to generalize across various topologies or to accurately model skewed and multi-modal performance metrics. In this work, a lightweight, data-efficient, and topology-awar…

Self driving algorithm for an active four wheel drive racecar
Gergely Bari, Laszlo Palkovics
https://arxiv.org/abs/2506.06077 https://

Self driving algorithm for an active four wheel drive racecar
Controlling autonomous vehicles at their handling limits is a significant challenge, particularly for electric vehicles with active four wheel drive (A4WD) systems offering independent wheel torque control. While traditional Vehicle Dynamics Control (VDC) methods use complex physics-based models, this study explores Deep Reinforcement Learning (DRL) to develop a unified, high-performance controller. We employ the Proximal Policy Optimization (PPO) algorithm to train an agent for optimal lap tim…

Learning to Explore: An In-Context Learning Approach for Pure Exploration
Alessio Russo, Ryan Welch, Aldo Pacchiano
https://arxiv.org/abs/2506.01876 https:…

Learning to Explore: An In-Context Learning Approach for Pure Exploration
In this work, we study the active sequential hypothesis testing problem, also known as pure exploration, where the goal is to actively control a data collection process to efficiently identify the correct hypothesis underlying a decision problem. While relevant across multiple domains, devising adaptive exploration strategies remains challenging, particularly due to difficulties in encoding appropriate inductive biases. Existing Reinforcement Learning (RL)-based methods often underperform when …

Auto-optimization of Energy Generation for Wave Energy Converters with Active Learning
Siyang Tang, Wen-Hua Chen, Cunjia Liu
https://arxiv.org/abs/2507.01727

Auto-optimization of Energy Generation for Wave Energy Converters with Active Learning
This paper presents an auto-optimization control framework for wave energy converters (WECs) to maximize energy generation under unknown and changing ocean conditions. The proposed control framework consists of two levels. The high-level controller operating at a longer time scale aims to maximize the average energy generation over several wave periods. The generated Power Take-Off (PTO) profile as the reference for the low-level physical system to follow. The new auto-optimization process leve…

Learning to See and Act: Task-Aware View Planning for Robotic Manipulation
Yongjie Bai, Zhouxia Wang, Yang Liu, Weixing Chen, Ziliang Chen, Mingtong Dai, Yongsen Zheng, Lingbo Liu, Guanbin Li, Liang Lin
https://arxiv.org/abs/2508.05186

Learning to See and Act: Task-Aware View Planning for Robotic Manipulation
Recent vision-language-action (VLA) models for multi-task robotic manipulation commonly rely on static viewpoints and shared visual encoders, which limit 3D perception and cause task interference, hindering robustness and generalization. In this work, we propose Task-Aware View Planning (TAVP), a framework designed to overcome these challenges by integrating active view planning with task-specific representation learning. TAVP employs an efficient exploration policy, accelerated by a novel pseu…

This https://arxiv.org/abs/2411.14608 has been replaced.
initial toot: https://mastoxiv.page/@a…

Toward machine learning interatomic potentials for modeling uranium mononitride
Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce …

This https://arxiv.org/abs/2505.14049 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification
The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they often suffer from concept leakages, where unintended information within soft concept representations undermines both interpretability and generalizability. Moreover, most concept-based models focus solely on local explanations (instance-level), neglecting the global decision logic (dataset-level). To address these li…

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
Deqing Song, Weimin Yang, Maryam Rezayati, Hans Wernher van de Venn
https://arxiv.org/abs/2508.12435

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors. We evaluated various convolutional neural network (CNN) architectures and collected two datasets to study the impact of data representation and model architecture on the recognition accuracy. Our results show that spectrogram-based representatio…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/8]:
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach

NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
Chengbing Chen, Yutong Li, Rui Zhao, Zhoulin Liu, Zheyong Fan, Gang Tang, Zhiyong Wang
https://arxiv.org/abs/2506.01868

NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Constructing high-quality training datasets is crucial for developing accurate NEP models. However, the preparation and screening of NEP training datasets remain a bottleneck for broader applications due to their time-consuming, labor-intensive, and resource-intensive nature. In this work, we have developed NepTrain and N…

Online Learning for Vibration Suppression in Physical Robot Interaction using Power Tools
Gokhan Solak, Arash Ajoudani
https://arxiv.org/abs/2508.03559 https://

Online Learning for Vibration Suppression in Physical Robot Interaction using Power Tools
Vibration suppression is an important capability for collaborative robots deployed in challenging environments such as construction sites. We study the active suppression of vibration caused by external sources such as power tools. We adopt the band-limited multiple Fourier linear combiner (BMFLC) algorithm to learn the vibration online and counter it by feedforward force control. We propose the damped BMFLC method, extending BMFLC with a novel adaptive step-size approach that improves the conv…

TLE-Based A2C Agent for Terrestrial Coverage Orbital Path Planning
Anantha Narayanan, Battu Bhanu Teja, Pruthwik Mishra
https://arxiv.org/abs/2508.10872 https://

TLE-Based A2C Agent for Terrestrial Coverage Orbital Path Planning
The increasing congestion of Low Earth Orbit (LEO) poses persistent challenges to the efficient deployment and safe operation of Earth observation satellites. Mission planners must now account not only for mission-specific requirements but also for the increasing collision risk with active satellites and space debris. This work presents a reinforcement learning framework using the Advantage Actor-Critic (A2C) algorithm to optimize satellite orbital parameters for precise terrestrial coverage wi…

WoMAP: World Models For Embodied Open-Vocabulary Object Localization
Tenny Yin, Zhiting Mei, Tao Sun, Lihan Zha, Emily Zhou, Jeremy Bao, Miyu Yamane, Ola Shorinwa, Anirudha Majumdar
https://arxiv.org/abs/2506.01600

WoMAP: World Models For Embodied Open-Vocabulary Object Localization
Language-instructed active object localization is a critical challenge for robots, requiring efficient exploration of partially observable environments. However, state-of-the-art approaches either struggle to generalize beyond demonstration datasets (e.g., imitation learning methods) or fail to generate physically grounded actions (e.g., VLMs). To address these limitations, we introduce WoMAP (World Models for Active Perception): a recipe for training open-vocabulary object localization policie…