id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.04120 | Bridging Impulse Control of Piecewise Deterministic Markov Processes and
Markov Decision Processes: Frameworks, Extensions, and Open Challenges | [
"stat.ME",
"cs.SY",
"eess.SY"
] | Control theory plays a pivotal role in understanding and optimizing the behavior of complex dynamical systems across various scientific and engineering disciplines. Two key frameworks that have emerged for modeling and solving control problems in stochastic systems are piecewise deterministic Markov processes (PDMPs) a... | {
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2501.04121 | Graph-Based Multimodal and Multi-view Alignment for Keystep Recognition | [
"cs.CV"
] | Egocentric videos capture scenes from a wearer's viewpoint, resulting in dynamic backgrounds, frequent motion, and occlusions, posing challenges to accurate keystep recognition. We propose a flexible graph-learning framework for fine-grained keystep recognition that is able to effectively leverage long-term dependencie... | {
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2501.04126 | Stochastic Process Learning via Operator Flow Matching | [
"cs.LG"
] | Expanding on neural operators, we propose a novel framework for stochastic process learning across arbitrary domains. In particular, we develop operator flow matching (OFM) for learning stochastic process priors on function spaces. OFM provides the probability density of the values of any collection of points and enabl... | {
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2501.04134 | Mixing Times and Privacy Analysis for the Projected Langevin Algorithm
under a Modulus of Continuity | [
"stat.ML",
"cs.LG",
"math.OC",
"math.ST",
"stat.TH"
] | We study the mixing time of the projected Langevin algorithm (LA) and the privacy curve of noisy Stochastic Gradient Descent (SGD), beyond nonexpansive iterations. Specifically, we derive new mixing time bounds for the projected LA which are, in some important cases, dimension-free and poly-logarithmic on the accuracy,... | {
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2501.04136 | Implementing Systemic Thinking for Automatic Schema Matching: An
Agent-Based Modeling Approach | [
"cs.AI",
"cs.MA"
] | Several approaches are proposed to deal with the problem of the Automatic Schema Matching (ASM). The challenges and difficulties caused by the complexity and uncertainty characterizing both the process and the outcome of Schema Matching motivated us to investigate how bio-inspired emerging paradigm can help with unders... | {
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2501.04138 | "Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer? | [
"cs.CL"
] | Human communication is a multifaceted and multimodal skill. Communication requires an understanding of both the surface-level textual content and the connotative intent of a piece of communication. In humans, learning to go beyond the surface level starts by learning communicative intent in speech. Once humans acquire ... | {
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2501.04141 | Hardware-In-The-Loop Training of a 4f Optical Correlator with
Logarithmic Complexity Reduction for CNNs | [
"cs.NE"
] | This work evaluates a forward-only learning algorithm on the MNIST dataset with hardware-in-the-loop training of a 4f optical correlator, achieving 87.6% accuracy with O(n2) complexity, compared to backpropagation, which achieves 88.8% accuracy with O(n2 log n) complexity. | {
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2501.04142 | BiasGuard: Guardrailing Fairness in Machine Learning Production Systems | [
"cs.LG",
"cs.AI",
"cs.CY"
] | As machine learning (ML) systems increasingly impact critical sectors such as hiring, financial risk assessments, and criminal justice, the imperative to ensure fairness has intensified due to potential negative implications. While much ML fairness research has focused on enhancing training data and processes, addressi... | {
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2501.04144 | Chirpy3D: Continuous Part Latents for Creative 3D Bird Generation | [
"cs.CV",
"cs.GR"
] | In this paper, we push the boundaries of fine-grained 3D generation into truly creative territory. Current methods either lack intricate details or simply mimic existing objects -- we enable both. By lifting 2D fine-grained understanding into 3D through multi-view diffusion and modeling part latents as continuous distr... | {
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2501.04150 | Benchmarking Large and Small MLLMs | [
"cs.CV"
] | Large multimodal language models (MLLMs) such as GPT-4V and GPT-4o have achieved remarkable advancements in understanding and generating multimodal content, showcasing superior quality and capabilities across diverse tasks. However, their deployment faces significant challenges, including slow inference, high computati... | {
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2501.04153 | Multilingual Open QA on the MIA Shared Task | [
"cs.CL",
"cs.LG"
] | Cross-lingual information retrieval (CLIR) ~\cite{shi2021cross, asai2021one, jiang2020cross} for example, can find relevant text in any language such as English(high resource) or Telugu (low resource) even when the query is posed in a different, possibly low-resource, language. In this work, we aim to develop useful CL... | {
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2501.04155 | MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data
Curation | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Vision-language models (VLMs) are highly effective but often underperform on specialized tasks; for example, Llava-1.5 struggles with chart and diagram understanding due to scarce task-specific training data. Existing training data, sourced from general-purpose datasets, fails to capture the nuanced details needed for ... | {
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2501.04160 | Collaborative Spacecraft Servicing under Partial Feedback using
Lyapunov-based Deep Neural Networks | [
"eess.SY",
"cs.SY",
"math.OC"
] | Multi-agent systems are increasingly applied in space missions, including distributed space systems, resilient constellations, and autonomous rendezvous and docking operations. A critical emerging application is collaborative spacecraft servicing, which encompasses on-orbit maintenance, space debris removal, and swarm-... | {
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2501.04161 | KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph
Information Fusion | [
"cs.LG",
"cs.IR"
] | While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to in... | {
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2501.04164 | Holographic Metasurface-Based Beamforming for Multi-Altitude LEO
Satellite Networks | [
"cs.IT",
"eess.SP",
"math.IT"
] | Low Earth Orbit (LEO) satellite networks are capable of improving the global Internet service coverage. In this context, we propose a hybrid beamforming design for holographic metasurface based terrestrial users in multi-altitude LEO satellite networks. Firstly, the holographic beamformer is optimized by maximizing the... | {
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2501.04167 | Reasoning-Enhanced Self-Training for Long-Form Personalized Text
Generation | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Personalized text generation requires a unique ability of large language models (LLMs) to learn from context that they often do not encounter during their standard training. One way to encourage LLMs to better use personalized context for generating outputs that better align with the user's expectations is to instruct ... | {
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2501.04169 | Learning to Transfer Human Hand Skills for Robot Manipulations | [
"cs.RO",
"cs.AI",
"cs.LG"
] | We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations. Unlike existing approaches that solely rely on kinematics information without taking into account the plausibility of robot and object interaction, our method directly infers plausible robot manipulation actio... | {
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2501.04170 | A Bayesian Modeling Framework for Estimation and Ground Segmentation of
Cluttered Staircases | [
"cs.RO"
] | Autonomous robot navigation in complex environments requires robust perception as well as high-level scene understanding due to perceptual challenges, such as occlusions, and uncertainty introduced by robot movement. For example, a robot climbing a cluttered staircase can misinterpret clutter as a step, misrepresenting... | {
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2501.04172 | Machine Learning for Identifying Grain Boundaries in Scanning Electron
Microscopy (SEM) Images of Nanoparticle Superlattices | [
"cond-mat.mtrl-sci",
"cs.CV",
"eess.IV"
] | Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is cr... | {
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2501.04173 | Multimodal Multihop Source Retrieval for Web Question Answering | [
"cs.CL",
"cs.AI"
] | This work deals with the challenge of learning and reasoning over multi-modal multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sentences to learn multi-source reasoning paths and find the supporting facts across both image and text modalities for answering t... | {
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2501.04179 | Generation from Noisy Examples | [
"stat.ML",
"cs.LG"
] | We continue to study the learning-theoretic foundations of generation by extending the results from Kleinberg and Mullainathan [2024] and Li et al. [2024] to account for noisy example streams. In the noiseless setting of Kleinberg and Mullainathan [2024] and Li et al. [2024], an adversary picks a hypothesis from a bina... | {
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2501.04180 | HIVEX: A High-Impact Environment Suite for Multi-Agent Research
(extended version) | [
"cs.MA",
"cs.AI",
"cs.GT"
] | Games have been vital test beds for the rapid development of Agent-based research. Remarkable progress has been achieved in the past, but it is unclear if the findings equip for real-world problems. While pressure grows, some of the most critical ecological challenges can find mitigation and prevention solutions throug... | {
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2501.04182 | Fixed Points of Deep Neural Networks: Emergence, Stability, and
Applications | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA"
] | We present numerical and analytical results on the formation and stability of a family of fixed points of deep neural networks (DNNs). Such fixed points appear in a class of DNNs when dimensions of input and output vectors are the same. We demonstrate examples of applications of such networks in supervised, semi-superv... | {
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2501.04184 | MedicalNarratives: Connecting Medical Vision and Language with Localized
Narratives | [
"cs.CV"
] | We propose MedicalNarratives, a dataset curated from medical pedagogical videos similar in nature to data collected in Think-Aloud studies and inspired by Localized Narratives, which collects grounded image-text data by curating instructors' speech and mouse cursor movements synchronized in time. MedicalNarratives enab... | {
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2501.04190 | Partition Constraints for Conjunctive Queries: Bounds and Worst-Case
Optimal Joins | [
"cs.DB"
] | In the last decade, various works have used statistics on relations to improve both the theory and practice of conjunctive query execution. Starting with the AGM bound which took advantage of relation sizes, later works incorporated statistics like functional dependencies and degree constraints. Each new statistic prom... | {
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2501.04193 | GNN-based Decentralized Perception in Multirobot Systems for Predicting
Worker Actions | [
"cs.RO",
"cs.AI",
"cs.MA"
] | In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. This paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows ea... | {
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2501.04194 | STLCG++: A Masking Approach for Differentiable Signal Temporal Logic
Specification | [
"cs.RO",
"cs.LG",
"cs.SC"
] | Signal Temporal Logic (STL) offers a concise yet expressive framework for specifying and reasoning about spatio-temporal behaviors of robotic systems. Attractively, STL admits the notion of robustness, the degree to which an input signal satisfies or violates an STL specification, thus providing a nuanced evaluation of... | {
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2501.04196 | Comparison of Neural Models for X-ray Image Classification in COVID-19
Detection | [
"eess.IV",
"cs.LG"
] | This study presents a comparative analysis of methods for detecting COVID-19 infection in radiographic images. The images, sourced from publicly available datasets, were categorized into three classes: 'normal,' 'pneumonia,' and 'COVID.' For the experiments, transfer learning was employed using eight pre-trained networ... | {
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2501.04199 | Unattainability of Common Knowledge in Asymmetric Games with Imperfect
Information | [
"cs.MA",
"cs.GT",
"cs.LO"
] | In this paper, we present a conceptual model game to examine the dynamics of asymmetric interactions in games with imperfect information. The game involves two agents with starkly contrasting capabilities: one agent can take actions but has no information of the state of the game, whereas the other agent has perfect in... | {
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2501.04202 | Generative Dataset Distillation Based on Self-knowledge Distillation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Dataset distillation is an effective technique for reducing the cost and complexity of model training while maintaining performance by compressing large datasets into smaller, more efficient versions. In this paper, we present a novel generative dataset distillation method that can improve the accuracy of aligning pred... | {
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2501.04204 | LipGen: Viseme-Guided Lip Video Generation for Enhancing Visual Speech
Recognition | [
"cs.CV",
"cs.MM"
] | Visual speech recognition (VSR), commonly known as lip reading, has garnered significant attention due to its wide-ranging practical applications. The advent of deep learning techniques and advancements in hardware capabilities have significantly enhanced the performance of lip reading models. Despite these advancement... | {
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2501.04206 | GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced
Explainability in Breast Cancer Histopathology | [
"eess.IV",
"cs.CV"
] | Explainable AI (XAI) in medical histopathology is essential for enhancing the interpretability and clinical trustworthiness of deep learning models in cancer diagnosis. However, the black-box nature of these models often limits their clinical adoption. We introduce GRAPHITE (Graph-based Interpretable Tissue Examination... | {
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2501.04210 | Recognition-Oriented Low-Light Image Enhancement based on Global and
Pixelwise Optimization | [
"cs.CV",
"eess.IV"
] | In this paper, we propose a novel low-light image enhancement method aimed at improving the performance of recognition models. Despite recent advances in deep learning, the recognition of images under low-light conditions remains a challenge. Although existing low-light image enhancement methods have been developed to ... | {
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2501.04211 | CURing Large Models: Compression via CUR Decomposition | [
"cs.LG",
"cs.AI"
] | Large deep learning models have achieved remarkable success but are resource-intensive, posing challenges such as memory usage. We introduce CURing, a novel model compression method based on CUR matrix decomposition, which approximates weight matrices as the product of selected columns (C) and rows (R), and a small lin... | {
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2501.04213 | UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection
in Autonomous Vehicles | [
"cs.CV",
"cs.AI",
"cs.LG"
] | To enhance perception in autonomous vehicles (AVs), recent efforts are concentrating on 3D object detectors, which deliver more comprehensive predictions than traditional 2D object detectors, at the cost of increased memory footprint and computational resource usage. We present a novel framework called UPAQ, which leve... | {
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2501.04216 | Optimal Oblivious Algorithms for Multi-way Joins | [
"cs.DB",
"cs.CR"
] | In cloud databases, cloud computation over sensitive data uploaded by clients inevitably causes concern about data security and privacy. Even when encryption primitives and trusted computing environments are integrated into query processing to safeguard the actual contents of the data, access patterns of algorithms can... | {
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2501.04217 | Continual Self-supervised Learning Considering Medical Domain Knowledge
in Chest CT Images | [
"cs.CV",
"cs.AI"
] | We propose a novel continual self-supervised learning method (CSSL) considering medical domain knowledge in chest CT images. Our approach addresses the challenge of sequential learning by effectively capturing the relationship between previously learned knowledge and new information at different stages. By incorporatin... | {
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2501.04222 | Privacy-Preserving Distributed Online Mirror Descent for Nonconvex
Optimization | [
"eess.SY",
"cs.SY"
] | We investigate the distributed online nonconvex optimization problem with differential privacy over time-varying networks. Each node minimizes the sum of several nonconvex functions while preserving the node's differential privacy. We propose a privacy-preserving distributed online mirror descent algorithm for nonconve... | {
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2501.04227 | Agent Laboratory: Using LLM Agents as Research Assistants | [
"cs.HC",
"cs.AI",
"cs.CL",
"cs.LG"
] | Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable o... | {
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2501.04228 | Constraints as Rewards: Reinforcement Learning for Robots without Reward
Functions | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Reinforcement learning has become an essential algorithm for generating complex robotic behaviors. However, to learn such behaviors, it is necessary to design a reward function that describes the task, which often consists of multiple objectives that needs to be balanced. This tuning process is known as reward engineer... | {
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2501.04231 | Computation and Communication Co-scheduling for Timely Multi-Task
Inference at the Wireless Edge | [
"cs.IT",
"cs.NI",
"math.IT"
] | In multi-task remote inference systems, an intelligent receiver (e.g., command center) performs multiple inference tasks (e.g., target detection) using data features received from several remote sources (e.g., edge sensors). Key challenges to facilitating timely inference in these systems arise from (i) limited computa... | {
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2501.04233 | A note on the differential spectrum of a class of locally APN functions | [
"cs.IT",
"cs.CR",
"math.IT"
] | Let $\gf_{p^n}$ denote the finite field containing $p^n$ elements, where $n$ is a positive integer and $p$ is a prime. The function $f_u(x)=x^{\frac{p^n+3}{2}}+ux^2$ over $\gf_{p^n}[x]$ with $u\in\gf_{p^n}\setminus\{0,\pm1\}$ was recently studied by Budaghyan and Pal in \cite{Budaghyan2024ArithmetizationorientedAP}, wh... | {
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2501.04234 | Statistical Uncertainty Quantification for Aggregate Performance Metrics
in Machine Learning Benchmarks | [
"stat.ML",
"cs.LG",
"stat.AP"
] | Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus and then adapted to solve a variety of downstream tasks. To summarize performance across multiple tasks, evaluation metrics are often aggregated into a summary metric, e.g., aver... | {
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2501.04238 | A Quasi-deterministic Channel Model for Underwater Acoustic
Communication Systems | [
"eess.SY",
"cs.SY"
] | In this paper, a quasi-deterministic (Q-D) model for non-stationary underwater acoustic (UWA) channels is proposed. This model combines the BELLHOP deterministic model and geometry-based stochastic model (GBSM), which provides higher accuracy and flexibility. Different propagation components in shallow water are classi... | {
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2501.04239 | Dynamic Localisation of Spatial-Temporal Graph Neural Network | [
"cs.LG"
] | Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable... | {
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2501.04240 | A Novel Non-Stationary Channel Emulator for 6G MIMO Wireless Channels | [
"eess.SY",
"cs.IT",
"cs.SY",
"math.IT"
] | The performance evaluation of sixth generation (6G) communication systems is anticipated to be a controlled and repeatable process in the lab, which brings up the demand for wireless channel emulators. However, channel emulation for 6G space-time-frequency (STF) non-stationary channels is missing currently. In this pap... | {
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2501.04242 | Beam Domain Channel Estimation for Spatial Non-Stationary Massive MIMO
Systems | [
"eess.SY",
"cs.IT",
"cs.SY",
"math.IT"
] | In massive multiple-input multiple-output (MIMO) systems, the channel estimation scheme is subject to the spatial non-stationarity and inevitably power leakage in the beam domain. In this paper, a beam domain channel estimation scheme is investigated for spatial non-stationary (SNS) massive MIMO systems considering pow... | {
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2501.04249 | IOLBENCH: Benchmarking LLMs on Linguistic Reasoning | [
"cs.CL"
] | Despite the remarkable advancements and widespread applications of deep neural networks, their ability to perform reasoning tasks remains limited, particularly in domains requiring structured, abstract thought. In this paper, we investigate the linguistic reasoning capabilities of state-of-the-art large language models... | {
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2501.04253 | Integrated Offline and Online Learning to Solve a Large Class of
Scheduling Problems | [
"math.OC",
"cs.AI",
"cs.LG"
] | In this paper, we develop a unified machine learning (ML) approach to predict high-quality solutions for single-machine scheduling problems with a non-decreasing min-sum objective function with or without release times. Our ML approach is novel in three major aspects. First, our approach is developed for the entire cla... | {
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2501.04259 | Stable Derivative Free Gaussian Mixture Variational Inference for
Bayesian Inverse Problems | [
"cs.LG",
"cs.NA",
"math.NA"
] | This paper is concerned with the approximation of probability distributions known up to normalization constants, with a focus on Bayesian inference for large-scale inverse problems in scientific computing. In this context, key challenges include costly repeated evaluations of forward models, multimodality, and inaccess... | {
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2501.04260 | Modeling All Response Surfaces in One for Conditional Search Spaces | [
"cs.LG"
] | Bayesian Optimization (BO) is a sample-efficient black-box optimizer commonly used in search spaces where hyperparameters are independent. However, in many practical AutoML scenarios, there will be dependencies among hyperparameters, forming a conditional search space, which can be partitioned into structurally distinc... | {
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2501.04262 | Target Tracking Using the Invariant Extended Kalman Filter with
Numerical Differentiation for Estimating Curvature and Torsion | [
"eess.SY",
"cs.SY",
"eess.SP"
] | The goal of target tracking is to estimate target position, velocity, and acceleration in real time using position data. This paper introduces a novel target-tracking technique that uses adaptive input and state estimation (AISE) for real-time numerical differentiation to estimate velocity, acceleration, and jerk from ... | {
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2501.04263 | KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial
Odometry | [
"cs.RO",
"cs.AI",
"eess.SP"
] | Recent advancements in LiDAR-Inertial Odometry (LIO) have boosted a large amount of applications. However, traditional LIO systems tend to focus more on localization rather than mapping, with maps consisting mostly of sparse geometric elements, which is not ideal for downstream tasks. Recent emerging neural field techn... | {
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2501.04266 | Scaling Large Language Model Training on Frontier with Low-Bandwidth
Partitioning | [
"cs.DC",
"cs.AI"
] | Scaling up Large Language Model(LLM) training involves fitting a tremendous amount of training parameters across a limited number of workers. However, methods like ZeRO-3 that drastically reduce GPU memory pressure often incur heavy communication to ensure global synchronization and consistency. Established efforts suc... | {
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2501.04268 | Robotic Programmer: Video Instructed Policy Code Generation for Robotic
Manipulation | [
"cs.RO",
"cs.CV"
] | Zero-shot generalization across various robots, tasks and environments remains a significant challenge in robotic manipulation. Policy code generation methods use executable code to connect high-level task descriptions and low-level action sequences, leveraging the generalization capabilities of large language models a... | {
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2501.04269 | Open set label noise learning with robust sample selection and
margin-guided module | [
"cs.CV"
] | In recent years, the remarkable success of deep neural networks (DNNs) in computer vision is largely due to large-scale, high-quality labeled datasets. Training directly on real-world datasets with label noise may result in overfitting. The traditional method is limited to deal with closed set label noise, where noisy ... | {
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2501.04272 | On weight and variance uncertainty in neural networks for regression
tasks | [
"stat.ML",
"cs.LG"
] | We consider the problem of weight uncertainty proposed by [Blundell et al. (2015). Weight uncertainty in neural network. In International conference on machine learning, 1613-1622, PMLR.] in neural networks {(NNs)} specialized for regression tasks. {We further} investigate the effect of variance uncertainty in {their m... | {
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2501.04273 | Frenet-Serret-Based Trajectory Prediction | [
"eess.SY",
"cs.SY",
"eess.SP"
] | Trajectory prediction is a crucial element of guidance, navigation, and control systems. This paper presents two novel trajectory-prediction methods based on real-time position measurements and adaptive input and state estimation (AISE). The first method, called AISE/va, uses position measurements to estimate the targe... | {
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2501.04275 | Adaptive Numerical Differentiation for Extremum Seeking with Sensor
Noise | [
"eess.SY",
"cs.SY"
] | Extremum-seeking control (ESC) is widely used to optimize performance when the system dynamics are uncertain. However, sensitivity to sensor noise is an important issue in ESC implementation due to the use of high-pass filters or gradient estimators. To reduce the sensitivity of ESC to noise, this paper investigates th... | {
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2501.04276 | Bridging Adaptivity and Safety: Learning Agile Collision-Free Locomotion
Across Varied Physics | [
"cs.RO",
"cs.LG"
] | Real-world legged locomotion systems often need to reconcile agility and safety for different scenarios. Moreover, the underlying dynamics are often unknown and time-variant (e.g., payload, friction). In this paper, we introduce BAS (Bridging Adaptivity and Safety), which builds upon the pipeline of prior work Agile Bu... | {
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2501.04279 | OpenIN: Open-Vocabulary Instance-Oriented Navigation in Dynamic Domestic
Environments | [
"cs.RO"
] | In daily domestic settings, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, the robot mu... | {
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2501.04281 | Cluster & Disperse: a general air conflict resolution heuristic using
unsupervised learning | [
"cs.RO",
"cs.LG",
"physics.soc-ph"
] | We provide a general and malleable heuristic for the air conflict resolution problem. This heuristic is based on a new neighborhood structure for searching the solution space of trajectories and flight-levels. Using unsupervised learning, the core idea of our heuristic is to cluster the conflict points and disperse the... | {
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2501.04283 | Enhancing Scene Classification in Cloudy Image Scenarios: A
Collaborative Transfer Method with Information Regulation Mechanism using
Optical Cloud-Covered and SAR Remote Sensing Images | [
"cs.CV",
"cs.AI",
"eess.IV"
] | In remote sensing scene classification, leveraging the transfer methods with well-trained optical models is an efficient way to overcome label scarcity. However, cloud contamination leads to optical information loss and significant impacts on feature distribution, challenging the reliability and stability of transferre... | {
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2501.04284 | ContextMRI: Enhancing Compressed Sensing MRI through Metadata
Conditioning | [
"cs.CV",
"cs.LG"
] | Compressed sensing MRI seeks to accelerate MRI acquisition processes by sampling fewer k-space measurements and then reconstructing the missing data algorithmically. The success of these approaches often relies on strong priors or learned statistical models. While recent diffusion model-based priors have shown great po... | {
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2501.04285 | Separate Source Channel Coding Is Still What You Need: An LLM-based
Rethinking | [
"cs.IT",
"eess.SP",
"math.IT"
] | Along with the proliferating research interest in Semantic Communication (SemCom), Joint Source Channel Coding (JSCC) has dominated the attention due to the widely assumed existence in efficiently delivering information semantics. %has emerged as a pivotal area of research, aiming to enhance the efficiency and reliabil... | {
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2501.04286 | Mapping the Edge of Chaos: Fractal-Like Boundaries in The Trainability
of Decoder-Only Transformer Models | [
"cs.LG",
"cs.AI"
] | In the realm of fractal geometry, intricate structures emerge from simple iterative processes that partition parameter spaces into regions of stability and instability. Likewise, training large language models involves iteratively applying update functions, such as Adam, where even slight hyperparameter adjustments can... | {
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2501.04287 | ElasticZO: A Memory-Efficient On-Device Learning with Combined Zeroth-
and First-Order Optimization | [
"cs.LG"
] | Zeroth-order (ZO) optimization is being recognized as a simple yet powerful alternative to standard backpropagation (BP)-based training. Notably, ZO optimization allows for training with only forward passes and (almost) the same memory as inference, making it well-suited for edge devices with limited computing and memo... | {
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2501.04288 | An Analysis of Model Robustness across Concurrent Distribution Shifts | [
"cs.LG"
] | Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occur in more complex forms in real-world scenarios, we broadened o... | {
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2501.04292 | MADUV: The 1st INTERSPEECH Mice Autism Detection via Ultrasound
Vocalization Challenge | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | The Mice Autism Detection via Ultrasound Vocalization (MADUV) Challenge introduces the first INTERSPEECH challenge focused on detecting autism spectrum disorder (ASD) in mice through their vocalizations. Participants are tasked with developing models to automatically classify mice as either wild-type or ASD models base... | {
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2501.04293 | TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task
Learning | [
"cs.CV"
] | Transfer learning paradigm has driven substantial advancements in various vision tasks. However, as state-of-the-art models continue to grow, classical full fine-tuning often becomes computationally impractical, particularly in multi-task learning (MTL) setup where training complexity increases proportional to the numb... | {
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2501.04299 | Circuit Complexity Bounds for Visual Autoregressive Model | [
"stat.ML",
"cs.AI",
"cs.CC",
"cs.CL",
"cs.LG"
] | Understanding the expressive ability of a specific model is essential for grasping its capacity limitations. Recently, several studies have established circuit complexity bounds for Transformer architecture. Besides, the Visual AutoRegressive (VAR) model has risen to be a prominent method in the field of image generati... | {
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2501.04300 | Handling Incomplete Heterogeneous Data using a Data-Dependent Kernel | [
"cs.LG"
] | Handling incomplete data in real-world applications is a critical challenge due to two key limitations of existing methods: (i) they are primarily designed for numeric data and struggle with categorical or heterogeneous/mixed datasets; (ii) they assume that data is missing completely at random, which is often not the c... | {
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2501.04302 | H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding
in Autonomous Driving | [
"cs.CV",
"cs.AI"
] | With the prevalence of Multimodal Large Language Models(MLLMs), autonomous driving has encountered new opportunities and challenges. In particular, multi-modal video understanding is critical to interactively analyze what will happen in the procedure of autonomous driving. However, videos in such a dynamical scene that... | {
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2501.04303 | Multimodal Graph Constrastive Learning and Prompt for ChartQA | [
"cs.CL"
] | ChartQA presents significant challenges due to the complex distribution of chart elements and the implicit patterns embedded within the underlying data. In this chapter, we have developed a joint multimodal scene graph for charts, explicitly representing the relationships between chart elements and their associated pat... | {
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2501.04304 | DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion
Models | [
"cs.CV",
"cs.LG"
] | Despite the widespread use of text-to-image diffusion models across various tasks, their computational and memory demands limit practical applications. To mitigate this issue, quantization of diffusion models has been explored. It reduces memory usage and computational costs by compressing weights and activations into ... | {
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2501.04305 | Physics-Informed Super-Resolution Diffusion for 6D Phase Space
Diagnostics | [
"cs.LG",
"math.DS",
"physics.acc-ph"
] | Adaptive physics-informed super-resolution diffusion is developed for non-invasive virtual diagnostics of the 6D phase space density of charged particle beams. An adaptive variational autoencoder (VAE) embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 326 pixel ... | {
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2501.04306 | LLM4SR: A Survey on Large Language Models for Scientific Research | [
"cs.CL",
"cs.DL"
] | In recent years, the rapid advancement of Large Language Models (LLMs) has transformed the landscape of scientific research, offering unprecedented support across various stages of the research cycle. This paper presents the first systematic survey dedicated to exploring how LLMs are revolutionizing the scientific rese... | {
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2501.04307 | Finite Dimensional Lattice Codes with Self Error-Detection and Retry
Decoding | [
"cs.IT",
"math.IT"
] | Lattice codes with optimal decoding coefficient are capacity-achieving when dimension $N \rightarrow \infty$. In communications systems, finite dimensional lattice codes are considered, where the optimal decoding coefficients may still fail decoding even when $R< C$. This paper presents a new retry decoding scheme for ... | {
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2501.04308 | FSC-loss: A Frequency-domain Structure Consistency Learning Approach for
Signal Data Recovery and Reconstruction | [
"eess.SP",
"cs.LG"
] | A core challenge for signal data recovery is to model the distribution of signal matrix (SM) data based on measured low-quality data in biomedical engineering of magnetic particle imaging (MPI). For acquiring the high-resolution (high-quality) SM, the number of meticulous measurements at numerous positions in the field... | {
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2501.04315 | RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for
Rank Adaptation | [
"cs.LG",
"cs.AI"
] | Fine-tuning helps large language models (LLM) recover degraded information and enhance task performance. Although Low-Rank Adaptation (LoRA) is widely used and effective for fine-tuning, we have observed that its scaling factor can limit or even reduce performance as the rank size increases. To address this issue, we p... | {
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2501.04316 | Who Does the Giant Number Pile Like Best: Analyzing Fairness in Hiring
Contexts | [
"cs.CL"
] | Large language models (LLMs) are increasingly being deployed in high-stakes applications like hiring, yet their potential for unfair decision-making and outcomes remains understudied, particularly in generative settings. In this work, we examine the fairness of LLM-based hiring systems through two real-world tasks: res... | {
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2501.04319 | VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated
Learning | [
"cs.CR",
"cs.DC",
"cs.ET",
"cs.LG"
] | Blockchain-based Federated Learning (FL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still vulnerable to various attacks, including inference and model poisoning. Addition... | {
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2501.04322 | Eve: Efficient Multimodal Vision Language Models with Elastic Visual
Experts | [
"cs.CV"
] | Multimodal vision language models (VLMs) have made significant progress with the support of continuously increasing model sizes and data volumes. Running VLMs on edge devices has become a challenge for their widespread application. There are several efficient VLM efforts, but they often sacrifice linguistic capabilitie... | {
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2501.04323 | Navigating the Designs of Privacy-Preserving Fine-tuning for Large
Language Models | [
"cs.LG",
"cs.CR"
] | Instruction tuning has proven effective in enhancing Large Language Models' (LLMs) performance on downstream tasks. However, real-world fine-tuning faces inherent conflicts between model providers' intellectual property protection, clients' data privacy requirements, and tuning costs. While recent approaches like split... | {
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2501.04325 | Edit as You See: Image-guided Video Editing via Masked Motion Modeling | [
"cs.CV"
] | Recent advancements in diffusion models have significantly facilitated text-guided video editing. However, there is a relative scarcity of research on image-guided video editing, a method that empowers users to edit videos by merely indicating a target object in the initial frame and providing an RGB image as reference... | {
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2501.04328 | Lower Bound on the Error Rate of Genie-Aided Lattice Decoding | [
"cs.IT",
"math.IT"
] | A genie-aided decoder for finite dimensional lattice codes is considered. The decoder may exhaustively search through all possible scaling factors $\alpha \in \mathbb{R}$. We show that this decoder can achieve lower word error rate (WER) than the one-shot decoder using $\alpha_{MMSE}$ as a scaling factor. A lower bound... | {
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2501.04329 | An Efficient Adaptive Compression Method for Human Perception and
Machine Vision Tasks | [
"cs.CV"
] | While most existing neural image compression (NIC) and neural video compression (NVC) methodologies have achieved remarkable success, their optimization is primarily focused on human visual perception. However, with the rapid development of artificial intelligence, many images and videos will be used for various machin... | {
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2501.04331 | AutoDFL: A Scalable and Automated Reputation-Aware Decentralized
Federated Learning | [
"cs.DC",
"cs.CR",
"cs.ET",
"cs.LG"
] | Blockchained federated learning (BFL) combines the concepts of federated learning and blockchain technology to enhance privacy, security, and transparency in collaborative machine learning models. However, implementing BFL frameworks poses challenges in terms of scalability and cost-effectiveness. Reputation-aware BFL ... | {
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2501.04336 | Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs
for Effective Long Video Analysis with LLMs | [
"cs.CV"
] | Long-form video understanding with Large Vision Language Models is challenged by the need to analyze temporally dispersed yet spatially concentrated key moments within limited context windows. In this work, we introduce VideoMindPalace, a new framework inspired by the "Mind Palace", which organizes critical video momen... | {
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} |
2501.04339 | DCIts -- Deep Convolutional Interpreter for time series | [
"stat.ML",
"cs.LG",
"physics.app-ph"
] | We introduce an interpretable deep learning model for multivariate time series forecasting that prioritizes both predictive performance and interpretability - key requirements for understanding complex physical phenomena. Our model not only matches but often surpasses existing interpretability methods, achieving this w... | {
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} |
2501.04340 | On Domain Decomposition for Magnetostatic Problems in 3D | [
"cs.CE",
"cs.NA",
"math.NA"
] | The simulation of three dimensional magnetostatic problems plays an important role, for example when simulating synchronous electric machines. Building on prior work that developed a domain decomposition algorithm using isogeometric analysis, this paper extends the method to support subdomains composed of multiple patc... | {
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} |
2501.04341 | Understanding Before Reasoning: Enhancing Chain-of-Thought with
Iterative Summarization Pre-Prompting | [
"cs.CL"
] | Chain-of-Thought (CoT) Prompting is a dominant paradigm in Large Language Models (LLMs) to enhance complex reasoning. It guides LLMs to present multi-step reasoning, rather than generating the final answer directly. However, CoT encounters difficulties when key information required for reasoning is implicit or missing.... | {
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} |
2501.04343 | TimelineKGQA: A Comprehensive Question-Answer Pair Generator for
Temporal Knowledge Graphs | [
"cs.LO",
"cs.AI",
"cs.CL"
] | Question answering over temporal knowledge graphs (TKGs) is crucial for understanding evolving facts and relationships, yet its development is hindered by limited datasets and difficulties in generating custom QA pairs. We propose a novel categorization framework based on timeline-context relationships, along with \tex... | {
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} |
2501.04347 | Keyword Search in the Deep Web | [
"cs.DB"
] | The Deep Web is constituted by data that are accessible through Web pages, but not readily indexable by search engines as they are returned in dynamic pages. In this paper we propose a conceptual framework for answering keyword queries on Deep Web sources represented as relational tables with so-called access limitatio... | {
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} |
2501.04352 | Online Gaussian Test-Time Adaptation of Vision-Language Models | [
"cs.CV"
] | Online test-time adaptation (OTTA) of vision-language models (VLMs) has recently garnered increased attention to take advantage of data observed along a stream to improve future predictions. Unfortunately, existing methods rely on dataset-specific hyperparameters, significantly limiting their adaptability to unseen tas... | {
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} |
2501.04353 | DeFusion: An Effective Decoupling Fusion Network for Multi-Modal
Pregnancy Prediction | [
"cs.CV",
"cs.LG"
] | Temporal embryo images and parental fertility table indicators are both valuable for pregnancy prediction in \textbf{in vitro fertilization embryo transfer} (IVF-ET). However, current machine learning models cannot make full use of the complementary information between the two modalities to improve pregnancy prediction... | {
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} |
2501.04359 | Decoding EEG Speech Perception with Transformers and VAE-based Data
Augmentation | [
"eess.AS",
"cs.CL",
"cs.HC",
"cs.LG",
"cs.SD"
] | Decoding speech from non-invasive brain signals, such as electroencephalography (EEG), has the potential to advance brain-computer interfaces (BCIs), with applications in silent communication and assistive technologies for individuals with speech impairments. However, EEG-based speech decoding faces major challenges, s... | {
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} |
2501.04361 | A Unified Framework for Foreground and Anonymization Area Segmentation
in CT and MRI Data | [
"eess.IV",
"cs.CV"
] | This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolkit comprises two main components: a segmentation network that delineates foreground regions to optimi... | {
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} |
2501.04364 | An innovative data collection method to eliminate the preprocessing
phase in web usage mining | [
"cs.IR"
] | The underlying data source for web usage mining (WUM) is commonly thought to be server logs. However, access log files ensure quite limited data about the clients. Identifying sessions from this messy data takes a considerable effort, and operations performed for this purpose do not always yield excellent results. Also... | {
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} |
2501.04366 | DispFormer: Pretrained Transformer for Flexible Dispersion Curve
Inversion from Global Synthesis to Regional Applications | [
"physics.geo-ph",
"cs.AI"
] | Surface wave dispersion curve inversion is essential for estimating subsurface Shear-wave velocity ($v_s$), yet traditional methods often struggle to balance computational efficiency with inversion accuracy. While deep learning approaches show promise, previous studies typically require large amounts of labeled data an... | {
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} |
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