id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.18170 | Continually Evolved Multimodal Foundation Models for Cancer Prognosis | [
"cs.LG"
] | Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. To enhance prediction accuracy, previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. However, existing approaches f... | {
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2501.18174 | Advancing Personalized Federated Learning: Integrative Approaches with
AI for Enhanced Privacy and Customization | [
"cs.LG",
"eess.SP"
] | In the age of data-driven decision making, preserving privacy while providing personalized experiences has become paramount. Personalized Federated Learning (PFL) offers a promising framework by decentralizing the learning process, thus ensuring data privacy and reducing reliance on centralized data repositories. Howev... | {
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2501.18177 | Investigating Tax Evasion Emergence Using Dual Large Language Model and
Deep Reinforcement Learning Powered Agent-based Simulation | [
"cs.IR",
"cs.CY",
"cs.MA"
] | Tax evasion, usually the largest component of an informal economy, is a persistent challenge over history with significant socio-economic implications. Many socio-economic studies investigate its dynamics, including influencing factors, the role and influence of taxation policies, and the prediction of the tax evasion ... | {
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2501.18178 | Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte
Carlo | [
"eess.SP",
"cs.LG",
"stat.ML"
] | This paper considers the problem of estimating chirp parameters from a noisy mixture of chirps. While a rich body of work exists in this area, challenges remain when extending these techniques to chirps of higher order polynomials. We formulate this as a non-convex optimization problem and propose a modified Langevin M... | {
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2501.18179 | Tunable Multilayer Surface Plasmon Resonance Biosensor for Trace-Level
Toxin Detection | [
"eess.SY",
"cs.SY"
] | This paper presents a comprehensive study on a novel multilayer surface plasmon resonance (SPR) biosensor designed for detecting trace-level toxins in liquid samples with exceptional precision and efficiency. Leveraging the Kretschmann configuration, the proposed design integrates advanced two-dimensional materials, in... | {
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2501.18183 | Decentralized Projection-free Online Upper-Linearizable Optimization
with Applications to DR-Submodular Optimization | [
"math.OC",
"cs.CC",
"cs.LG",
"stat.ML"
] | We introduce a novel framework for decentralized projection-free optimization, extending projection-free methods to a broader class of upper-linearizable functions. Our approach leverages decentralized optimization techniques with the flexibility of upper-linearizable function frameworks, effectively generalizing tradi... | {
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2501.18184 | Genetic Algorithm with Border Trades (GAB) | [
"cs.LG",
"cs.NE",
"stat.CO"
] | This paper introduces a novel approach to improving Genetic Algorithms (GA) in large or complex problem spaces by incorporating new chromosome patterns in the breeding process through border trade activities. These strategies increase chromosome diversity, preventing premature convergence and enhancing the GA's ability... | {
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2501.18187 | In-Context Learning of Polynomial Kernel Regression in Transformers with
GLU Layers | [
"cs.LG",
"cs.AI"
] | Transformer-based models have demonstrated remarkable ability in in-context learning (ICL), where they can adapt to unseen tasks from a prompt with a few examples, without requiring parameter updates. Recent research has provided insight into how linear Transformers can perform ICL by implementing gradient descent esti... | {
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2501.18189 | Neural Network Modeling of Microstructure Complexity Using Digital
Libraries | [
"cs.LG",
"cond-mat.mtrl-sci",
"cs.CE",
"nlin.PS",
"physics.comp-ph"
] | Microstructure evolution in matter is often modeled numerically using field or level-set solvers, mirroring the dual representation of spatiotemporal complexity in terms of pixel or voxel data, and geometrical forms in vector graphics. Motivated by this analog, as well as the structural and event-driven nature of artif... | {
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2501.18190 | Economic Rationality under Specialization: Evidence of Decision Bias in
AI Agents | [
"cs.AI"
] | In the study by Chen et al. (2023) [01], the large language model GPT demonstrated economic rationality comparable to or exceeding the average human level in tasks such as budget allocation and risk preference. Building on this finding, this paper further incorporates specialized agents, such as biotechnology experts a... | {
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2501.18192 | Machine Learning Fairness for Depression Detection using EEG Data | [
"cs.CV",
"cs.LG",
"eess.SP"
] | This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We conduct experiments using different deep learning architectures such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Uni... | {
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2501.18196 | GDformer: Going Beyond Subsequence Isolation for Multivariate Time
Series Anomaly Detection | [
"cs.LG"
] | Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion without accessing the anomaly points. The existing methods are mainly based on reconstruction error or association divergence, which are both confined to isolated subsequenc... | {
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2501.18197 | Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting
Their Limitations | [
"cs.LG",
"cs.DB"
] | In this work, we dive into the fundamental challenges of evaluating Text2SQL solutions and highlight potential failure causes and the potential risks of relying on aggregate metrics in existing benchmarks. We identify two largely unaddressed limitations in current open benchmarks: (1) data quality issues in the evaluat... | {
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2501.18199 | HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation | [
"cs.LG",
"cs.AI"
] | This paper introduces the Hierarchical Kolmogorov-Arnold Network (HKAN), a novel network architecture that offers a competitive alternative to the recently proposed Kolmogorov-Arnold Network (KAN). Unlike KAN, which relies on backpropagation, HKAN adopts a randomized learning approach, where the parameters of its basis... | {
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2501.18200 | Characterization of Permanent Magnet Synchronous Machines based on
semi-analytic model reduction for drive cycle analysis | [
"cs.CE"
] | The characterization of an interior permanent magnet synchronous machine (IPMSM) requires numerical analysis of the nonlinear magnetic motor model in different load conditions. To obtain the case-specific best machine behavior, a strategy for the determination of stator input current amplitude and angle is employed for... | {
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2501.18201 | Neural Operator based Reinforcement Learning for Control of first-order
PDEs with Spatially-Varying State Delay | [
"cs.AI",
"cs.SY",
"eess.SY"
] | Control of distributed parameter systems affected by delays is a challenging task, particularly when the delays depend on spatial variables. The idea of integrating analytical control theory with learning-based control within a unified control scheme is becoming increasingly promising and advantageous. In this paper, w... | {
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2501.18202 | On Scaling Neurosymbolic Programming through Guided Logical Inference | [
"cs.AI"
] | Probabilistic neurosymbolic learning seeks to integrate neural networks with symbolic programming. Many state-of-the-art systems rely on a reduction to the Probabilistic Weighted Model Counting Problem (PWMC), which requires computing a Boolean formula called the logical provenance.However, PWMC is \\#P-hard, and the n... | {
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2501.18203 | Joint Design and Pricing of Extended Warranties for Multiple Automobiles
with Different Price Bands | [
"math.OC",
"cs.SY",
"eess.SY"
] | Extended warranties (EWs) are significant source of revenue for capital-intensive products like automobiles. Such products consist of multiple subsystems, providing flexibility in EW customization, for example, bundling a tailored set of subsystems in an EW contract. This, in turn, enables the creation of a service men... | {
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2501.18205 | Contextually Structured Token Dependency Encoding for Large Language
Models | [
"cs.CL"
] | Token representation strategies within large-scale neural architectures often rely on contextually refined embeddings, yet conventional approaches seldom encode structured relationships explicitly within token interactions. Self-attention mechanisms effectively capture dynamic contextual dependencies, but their relianc... | {
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2501.18210 | Hashtag Re-Appropriation for Audience Control on Recommendation-Driven
Social Media Xiaohongshu (rednote) | [
"cs.HC",
"cs.CY",
"cs.IR",
"cs.SI"
] | Algorithms have played a central role in personalized recommendations on social media. However, they also present significant obstacles for content creators trying to predict and manage their audience reach. This issue is particularly challenging for marginalized groups seeking to maintain safe spaces. Our study explor... | {
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2501.18216 | Behavior Modeling Space Reconstruction for E-Commerce Search | [
"cs.IR"
] | Delivering superior search services is crucial for enhancing customer experience and driving revenue growth. Conventionally, search systems model user behaviors by combining user preference and query item relevance statically, often through a fixed logical 'and' relationship. This paper reexamines existing approaches t... | {
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2501.18219 | Revisiting $\Psi$DONet: microlocally inspired filters for
incomplete-data tomographic reconstructions | [
"math.OC",
"cs.CV",
"cs.LG"
] | In this paper, we revisit a supervised learning approach based on unrolling, known as $\Psi$DONet, by providing a deeper microlocal interpretation for its theoretical analysis, and extending its study to the case of sparse-angle tomography. Furthermore, we refine the implementation of the original $\Psi$DONet consideri... | {
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2501.18220 | On-Line Learning for Planning and Control of Underactuated Robots with
Uncertain Dynamics | [
"cs.RO"
] | We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of the algorithm makes us... | {
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2501.18223 | Exploring Large Protein Language Models in Constrained Evaluation
Scenarios within the FLIP Benchmark | [
"cs.LG",
"cs.AI"
] | In this study, we expand upon the FLIP benchmark-designed for evaluating protein fitness prediction models in small, specialized prediction tasks-by assessing the performance of state-of-the-art large protein language models, including ESM-2 and SaProt on the FLIP dataset. Unlike larger, more diverse benchmarks such as... | {
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2501.18229 | GPD: Guided Polynomial Diffusion for Motion Planning | [
"cs.RO"
] | Diffusion-based motion planners are becoming popular due to their well-established performance improvements, stemming from sample diversity and the ease of incorporating new constraints directly during inference. However, a primary limitation of the diffusion process is the requirement for a substantial number of denoi... | {
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2501.18232 | Free-T2M: Frequency Enhanced Text-to-Motion Diffusion Model With
Consistency Loss | [
"cs.CV"
] | Rapid progress in text-to-motion generation has been largely driven by diffusion models. However, existing methods focus solely on temporal modeling, thereby overlooking frequency-domain analysis. We identify two key phases in motion denoising: the **semantic planning stage** and the **fine-grained improving stage**. T... | {
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2501.18236 | RIS-assisted Physical Layer Security | [
"cs.IT",
"eess.SP",
"math.IT"
] | We propose a reconfigurable intelligent surface (RIS)-assisted wiretap channel, where the RIS is strategically deployed to provide a spatial separation to the transmitter, and orthogonal combiners are employed at the legitimate receiver to extract the data streams from the direct and RIS-assisted links. Then we derive ... | {
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2501.18237 | Arbitrary Data as Images: Fusion of Patient Data Across Modalities and
Irregular Intervals with Vision Transformers | [
"cs.CV",
"cs.AI"
] | A patient undergoes multiple examinations in each hospital stay, where each provides different facets of the health status. These assessments include temporal data with varying sampling rates, discrete single-point measurements, therapeutic interventions such as medication administration, and images. While physicians a... | {
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2501.18243 | Statistical multi-metric evaluation and visualization of LLM system
predictive performance | [
"stat.AP",
"cs.CL",
"cs.LG"
] | The evaluation of generative or discriminative large language model (LLM)-based systems is often a complex multi-dimensional problem. Typically, a set of system configuration alternatives are evaluated on one or more benchmark datasets, each with one or more evaluation metrics, which may differ between datasets. We oft... | {
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2501.18246 | Ground Awareness in Deep Learning for Large Outdoor Point Cloud
Segmentation | [
"cs.CV"
] | This paper presents an analysis of utilizing elevation data to aid outdoor point cloud semantic segmentation through existing machine-learning networks in remote sensing, specifically in urban, built-up areas. In dense outdoor point clouds, the receptive field of a machine learning model may be too small to accurately ... | {
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2501.18250 | Dynamic Model Fine-Tuning For Extreme MIMO CSI Compression | [
"cs.IT",
"eess.SP",
"math.IT"
] | Efficient channel state information (CSI) compression is crucial in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems due to excessive feedback overhead. Recently, deep learning-based compression techniques have demonstrated superior performance across various data types, includin... | {
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2501.18251 | How to Select Datapoints for Efficient Human Evaluation of NLG Models? | [
"cs.CL"
] | Human evaluation is the gold-standard for evaluating text generation models. It is also expensive, and to fit budgetary constraints, a random subset of the test data is often chosen in practice. The randomly selected data may not accurately represent test performance, making this approach economically inefficient for m... | {
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2501.18258 | PDE-DKL: PDE-constrained deep kernel learning in high dimensionality | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Many physics-informed machine learning methods for PDE-based problems rely on Gaussian processes (GPs) or neural networks (NNs). However, both face limitations when data are scarce and the dimensionality is high. Although GPs are known for their robust uncertainty quantification in low-dimensional settings, their compu... | {
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2501.18265 | Collecting Cost-Effective, High-Quality Truthfulness Assessments with
LLM Summarized Evidence | [
"cs.IR",
"cs.CL",
"cs.HC"
] | With the degradation of guardrails against mis- and disinformation online, it is more critical than ever to be able to effectively combat it. In this paper, we explore the efficiency and effectiveness of using crowd-sourced truthfulness assessments based on condensed, large language model (LLM) generated summaries of o... | {
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2501.18268 | Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data
Acquisition | [
"cs.LG"
] | To generate accurate and reliable predictions, modern AI systems need to combine data from multiple modalities, such as text, images, audio, spreadsheets, and time series. Multi-modal data introduces new opportunities and challenges for disentangling uncertainty: it is commonly assumed in the machine learning community... | {
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2501.18269 | MAMS: Model-Agnostic Module Selection Framework for Video Captioning | [
"cs.CV",
"cs.AI"
] | Multi-modal transformers are rapidly gaining attention in video captioning tasks. Existing multi-modal video captioning methods typically extract a fixed number of frames, which raises critical challenges. When a limited number of frames are extracted, important frames with essential information for caption generation ... | {
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2501.18270 | The iToBoS dataset: skin region images extracted from 3D total body
photographs for lesion detection | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Artificial intelligence has significantly advanced skin cancer diagnosis by enabling rapid and accurate detection of malignant lesions. In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the center of the image. While these lesion-centric datasets have been fun... | {
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2501.18271 | Pre-Trained Vision-Language Model Selection and Reuse for Downstream
Tasks | [
"cs.LG",
"cs.AI"
] | Pre-trained Vision-Language Models (VLMs) are becoming increasingly popular across various visual tasks, and several open-sourced VLM variants have been released. However, selecting the best-performing pre-trained VLM for a specific downstream task is challenging since no single VLM can achieve promising performance on... | {
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2501.18277 | Sebra: Debiasing Through Self-Guided Bias Ranking | [
"cs.LG"
] | Ranking samples by fine-grained estimates of spuriosity (the degree to which spurious cues are present) has recently been shown to significantly benefit bias mitigation, over the traditional binary biased-\textit{vs}-unbiased partitioning of train sets. However, this spuriosity ranking comes with the requirement of hum... | {
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2501.18278 | ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation
Learning via Biochemical Reaction Networks | [
"cs.LG"
] | The challenge in computational biology and drug discovery lies in creating comprehensive representations of proteins and molecules that capture their intrinsic properties and interactions. Traditional methods often focus on unimodal data, such as protein sequences or molecular structures, limiting their ability to capt... | {
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2501.18280 | Jailbreaking LLMs' Safeguard with Universal Magic Words for Text
Embedding Models | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.NE"
] | The security issue of large language models (LLMs) has gained significant attention recently, with various defense mechanisms developed to prevent harmful outputs, among which safeguards based on text embedding models serve as a fundamental defense. Through testing, we discover that the distribution of text embedding m... | {
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2501.18282 | Leveraging Sparsity for Sample-Efficient Preference Learning: A
Theoretical Perspective | [
"cs.LG"
] | This paper considers the sample-efficiency of preference learning, which models and predicts human choices based on comparative judgments. The minimax optimal estimation rate $\Theta(d/n)$ in traditional estimation theory requires that the number of samples $n$ scales linearly with the dimensionality of the feature spa... | {
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2501.18283 | Random Feature Representation Boosting | [
"stat.ML",
"cs.LG"
] | We introduce Random Feature Representation Boosting (RFRBoost), a novel method for constructing deep residual random feature neural networks (RFNNs) using boosting theory. RFRBoost uses random features at each layer to learn the functional gradient of the network representation, enhancing performance while preserving t... | {
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2501.18287 | Mining for Species, Locations, Habitats, and Ecosystems from Scientific
Papers in Invasion Biology: A Large-Scale Exploratory Study with Large
Language Models | [
"cs.CL",
"cs.AI",
"cs.DL"
] | This paper presents an exploratory study that harnesses the capabilities of large language models (LLMs) to mine key ecological entities from invasion biology literature. Specifically, we focus on extracting species names, their locations, associated habitats, and ecosystems, information that is critical for understand... | {
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2501.18288 | Normalizing flows for SU($N$) gauge theories employing singular value
decomposition | [
"hep-lat",
"cs.LG"
] | We present a progress report on the use of normalizing flows for generating gauge field configurations in pure SU(N) gauge theories. We discuss how the singular value decomposition can be used to construct gauge-invariant quantities, which serve as the building blocks for designing gauge-equivariant transformations of ... | {
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2501.18291 | CueTip: An Interactive and Explainable Physics-aware Pool Assistant | [
"cs.AI",
"cs.HC"
] | We present an interactive and explainable automated coaching assistant called CueTip for a variant of pool/billiards. CueTip's novelty lies in its combination of three features: a natural-language interface, an ability to perform contextual, physics-aware reasoning, and that its explanations are rooted in a set of pred... | {
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2501.18292 | Citation Recommendation based on Argumentative Zoning of User Queries | [
"cs.IR",
"cs.CL",
"cs.DL"
] | Citation recommendation aims to locate the important papers for scholars to cite. When writing the citing sentences, the authors usually hold different citing intents, which are referred to citation function in citation analysis. Since argumentative zoning is to identify the argumentative and rhetorical structure in sc... | {
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2501.18294 | A Comprehensive Analysis on Machine Learning based Methods for Lung
Cancer Level Classification | [
"cs.CV",
"cs.AI"
] | Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer stages. A cautious analysis is performed to overcome overfitting issues in model per... | {
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2501.18296 | Extending the design space of ontologization practices: Using bCLEARer
as an example | [
"cs.AI"
] | Our aim in this paper is to outline how the design space for the ontologization process is richer than current practice would suggest. We point out that engineering processes as well as products need to be designed - and identify some components of the design. We investigate the possibility of designing a range of radi... | {
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2501.18298 | Update Estimation and Scheduling for Over-the-Air Federated Learning
with Energy Harvesting Devices | [
"cs.LG",
"cs.DC"
] | We study over-the-air (OTA) federated learning (FL) for energy harvesting devices with heterogeneous data distribution over wireless fading multiple access channel (MAC). To address the impact of low energy arrivals and data heterogeneity on global learning, we propose user scheduling strategies. Specifically, we devel... | {
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2501.18299 | Model-Free RL Agents Demonstrate System 1-Like Intentionality | [
"cs.AI"
] | This paper argues that model-free reinforcement learning (RL) agents, while lacking explicit planning mechanisms, exhibit behaviours that can be analogised to System 1 ("thinking fast") processes in human cognition. Unlike model-based RL agents, which operate akin to System 2 ("thinking slow") reasoning by leveraging i... | {
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2501.18308 | Zero Estimation Cost Strategy for Witsenhausen Counterexample with
Causal Encoder | [
"cs.IT",
"math.IT"
] | We propose a zero estimation cost (ZEC) scheme for causal-encoding noncausal-decoding vector-valued Witsenhausen counterexample based on the coordination coding result. In contrast to source coding, our goal is to communicate a controlled system state. The introduced ZEC scheme is a joint control-communication approach... | {
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2501.18309 | Knowledge in multi-robot systems: an interplay of dynamics, computation
and communication | [
"cs.LO",
"cs.DC",
"cs.RO"
] | We show that the hybrid systems perspective of distributed multi-robot systems is compatible with logical models of knowledge already used in distributed computing, and demonstrate its usefulness by deriving sufficient epistemic conditions for exploration and gathering robot tasks to be solvable. We provide a separatio... | {
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2501.18310 | Efficient Neural Theorem Proving via Fine-grained Proof Structure
Analysis | [
"cs.LG",
"cs.AI"
] | The synergy between deep learning models and traditional automation tools plays a pivotal role in developing robust neural theorem provers (NTPs). However, for proof synthesis with LLMs, previous work applies automation tools either only when the model explicitly calls the method, or only at a single granularity level,... | {
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2501.18313 | Simulation of microstructures and machine learning | [
"cs.CV"
] | Machine learning offers attractive solutions to challenging image processing tasks. Tedious development and parametrization of algorithmic solutions can be replaced by training a convolutional neural network or a random forest with a high potential to generalize. However, machine learning methods rely on huge amounts o... | {
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2501.18314 | AGAV-Rater: Adapting Large Multimodal Model for AI-Generated
Audio-Visual Quality Assessment | [
"cs.MM",
"cs.CV",
"cs.SD",
"eess.AS"
] | Many video-to-audio (VTA) methods have been proposed for dubbing silent AI-generated videos. An efficient quality assessment method for AI-generated audio-visual content (AGAV) is crucial for ensuring audio-visual quality. Existing audio-visual quality assessment methods struggle with unique distortions in AGAVs, such ... | {
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2501.18315 | Surface Defect Identification using Bayesian Filtering on a 3D Mesh | [
"cs.CV",
"cs.RO"
] | This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygon... | {
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2501.18318 | Estimating unknown dynamics and cost as a bilinear system with
Koopman-based Inverse Optimal Control | [
"eess.SY",
"cs.SY",
"math.DS"
] | In this work, we address the challenge of approximating unknown system dynamics and costs by representing them as a bilinear system using Koopman-based Inverse Optimal Control (IOC). Using optimal trajectories, we construct a bilinear control system in transformed state variables through a modified Extended Dynamic Mod... | {
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2501.18320 | Leveraging LLM Agents for Automated Optimization Modeling for SASP
Problems: A Graph-RAG based Approach | [
"cs.AI",
"eess.SP"
] | Automated optimization modeling (AOM) has evoked considerable interest with the rapid evolution of large language models (LLMs). Existing approaches predominantly rely on prompt engineering, utilizing meticulously designed expert response chains or structured guidance. However, prompt-based techniques have failed to pe... | {
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2501.18322 | A Unified Perspective on the Dynamics of Deep Transformers | [
"cs.LG",
"math.AP"
] | Transformers, which are state-of-the-art in most machine learning tasks, represent the data as sequences of vectors called tokens. This representation is then exploited by the attention function, which learns dependencies between tokens and is key to the success of Transformers. However, the iterative application of at... | {
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2501.18324 | A Video-grounded Dialogue Dataset and Metric for Event-driven Activities | [
"cs.CV",
"cs.CL"
] | This paper presents VDAct, a dataset for a Video-grounded Dialogue on Event-driven Activities, alongside VDEval, a session-based context evaluation metric specially designed for the task. Unlike existing datasets, VDAct includes longer and more complex video sequences that depict a variety of event-driven activities th... | {
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2501.18328 | CodeBrain: Impute Any Brain MRI via Instance-specific Scalar-quantized
Codes | [
"cs.CV",
"cs.AI"
] | MRI imputation aims to synthesize the missing modality from one or more available ones, which is highly desirable since it reduces scanning costs and delivers comprehensive MRI information to enhance clinical diagnosis. In this paper, we propose a unified model, CodeBrain, designed to adapt to various brain MRI imputat... | {
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2501.18331 | Stream-Based Monitoring of Algorithmic Fairness | [
"cs.LG",
"cs.LO",
"cs.SE"
] | Automatic decision and prediction systems are increasingly deployed in applications where they significantly impact the livelihood of people, such as for predicting the creditworthiness of loan applicants or the recidivism risk of defendants. These applications have given rise to a new class of algorithmic-fairness spe... | {
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2501.18337 | Unfaithful Probability Distributions in Binary Triple of Causality
Directed Acyclic Graph | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Faithfulness is the foundation of probability distribution and graph in causal discovery and causal inference. In this paper, several unfaithful probability distribution examples are constructed in three--vertices binary causality directed acyclic graph (DAG) structure, which are not faithful to causal DAGs described i... | {
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2501.18344 | Transfer Learning of Surrogate Models: Integrating Domain Warping and
Affine Transformations | [
"cs.LG",
"cs.AI"
] | Surrogate models provide efficient alternatives to computationally demanding real-world processes but often require large datasets for effective training. A promising solution to this limitation is the transfer of pre-trained surrogate models to new tasks. Previous studies have investigated the transfer of differentiab... | {
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2501.18350 | Joint Power and Spectrum Orchestration for D2D Semantic Communication
Underlying Energy-Efficient Cellular Networks | [
"eess.SY",
"cs.SY"
] | Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus naturally introducing a novel and practical network paradigm where cellular and device-to-device (D2D) SemCom approaches coexist. Nevertheless, the... | {
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2501.18351 | Dual-BEV Nav: Dual-layer BEV-based Heuristic Path Planning for Robotic
Navigation in Unstructured Outdoor Environments | [
"cs.RO"
] | Path planning with strong environmental adaptability plays a crucial role in robotic navigation in unstructured outdoor environments, especially in the case of low-quality location and map information. The path planning ability of a robot depends on the identification of the traversability of global and local ground ar... | {
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2501.18355 | Multilayered Intelligent Reflecting Surface for Long-Range Underwater
Acoustic Communication | [
"eess.AS",
"cs.SD",
"cs.SY",
"eess.SP",
"eess.SY"
] | This article introduces a multilayered acoustic reconfigurable intelligent surface (ML-ARIS) architecture designed for the next generation of underwater communications. ML-ARIS incorporates multiple layers of piezoelectric material in each acoustic reflector, with the load impedance of each layer independently adjustab... | {
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2501.18356 | State Stream Transformer (SST) : Emergent Metacognitive Behaviours
Through Latent State Persistence | [
"cs.LG",
"cs.AI",
"cs.CL"
] | We introduce the State Stream Transformer (SST), a novel LLM architecture that reveals emergent reasoning behaviours and capabilities latent in pretrained weights through addressing a fundamental limitation in traditional transformer models: the lack of latent computational continuity across autoregressive generations ... | {
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2501.18357 | Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A
Comprehensive Graph Representation Framework from Local to Global | [
"cs.LG"
] | Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in various graph representation learning tasks. However, most existing GNNs focus primarily on capturing local information through explicit graph convolution, often neglecting global message-passing. This limitation hinders the establishment of a c... | {
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2501.18359 | Contextual Online Decision Making with Infinite-Dimensional Functional
Regression | [
"stat.ML",
"cs.LG"
] | Contextual sequential decision-making problems play a crucial role in machine learning, encompassing a wide range of downstream applications such as bandits, sequential hypothesis testing and online risk control. These applications often require different statistical measures, including expectation, variance and quanti... | {
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2501.18361 | Video-based Surgical Tool-tip and Keypoint Tracking using Multi-frame
Context-driven Deep Learning Models | [
"cs.CV"
] | Automated tracking of surgical tool keypoints in robotic surgery videos is an essential task for various downstream use cases such as skill assessment, expertise assessment, and the delineation of safety zones. In recent years, the explosion of deep learning for vision applications has led to many works in surgical ins... | {
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2501.18362 | MedXpertQA: Benchmarking Expert-Level Medical Reasoning and
Understanding | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | We introduce MedXpertQA, a highly challenging and comprehensive benchmark to evaluate expert-level medical knowledge and advanced reasoning. MedXpertQA includes 4,460 questions spanning 17 specialties and 11 body systems. It includes two subsets, Text for text evaluation and MM for multimodal evaluation. Notably, MM in... | {
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2501.18363 | Robust Online Conformal Prediction under Uniform Label Noise | [
"cs.LG"
] | Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability. Recent work develops online conformal prediction methods that adaptively construct prediction sets to accommodate distribution shifts. However,... | {
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2501.18365 | RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against
Retrieval Defects | [
"cs.CL",
"cs.IR"
] | Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved from a knowledge base. However, its effectiveness is fundamentally constrained by the reliability of both the retriever and the knowledge base. In real-world scenarios, imperfections in these component... | {
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2501.18367 | A Learnable Multi-views Contrastive Framework with Reconstruction
Discrepancy for Medical Time-Series | [
"cs.LG",
"cs.AI"
] | In medical time series disease diagnosis, two key challenges are identified.First, the high annotation cost of medical data leads to overfitting in models trained on label-limited, single-center datasets. To address this, we propose incorporating external data from related tasks and leveraging AE-GAN to extract prior k... | {
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2501.18369 | A Cartesian Encoding Graph Neural Network for Crystal Structures
Property Prediction: Application to Thermal Ellipsoid Estimation | [
"cs.LG"
] | In diffraction-based crystal structure analysis, thermal ellipsoids, quantified via Anisotropic Displacement Parameters (ADPs), are critical yet challenging to determine. ADPs capture atomic vibrations, reflecting thermal and structural properties, but traditional computation is often expensive. This paper introduces C... | {
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2501.18373 | Function Encoders: A Principled Approach to Transfer Learning in Hilbert
Spaces | [
"cs.LG"
] | A central challenge in transfer learning is designing algorithms that can quickly adapt and generalize to new tasks without retraining. Yet, the conditions of when and how algorithms can effectively transfer to new tasks is poorly characterized. We introduce a geometric characterization of transfer in Hilbert spaces an... | {
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2501.18374 | Proofs for Folklore Theorems on the Radon-Nikodym Derivative | [
"cs.IT",
"math.HO",
"math.IT",
"math.ST",
"stat.ML",
"stat.TH"
] | Rigorous statements and formal proofs are presented for both foundational and advanced folklore theorems on the Radon-Nikodym derivative. The cases of product and marginal measures are carefully considered; and the hypothesis under which the statements hold are rigorously enumerated. | {
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2501.18376 | Cracks in concrete | [
"cs.CV",
"eess.IV",
"stat.AP"
] | Finding and properly segmenting cracks in images of concrete is a challenging task. Cracks are thin and rough and being air filled do yield a very weak contrast in 3D images obtained by computed tomography. Enhancing and segmenting dark lower-dimensional structures is already demanding. The heterogeneous concrete matri... | {
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2501.18377 | Using Read Promotion and Mixed Isolation Levels for Performant Yet
Serializable Execution of Transaction Programs | [
"cs.DB"
] | We propose a theory that can determine the lowest isolation level that can be allocated to each transaction program in an application in a mixed-isolation-level setting, to guarantee that all executions will be serializable and thus preserve all integrity constraints, even those that are not explicitly declared. This e... | {
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2501.18381 | Implicit Riemannian Optimism with Applications to Min-Max Problems | [
"math.OC",
"cs.LG"
] | We introduce a Riemannian optimistic online learning algorithm for Hadamard manifolds based on inexact implicit updates. Unlike prior work, our method can handle in-manifold constraints, and matches the best known regret bounds in the Euclidean setting with no dependence on geometric constants, like the minimum curvatu... | {
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2501.18382 | Rydberg Atomic Quantum Receivers for the Multi-User MIMO Uplink | [
"eess.SP",
"cs.IT",
"math.IT"
] | Rydberg atomic quantum receivers exhibit great potential in assisting classical wireless communications due to their outstanding advantages in detecting radio frequency signals. To realize this potential, we integrate a Rydberg atomic quantum receiver into a classical multi-user multiple-input multiple-output (MIMO) sc... | {
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2501.18385 | Performance guarantees for optimization-based state estimation using
turnpike properties | [
"math.OC",
"cs.SY",
"eess.SY"
] | In this paper, we develop novel accuracy and performance guarantees for optimal state estimation of general nonlinear systems (in particular, moving horizon estimation, MHE). Our results rely on a turnpike property of the optimal state estimation problem, which essentially states that the omniscient infinite-horizon so... | {
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2501.18388 | Improved Replicable Boosting with Majority-of-Majorities | [
"cs.LG"
] | We introduce a new replicable boosting algorithm which significantly improves the sample complexity compared to previous algorithms. The algorithm works by doing two layers of majority voting, using an improved version of the replicable boosting algorithm introduced by Impagliazzo et al. [2022] in the bottom layer. | {
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2501.18394 | Quantum-Key Distribution using Decoy Pulses to Combat Photon-Number
Splitting by Eavesdropper: An Event-by-Event Impairment Enumeration Approach
for Performance Evaluation and Design | [
"quant-ph",
"cs.CR",
"cs.IT",
"cs.NI",
"math.IT"
] | Quantum-key distribution (QKD) schemes employing quantum communication links are typically based on the transmission of weak optical pulses over optical fibers to setup a secret key between the transmitting and receiving nodes. Alice transmits optically a random bit stream to the receiver (Bob) through the photon polar... | {
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2501.18397 | A weakly compressible SPH method for RANS simulation of wall-bounded
turbulent flows | [
"physics.flu-dyn",
"cs.CE"
] | This paper presents a Weakly Compressible Smoothed Particle Hydrodynamics (WCSPH) method for solving the two-equation Reynolds-Averaged Navier-Stokes (RANS) model. The turbulent wall-bounded flow with or without mild flow separation, a crucial flow pattern in engineering applications, yet rarely explored in the SPH com... | {
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2501.18401 | MatIR: A Hybrid Mamba-Transformer Image Restoration Model | [
"cs.CV"
] | In recent years, Transformers-based models have made significant progress in the field of image restoration by leveraging their inherent ability to capture complex contextual features. Recently, Mamba models have made a splash in the field of computer vision due to their ability to handle long-range dependencies and th... | {
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2501.18403 | Efficient Transformer for High Resolution Image Motion Deblurring | [
"cs.CV",
"cs.AI"
] | This paper presents a comprehensive study and improvement of the Restormer architecture for high-resolution image motion deblurring. We introduce architectural modifications that reduce model complexity by 18.4% while maintaining or improving performance through optimized attention mechanisms. Our enhanced training pip... | {
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2501.18405 | Segmentation of cracks in 3d images of fiber reinforced concrete using
deep learning | [
"cs.LG"
] | Cracks in concrete structures are very common and are an integral part of this heterogeneous material. Characteristics of cracks induced by standardized tests yield valuable information about the tested concrete formulation and its mechanical properties. Observing cracks on the surface of the concrete structure leaves ... | {
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2501.18407 | Degree is Important: On Evolving Homogeneous Boolean Functions | [
"cs.NE",
"cs.CR"
] | Boolean functions with good cryptographic properties like high nonlinearity and algebraic degree play an important in the security of stream and block ciphers. Such functions may be designed, for instance, by algebraic constructions or metaheuristics. This paper investigates the use of Evolutionary Algorithms (EAs) to ... | {
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2501.18411 | Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for
Agents | [
"cs.AI",
"astro-ph.IM",
"physics.comp-ph"
] | Modern science emerged from reasoning over repeatedly-observed planetary motions. We present Gravity-Bench-v1, an environment-based benchmark that challenges AI agents on tasks that parallel this historical development. Gravity-Bench-v1 evaluates agents on the discovery of physics concealed within a dynamic environment... | {
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} |
2501.18412 | Real Time Scheduling Framework for Multi Object Detection via Spiking
Neural Networks | [
"eess.SY",
"cs.CV",
"cs.NE",
"cs.SY"
] | Given the energy constraints in autonomous mobile agents (AMAs), such as unmanned vehicles, spiking neural networks (SNNs) are increasingly favored as a more efficient alternative to traditional artificial neural networks. AMAs employ multi-object detection (MOD) from multiple cameras to identify nearby objects while e... | {
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} |
2501.18413 | GBFRS: Robust Fuzzy Rough Sets via Granular-ball Computing | [
"cs.AI",
"cs.LG"
] | Fuzzy rough set theory is effective for processing datasets with complex attributes, supported by a solid mathematical foundation and closely linked to kernel methods in machine learning. Attribute reduction algorithms and classifiers based on fuzzy rough set theory exhibit promising performance in the analysis of high... | {
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} |
2501.18415 | Consensus statement on the credibility assessment of ML predictors | [
"q-bio.QM",
"cs.LG"
] | The rapid integration of machine learning (ML) predictors into in silico medicine has revolutionized the estimation of quantities of interest (QIs) that are otherwise challenging to measure directly. However, the credibility of these predictors is critical, especially when they inform high-stakes healthcare decisions. ... | {
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} |
2501.18416 | Exploring Potential Prompt Injection Attacks in Federated Military LLMs
and Their Mitigation | [
"cs.LG"
] | Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty. However, prompt injection attacks-malicious manipulations of input prompts-pose new threats that may undermine operational security, disrupt decision-making, and er... | {
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} |
2501.18417 | Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM) | [
"cs.LG"
] | Anomaly detection is essential for identifying rare and significant events across diverse domains such as finance, cybersecurity, and network monitoring. This paper presents Synthetic Anomaly Monitoring (SAM), an innovative approach that applies synthetic control methods from causal inference to improve both the accura... | {
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} |
2501.18418 | Task-based Regularization in Penalized Least-Squares for Binary Signal
Detection Tasks in Medical Image Denoising | [
"eess.IV",
"cs.CV"
] | Image denoising algorithms have been extensively investigated for medical imaging. To perform image denoising, penalized least-squares (PLS) problems can be designed and solved, in which the penalty term encodes prior knowledge of the object being imaged. Sparsity-promoting penalties, such as total variation (TV), have... | {
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} |
2501.18423 | DeepExtractor: Time-domain reconstruction of signals and glitches in
gravitational wave data with deep learning | [
"gr-qc",
"astro-ph.IM",
"cs.LG",
"physics.data-an",
"physics.ins-det"
] | Gravitational wave (GW) interferometers, detect faint signals from distant astrophysical events, such as binary black hole mergers. However, their high sensitivity also makes them susceptible to background noise, which can obscure these signals. This noise often includes transient artifacts called "glitches" that can m... | {
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} |
2501.18426 | Guaranteed confidence-band enclosures for PDE surrogates | [
"cs.LG",
"cs.AI"
] | We propose a method for obtaining statistically guaranteed confidence bands for functional machine learning techniques: surrogate models which map between function spaces, motivated by the need build reliable PDE emulators. The method constructs nested confidence sets on a low-dimensional representation (an SVD) of the... | {
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} |
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