id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.18649 | Fake News Detection After LLM Laundering: Measurement and Explanation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | With their advanced capabilities, Large Language Models (LLMs) can generate highly convincing and contextually relevant fake news, which can contribute to disseminating misinformation. Though there is much research on fake news detection for human-written text, the field of detecting LLM-generated fake news is still un... | {
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2501.18650 | Constructing Cell-type Taxonomy by Optimal Transport with Relaxed
Marginal Constraints | [
"q-bio.GN",
"cs.LG",
"stat.ML"
] | The rapid emergence of single-cell data has facilitated the study of many different biological conditions at the cellular level. Cluster analysis has been widely applied to identify cell types, capturing the essential patterns of the original data in a much more concise form. One challenge in the cluster analysis of ce... | {
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2501.18653 | Cogito, ergo sum: A Neurobiologically-Inspired Cognition-Memory-Growth
System for Code Generation | [
"cs.SE",
"cs.AI"
] | Large language models based Multi Agent Systems (MAS) have demonstrated promising performance for enhancing the efficiency and accuracy of code generation tasks. However,most existing methods follow a conventional sequence of planning, coding, and debugging,which contradicts the growth-driven nature of human learning p... | {
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2501.18657 | Enhancing Large Language Model Efficiencyvia Symbolic Compression: A
Formal Approach Towards Interpretability | [
"cs.AI",
"cs.SE"
] | Large language models (LLMs) face significant token efficiency bottlenecks in code generation and logical reasoning tasks, a challenge that directly impacts inference cost and model interpretability. This paper proposes a formal framework based on symbolic compression,integrating combinatory logic, information-theoreti... | {
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2501.18659 | SAFL: Structure-Aware Personalized Federated Learning via
Client-Specific Clustering and SCSI-Guided Model Pruning | [
"cs.LG",
"cs.DC"
] | Federated Learning (FL) enables clients to collaboratively train machine learning models without sharing local data, preserving privacy in diverse environments. While traditional FL approaches preserve privacy, they often struggle with high computational and communication overhead. To address these issues, model prunin... | {
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2501.18660 | NAR db status Version 2 and miRNAverse: Over Two Years of Manual
Meta-Registry Curation and Updates | [
"q-bio.OT",
"cs.DB"
] | Previously, we reported on a new meta-registry for NAR published databases focusing on high-quality annotations regarding database availability and longevity. With over two years of continued manual curation, here, we report on recent updates and additions. Furthermore, the available annotations as well as the underlyi... | {
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2501.18663 | Joint Optimization of Prompt Security and System Performance in
Edge-Cloud LLM Systems | [
"cs.CR",
"cs.AI"
] | Large language models (LLMs) have significantly facilitated human life, and prompt engineering has improved the efficiency of these models. However, recent years have witnessed a rise in prompt engineering-empowered attacks, leading to issues such as privacy leaks, increased latency, and system resource wastage. Though... | {
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2501.18664 | Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Deep learning has achieved significant success in single hyperspectral image super-resolution (SHSR); however, the high spectral dimensionality leads to a heavy computational burden, thus making it difficult to deploy in real-time scenarios. To address this issue, this paper proposes a novel lightweight SHSR network, i... | {
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2501.18665 | BARNN: A Bayesian Autoregressive and Recurrent Neural Network | [
"cs.LG",
"cs.AI"
] | Autoregressive and recurrent networks have achieved remarkable progress across various fields, from weather forecasting to molecular generation and Large Language Models. Despite their strong predictive capabilities, these models lack a rigorous framework for addressing uncertainty, which is key in scientific applicati... | {
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2501.18666 | Structure Development in List-Sorting Transformers | [
"cs.LG",
"cs.AI",
"cs.NE"
] | We study how a one-layer attention-only transformer develops relevant structures while learning to sort lists of numbers. At the end of training, the model organizes its attention heads in two main modes that we refer to as vocabulary-splitting and copy-suppression. Both represent simpler modes than having multiple hea... | {
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2501.18668 | Simulation Streams: A Programming Paradigm for Controlling Large
Language Models and Building Complex Systems with Generative AI | [
"cs.AI",
"cs.SE"
] | We introduce Simulation Streams, a programming paradigm designed to efficiently control and leverage Large Language Models (LLMs) for complex, dynamic simulations and agentic workflows. Our primary goal is to create a minimally interfering framework that harnesses the agentic abilities of LLMs while addressing their li... | {
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2501.18669 | The Pitfalls of "Security by Obscurity" And What They Mean for
Transparent AI | [
"cs.CR",
"cs.AI",
"cs.CY"
] | Calls for transparency in AI systems are growing in number and urgency from diverse stakeholders ranging from regulators to researchers to users (with a comparative absence of companies developing AI). Notions of transparency for AI abound, each addressing distinct interests and concerns. In computer security, transp... | {
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2501.18670 | High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA
Fine-Tuning with Multimodal LLaMA 3.2 | [
"cs.CV",
"cs.AI"
] | Electrocardiogram (ECG) interpretation is a cornerstone of cardiac diagnostics. This paper explores a practical approach to enhance ECG image interpretation using the multimodal LLaMA 3.2 model. We used a parameter-efficient fine-tuning strategy, Low-Rank Adaptation (LoRA), specifically designed to boost the model's ab... | {
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2501.18671 | Machine Learning Strategies for Parkinson Tremor Classification Using
Wearable Sensor Data | [
"cs.LG",
"eess.SP"
] | Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accuracy, particularly by leveraging wearable sensor data. This survey comprehensively reviews current ML ... | {
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2501.18672 | Drag Your Gaussian: Effective Drag-Based Editing with Score Distillation
for 3D Gaussian Splatting | [
"cs.GR",
"cs.CV"
] | Recent advancements in 3D scene editing have been propelled by the rapid development of generative models. Existing methods typically utilize generative models to perform text-guided editing on 3D representations, such as 3D Gaussian Splatting (3DGS). However, these methods are often limited to texture modifications an... | {
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2501.18674 | Unpaired Translation of Point Clouds for Modeling Detector Response | [
"cs.CV",
"cs.LG",
"nucl-ex"
] | Modeling detector response is a key challenge in time projection chambers. We cast this problem as an unpaired point cloud translation task, between data collected from simulations and from experimental runs. Effective translation can assist with both noise rejection and the construction of high-fidelity simulators. Bu... | {
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2501.18691 | Regularized second-order optimization of tensor-network Born machines | [
"cs.LG",
"quant-ph"
] | Tensor-network Born machines (TNBMs) are quantum-inspired generative models for learning data distributions. Using tensor-network contraction and optimization techniques, the model learns an efficient representation of the target distribution, capable of capturing complex correlations with a compact parameterization. D... | {
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2501.18698 | Human Re-ID Meets LVLMs: What can we expect? | [
"cs.CV"
] | Large vision-language models (LVLMs) have been regarded as a breakthrough advance in an astoundingly variety of tasks, from content generation to virtual assistants and multimodal search or retrieval. However, for many of these applications, the performance of these methods has been widely criticized, particularly when... | {
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2501.18699 | STAN: Smooth Transition Autoregressive Networks | [
"cs.LG"
] | Traditional Smooth Transition Autoregressive (STAR) models offer an effective way to model these dynamics through smooth regime changes based on specific transition variables. In this paper, we propose a novel approach by drawing an analogy between STAR models and a multilayer neural network architecture. Our proposed ... | {
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2501.18707 | Hierarchical Multi-field Representations for Two-Stage E-commerce
Retrieval | [
"cs.IR"
] | Dense retrieval methods typically target unstructured text data represented as flat strings. However, e-commerce catalogs often include structured information across multiple fields, such as brand, title, and description, which contain important information potential for retrieval systems. We present Cascading Hierarch... | {
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2501.18708 | Combining physics-based and data-driven models: advancing the frontiers
of research with Scientific Machine Learning | [
"math.NA",
"cs.LG",
"cs.NA",
"physics.comp-ph"
] | Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at hand, subsequent mathematical formulation, and numerical approx... | {
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2501.18712 | Invisible Traces: Using Hybrid Fingerprinting to identify underlying
LLMs in GenAI Apps | [
"cs.LG",
"cs.CR"
] | Fingerprinting refers to the process of identifying underlying Machine Learning (ML) models of AI Systemts, such as Large Language Models (LLMs), by analyzing their unique characteristics or patterns, much like a human fingerprint. The fingerprinting of Large Language Models (LLMs) has become essential for ensuring the... | {
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2501.18715 | chebgreen: Learning and Interpolating Continuous Empirical Green's
Functions from Data | [
"cs.LG",
"cs.NA",
"math.NA"
] | In this work, we present a mesh-independent, data-driven library, chebgreen, to mathematically model one-dimensional systems, possessing an associated control parameter, and whose governing partial differential equation is unknown. The proposed method learns an Empirical Green's Function for the associated, but hidden,... | {
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2501.18716 | Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and
Data Release | [
"cs.CV",
"cs.LG",
"eess.IV",
"q-bio.NC"
] | The goal of this work was to develop a deep network for whole-head segmentation, including clinical MRIs with abnormal anatomy, and compile the first public benchmark dataset for this purpose. We collected 91 MRIs with volumetric segmentation labels for a diverse set of human subjects (4 normal, 32 traumatic brain inju... | {
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2501.18718 | Distributed Offloading in Multi-Access Edge Computing Systems: A
Mean-Field Perspective | [
"cs.IT",
"cs.MA",
"cs.SY",
"eess.SY",
"math.IT",
"math.OC"
] | Multi-access edge computing (MEC) technology is a promising solution to assist power-constrained IoT devices by providing additional computing resources for time-sensitive tasks. In this paper, we consider the problem of optimal task offloading in MEC systems with due consideration of the timeliness and scalability iss... | {
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2501.18723 | Scaling Policy Gradient Quality-Diversity with Massive Parallelization
via Behavioral Variations | [
"cs.NE",
"cs.AI",
"cs.LG",
"cs.RO"
] | Quality-Diversity optimization comprises a family of evolutionary algorithms aimed at generating a collection of diverse and high-performing solutions. MAP-Elites (ME), a notable example, is used effectively in fields like evolutionary robotics. However, the reliance of ME on random mutations from Genetic Algorithms li... | {
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2501.18724 | Zero-shot Large Language Models for Long Clinical Text Summarization
with Temporal Reasoning | [
"cs.CL"
] | Recent advancements in large language models (LLMs) have shown potential for transforming data processing in healthcare, particularly in understanding complex clinical narratives. This study evaluates the efficacy of zero-shot LLMs in summarizing long clinical texts that require temporal reasoning, a critical aspect fo... | {
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2501.18726 | Strong and Controllable 3D Motion Generation | [
"cs.CV"
] | Human motion generation is a significant pursuit in generative computer vision with widespread applications in film-making, video games, AR/VR, and human-robot interaction. Current methods mainly utilize either diffusion-based generative models or autoregressive models for text-to-motion generation. However, they face ... | {
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2501.18727 | Exploring Audio Editing Features as User-Centric Privacy Defenses
Against Large Language Model(LLM) Based Emotion Inference Attacks | [
"cs.CR",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | The rapid proliferation of speech-enabled technologies, including virtual assistants, video conferencing platforms, and wearable devices, has raised significant privacy concerns, particularly regarding the inference of sensitive emotional information from audio data. Existing privacy-preserving methods often compromise... | {
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2501.18729 | Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human
Motion Demonstrated on Karate Techniques | [
"cs.CV",
"cs.LG"
] | Attribute manipulation deals with the problem of changing individual attributes of a data point or a time series, while leaving all other aspects unaffected. This work focuses on the domain of human motion, more precisely karate movement patterns. To the best of our knowledge, it presents the first success at manipulat... | {
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2501.18731 | Evaluating Spoken Language as a Biomarker for Automated Screening of
Cognitive Impairment | [
"cs.LG",
"cs.CL"
] | Timely and accurate assessment of cognitive impairment is a major unmet need in populations at risk. Alterations in speech and language can be early predictors of Alzheimer's disease and related dementias (ADRD) before clinical signs of neurodegeneration. Voice biomarkers offer a scalable and non-invasive solution for ... | {
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2501.18732 | Optimizing Bidding Curves for Renewable Energy in Two-Settlement
Electricity Markets | [
"eess.SY",
"cs.SY",
"math.OC"
] | Coordination of day-ahead and real-time electricity markets is imperative for cost-effective electricity supply and also to provide efficient incentives for the energy transition. Although stochastic market designs feature the least-cost coordination, they are incompatible with current deterministic markets. This paper... | {
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2501.18733 | Integrating LMM Planners and 3D Skill Policies for Generalizable
Manipulation | [
"cs.RO",
"cs.AI"
] | The recent advancements in visual reasoning capabilities of large multimodal models (LMMs) and the semantic enrichment of 3D feature fields have expanded the horizons of robotic capabilities. These developments hold significant potential for bridging the gap between high-level reasoning from LMMs and low-level control ... | {
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2501.18734 | STaleX: A Spatiotemporal-Aware Adaptive Auto-scaling Framework for
Microservices | [
"cs.SE",
"cs.DC",
"cs.LG",
"cs.SY",
"eess.SY"
] | While cloud environments and auto-scaling solutions have been widely applied to traditional monolithic applications, they face significant limitations when it comes to microservices-based architectures. Microservices introduce additional challenges due to their dynamic and spatiotemporal characteristics, which require ... | {
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2501.18736 | Distillation-Driven Diffusion Model for Multi-Scale MRI
Super-Resolution: Make 1.5T MRI Great Again | [
"eess.IV",
"cs.CV"
] | Magnetic Resonance Imaging (MRI) offers critical insights into microstructural details, however, the spatial resolution of standard 1.5T imaging systems is often limited. In contrast, 7T MRI provides significantly enhanced spatial resolution, enabling finer visualization of anatomical structures. Though this, the high ... | {
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2501.18738 | Examining the Robustness of Large Language Models across Language
Complexity | [
"cs.CL"
] | With the advancement of large language models (LLMs), an increasing number of student models have leveraged LLMs to analyze textual artifacts generated by students to understand and evaluate their learning. These student models typically employ pre-trained LLMs to vectorize text inputs into embeddings and then use the ... | {
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2501.18739 | Neural Graph Pattern Machine | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Graph learning tasks require models to comprehend essential substructure patterns relevant to downstream tasks, such as triadic closures in social networks and benzene rings in molecular graphs. Due to the non-Euclidean nature of graphs, existing graph neural networks (GNNs) rely on message passing to iteratively aggre... | {
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2501.18741 | Synthetic Data Generation for Augmenting Small Samples | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution. Augmentation increases sample size and is seen as a form of regularization that increases the diversity... | {
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2501.18750 | Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity
Recognition in Low-Resource Languages | [
"cs.CL",
"cs.IR"
] | Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for crosslingual NER and can outperform multi... | {
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2501.18753 | INT: Instance-Specific Negative Mining for Task-Generic Promptable
Segmentation | [
"cs.CV"
] | Task-generic promptable image segmentation aims to achieve segmentation of diverse samples under a single task description by utilizing only one task-generic prompt. Current methods leverage the generalization capabilities of Vision-Language Models (VLMs) to infer instance-specific prompts from these task-generic promp... | {
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2501.18756 | A Unified Framework for Entropy Search and Expected Improvement in
Bayesian Optimization | [
"stat.ML",
"cs.LG",
"math.OC"
] | Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In contrast, information-theoretic acquisition functions aim to reduce uncertainty about the function's optimum and are often considered fundam... | {
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2501.18758 | A New Statistical Approach to the Performance Analysis of Vision-based
Localization | [
"cs.CV",
"cs.IT",
"eess.IV",
"math.IT",
"math.ST",
"stat.AP",
"stat.TH"
] | Many modern wireless devices with accurate positioning needs also have access to vision sensors, such as a camera, radar, and Light Detection and Ranging (LiDAR). In scenarios where wireless-based positioning is either inaccurate or unavailable, using information from vision sensors becomes highly desirable for determi... | {
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2501.18761 | Probabilistic Joint Recovery Method for CO$_2$ Plume Monitoring | [
"cs.LG",
"physics.ao-ph"
] | Reducing CO$_2$ emissions is crucial to mitigating climate change. Carbon Capture and Storage (CCS) is one of the few technologies capable of achieving net-negative CO$_2$ emissions. However, predicting fluid flow patterns in CCS remains challenging due to uncertainties in CO$_2$ plume dynamics and reservoir properties... | {
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2501.18766 | Breaking the Fake News Barrier: Deep Learning Approaches in Bangla
Language | [
"cs.CL",
"cs.AI"
] | The rapid development of digital stages has greatly compounded the dispersal of untrue data, dissolving certainty and judgment in society, especially among the Bengali-speaking community. Our ponder addresses this critical issue by presenting an interesting strategy that utilizes a profound learning innovation, particu... | {
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2501.18768 | Diversity By Design: Leveraging Distribution Matching for Offline
Model-Based Optimization | [
"cs.LG",
"cs.AI"
] | The goal of offline model-based optimization (MBO) is to propose new designs that maximize a reward function given only an offline dataset. However, an important desiderata is to also propose a diverse set of final candidates that capture many optimal and near-optimal design configurations. We propose Diversity in Adve... | {
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2501.18769 | One Stack, Diverse Vehicles: Checking Safe Portability of Automated
Driving Software | [
"eess.SY",
"cs.RO",
"cs.SY"
] | Integrating an automated driving software stack into vehicles with variable configuration is challenging, especially due to different hardware characteristics. Further, to provide software updates to a vehicle fleet in the field, the functional safety of every affected configuration has to be ensured. These additional ... | {
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2501.18771 | Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data
Contamination's Impact on Machine Translation | [
"cs.CL",
"cs.AI"
] | Data contamination -- the accidental consumption of evaluation examples within the pre-training data -- can undermine the validity of evaluation benchmarks. In this paper, we present a rigorous analysis of the effects of contamination on language models at 1B and 8B scales on the machine translation task. Starting from... | {
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2501.18773 | Beyond Short Steps in Frank-Wolfe Algorithms | [
"math.OC",
"cs.LG"
] | We introduce novel techniques to enhance Frank-Wolfe algorithms by leveraging function smoothness beyond traditional short steps. Our study focuses on Frank-Wolfe algorithms with step sizes that incorporate primal-dual guarantees, offering practical stopping criteria. We present a new Frank-Wolfe algorithm utilizing an... | {
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2501.18777 | Navigating the Fragrance space Via Graph Generative Models And
Predicting Odors | [
"cs.LG"
] | We explore a suite of generative modelling techniques to efficiently navigate and explore the complex landscapes of odor and the broader chemical space. Unlike traditional approaches, we not only generate molecules but also predict the odor likeliness with ROC AUC score of 0.97 and assign probable odor labels. We corre... | {
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2501.18781 | A consistent diffuse-interface finite element approach to rapid
melt--vapor dynamics in metal additive manufacturing | [
"cs.CE"
] | Metal additive manufacturing via laser-based powder bed fusion (PBF-LB/M) faces performance-critical challenges due to complex melt pool and vapor dynamics, often oversimplified by computational models that neglect crucial aspects, such as vapor jet formation. To address this limitation, we propose a consistent computa... | {
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2501.18782 | PSO-Net: Development of an automated psoriasis assessment system using
attention-based interpretable deep neural networks | [
"eess.IV",
"cs.CV"
] | Psoriasis is a chronic skin condition that requires long-term treatment and monitoring. Although, the Psoriasis Area and Severity Index (PASI) is utilized as a standard measurement to assess psoriasis severity in clinical trials, it has many drawbacks such as (1) patient burden for in-person clinic visits for assessmen... | {
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2501.18783 | RUN: Reversible Unfolding Network for Concealed Object Segmentation | [
"cs.CV"
] | Existing concealed object segmentation (COS) methods frequently utilize reversible strategies to address uncertain regions. However, these approaches are typically restricted to the mask domain, leaving the potential of the RGB domain underexplored. To address this, we propose the Reversible Unfolding Network (RUN), wh... | {
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2501.18784 | LLM-Generated Heuristics for AI Planning: Do We Even Need
Domain-Independence Anymore? | [
"cs.AI"
] | Domain-independent heuristics have long been a cornerstone of AI planning, offering general solutions applicable across a wide range of tasks without requiring domain-specific engineering. However, the advent of large language models (LLMs) presents an opportunity to generate heuristics tailored to specific planning pr... | {
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2501.18786 | Multispectral 3D mapping on a Roman sculpture to study ancient
polychromy | [
"cs.CV",
"eess.IV"
] | Research into the polychromy of Greek and Roman sculptures has surged to explore the hypothesis that ancient sculptures were originally not pristine white but adorned with colors. Multispectral and multimodal imaging techniques have been crucial in studying painted surfaces, revealing polychromies even in traces. In fa... | {
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2501.18788 | Tuning Event Camera Biases Heuristic for Object Detection Applications
in Staring Scenarios | [
"cs.CV",
"math.OC"
] | One of the main challenges in unlocking the potential of neuromorphic cameras, also called 'event cameras', is the development of novel methods that solve the multi-parameter problem of adjusting their bias parameters to accommodate a desired task. Actually, it is very difficult to find in the literature a systematic h... | {
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2501.18790 | Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward
POMDPs with Known Observation Models | [
"cs.LG",
"stat.ML"
] | We tackle average-reward infinite-horizon POMDPs with an unknown transition model but a known observation model, a setting that has been previously addressed in two limiting ways: (i) frequentist methods relying on suboptimal stochastic policies having a minimum probability of choosing each action, and (ii) Bayesian ap... | {
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2501.18792 | Bayesian Optimization with Preference Exploration by Monotonic Neural
Network Ensemble | [
"cs.LG",
"math.OC",
"stat.ML"
] | Many real-world black-box optimization problems have multiple conflicting objectives. Rather than attempting to approximate the entire set of Pareto-optimal solutions, interactive preference learning allows to focus the search on the most relevant subset. However, few previous studies have exploited the fact that utili... | {
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2501.18793 | OT-Transformer: A Continuous-time Transformer Architecture with Optimal
Transport Regularization | [
"cs.LG",
"cs.AI"
] | Transformers have achieved state-of-the-art performance in numerous tasks. In this paper, we propose a continuous-time formulation of transformers. Specifically, we consider a dynamical system whose governing equation is parametrized by transformer blocks. We leverage optimal transport theory to regularize the training... | {
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2501.18794 | Survey and Improvement Strategies for Gene Prioritization with Large
Language Models | [
"q-bio.GN",
"cs.AI"
] | Rare diseases are challenging to diagnose due to limited patient data and genetic diversity. Despite advances in variant prioritization, many cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their effectiveness in diagnosing rare genetic diseases has not been assessed. ... | {
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2501.18795 | Rope to Nope and Back Again: A New Hybrid Attention Strategy | [
"cs.CL"
] | Long-context large language models (LLMs) have achieved remarkable advancements, driven by techniques like Rotary Position Embedding (RoPE) (Su et al., 2023) and its extensions (Chen et al., 2023; Liu et al., 2024c; Peng et al., 2023). By adjusting RoPE parameters and incorporating training data with extended contexts,... | {
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2501.18796 | Designing Kresling Origami for Personalised Wrist Orthosis | [
"cs.RO"
] | The wrist plays a pivotal role in facilitating motion dexterity and hand functions. Wrist orthoses, from passive braces to active exoskeletons, provide an effective solution for the assistance and rehabilitation of motor abilities. However, the type of motions facilitated by currently available orthoses is limited, wit... | {
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2501.18797 | Compositional Generalization Requires More Than Disentangled
Representations | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Composition-the ability to generate myriad variations from finite means-is believed to underlie powerful generalization. However, compositional generalization remains a key challenge for deep learning. A widely held assumption is that learning disentangled (factorized) representations naturally supports this kind of ex... | {
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2501.18799 | A General-Purpose Neuromorphic Sensor based on Spiketrum Algorithm:
Hardware Details and Real-life Applications | [
"eess.SP",
"cs.SY",
"eess.AS",
"eess.SY"
] | Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm, enabling energy-efficient data processing through spike-based information transmission. Despite notable advancements in hardware for SNNs, spike encoding has largely remained software-dependent, limiting efficiency. This paper addresse... | {
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2501.18801 | Every Image Listens, Every Image Dances: Music-Driven Image Animation | [
"cs.CV",
"cs.AI"
] | Image animation has become a promising area in multimodal research, with a focus on generating videos from reference images. While prior work has largely emphasized generic video generation guided by text, music-driven dance video generation remains underexplored. In this paper, we introduce MuseDance, an innovative en... | {
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2501.18802 | Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Quadrotors can carry slung loads to hard-to-reach locations at high speed. Since a single quadrotor has limited payload capacities, using a team of quadrotors to collaboratively manipulate a heavy object is a scalable and promising solution. However, existing control algorithms for multi-lifting systems only enable low... | {
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2501.18803 | Deceptive Sequential Decision-Making via Regularized Policy Optimization | [
"cs.LG",
"math.OC"
] | Autonomous systems are increasingly expected to operate in the presence of adversaries, though an adversary may infer sensitive information simply by observing a system, without even needing to interact with it. Therefore, in this work we present a deceptive decision-making framework that not only conceals sensitive in... | {
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2501.18804 | Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric
Diffusion | [
"cs.CV",
"cs.LG"
] | Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-view consistent scene appearance and geometry. In this paper we introduce MVGD, a diffusion-based architecture capable of direct pixel-level g... | {
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2501.18805 | Are Representation Disentanglement and Interpretability Linked in
Recommendation Models? A Critical Review and Reproducibility Study | [
"cs.IR"
] | Unsupervised learning of disentangled representations has been closely tied to enhancing the representation intepretability of Recommender Systems (RSs). This has been achieved by making the representation of individual features more distinctly separated, so that it is easier to attribute the contribution of features t... | {
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2501.18808 | Learning Hamiltonian Dynamics with Bayesian Data Assimilation | [
"cs.LG",
"cs.RO",
"cs.SY",
"eess.SY"
] | In this paper, we develop a neural network-based approach for time-series prediction in unknown Hamiltonian dynamical systems. Our approach leverages a surrogate model and learns the system dynamics using generalized coordinates (positions) and their conjugate momenta while preserving a constant Hamiltonian. To further... | {
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2501.18812 | Estimating the Probability of Sampling a Trained Neural Network at
Random | [
"cs.LG"
] | We present an algorithm for estimating the probability mass, under a Gaussian or uniform prior, of a region in neural network parameter space corresponding to a particular behavior, such as achieving test loss below some threshold. When the prior is uniform, this problem is equivalent to measuring the volume of a regio... | {
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2501.18815 | An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D
Brain Images | [
"eess.IV",
"cs.AI",
"cs.CV"
] | We developed a generative patch based 3D image registration model that can register very high resolution images obtained from a biochemical process name tissue clearing. Tissue clearing process removes lipids and fats from the tissue and make the tissue transparent. When cleared tissues are imaged with Light-sheet fluo... | {
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2501.18816 | Large Language Models as Common-Sense Heuristics | [
"cs.CL",
"cs.AI",
"cs.LG"
] | While systems designed for solving planning tasks vastly outperform Large Language Models (LLMs) in this domain, they usually discard the rich semantic information embedded within task descriptions. In contrast, LLMs possess parametrised knowledge across a wide range of topics, enabling them to leverage the natural lan... | {
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2501.18817 | Bridging the Reasoning Gap: Small LLMs Can Plan with Generalised
Strategies | [
"cs.AI",
"cs.CL"
] | Recent advancements in the reasoning skills of Large Language Models (LLMs) demonstrate an increase in the ability of LLMs to solve simple planning tasks. However, as long as the driving force behind improved reasoning capability is the size and complexity of the model, the financial and computational costs associated ... | {
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2501.18821 | An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for
Anomaly Detection in CAN Bus | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Autonomous vehicles represent a revolutionary advancement driven by the integration of artificial intelligence within intelligent transportation systems. However, they remain vulnerable due to the absence of robust security mechanisms in the Controller Area Network (CAN) bus. In order to mitigate the security issue, ma... | {
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2501.18823 | Transcoders Beat Sparse Autoencoders for Interpretability | [
"cs.LG"
] | Sparse autoencoders (SAEs) extract human-interpretable features from deep neural networks by transforming their activations into a sparse, higher dimensional latent space, and then reconstructing the activations from these latents. Transcoders are similar to SAEs, but they are trained to reconstruct the output of a com... | {
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2501.18824 | Memory-Efficient Fine-Tuning of Transformers via Token Selection | [
"cs.CL",
"cs.LG"
] | Fine-tuning provides an effective means to specialize pre-trained models for various downstream tasks. However, fine-tuning often incurs high memory overhead, especially for large transformer-based models, such as LLMs. While existing methods may reduce certain parts of the memory required for fine-tuning, they still r... | {
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2501.18826 | Structural Embedding Projection for Contextual Large Language Model
Inference | [
"cs.CL"
] | Structured embedding transformations offer a promising approach for enhancing the efficiency and coherence of language model inference. The introduction of Structural Embedding Projection (SEP) provides a mechanism for refining token representations through projection matrices that integrate hierarchical and relational... | {
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2501.18834 | Pitfalls of defacing whole-head MRI: re-identification risk with
diffusion models and compromised research potential | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Defacing is often applied to head magnetic resonance image (MRI) datasets prior to public release to address privacy concerns. The alteration of facial and nearby voxels has provoked discussions about the true capability of these techniques to ensure privacy as well as their impact on downstream tasks. With advancement... | {
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2501.18835 | Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt
Disease and Coconut Caterpillar Infestation using Deep Learning-based Image
Processing Techniques | [
"cs.CV"
] | Global Coconut (Cocos nucifera (L.)) cultivation faces significant challenges, including yield loss, due to pest and disease outbreaks. In particular, Weligama Coconut Leaf Wilt Disease (WCWLD) and Coconut Caterpillar Infestation (CCI) damage coconut trees, causing severe coconut production loss in Sri Lanka and nearby... | {
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2501.18836 | Transfer Learning for Nonparametric Contextual Dynamic Pricing | [
"cs.LG",
"math.ST",
"stat.ME",
"stat.TH"
] | Dynamic pricing strategies are crucial for firms to maximize revenue by adjusting prices based on market conditions and customer characteristics. However, designing optimal pricing strategies becomes challenging when historical data are limited, as is often the case when launching new products or entering new markets. ... | {
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2501.18837 | Constitutional Classifiers: Defending against Universal Jailbreaks
across Thousands of Hours of Red Teaming | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | Large language models (LLMs) are vulnerable to universal jailbreaks-prompting strategies that systematically bypass model safeguards and enable users to carry out harmful processes that require many model interactions, like manufacturing illegal substances at scale. To defend against these attacks, we introduce Constit... | {
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2501.18838 | Partially Rewriting a Transformer in Natural Language | [
"cs.LG",
"cs.CL"
] | The greatest ambition of mechanistic interpretability is to completely rewrite deep neural networks in a format that is more amenable to human understanding, while preserving their behavior and performance. In this paper, we attempt to partially rewrite a large language model using simple natural language explanations.... | {
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2501.18839 | Social Cyber Geographical Worldwide Inventory of Bots | [
"cs.SI"
] | Social Cyber Geography is the space in the digital cyber realm that is produced through social relations. Communication in the social media ecosystem happens not only because of human interactions, but is also fueled by algorithmically controlled bot agents. Most studies have not looked at the social cyber geography of... | {
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2501.18841 | Trading Inference-Time Compute for Adversarial Robustness | [
"cs.LG",
"cs.CR"
] | We conduct experiments on the impact of increasing inference-time compute in reasoning models (specifically OpenAI o1-preview and o1-mini) on their robustness to adversarial attacks. We find that across a variety of attacks, increased inference-time compute leads to improved robustness. In many cases (with important ex... | {
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2501.18845 | Text Data Augmentation for Large Language Models: A Comprehensive Survey
of Methods, Challenges, and Opportunities | [
"cs.CL"
] | The increasing size and complexity of pre-trained language models have demonstrated superior performance in many applications, but they usually require large training datasets to be adequately trained. Insufficient training sets could unexpectedly make the model overfit and fail to cope with complex tasks. Large langua... | {
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2501.18848 | Reinforcement Learning of Flexible Policies for Symbolic Instructions
with Adjustable Mapping Specifications | [
"cs.RO"
] | Symbolic task representation is a powerful tool for encoding human instructions and domain knowledge. Such instructions guide robots to accomplish diverse objectives and meet constraints through reinforcement learning (RL). Most existing methods are based on fixed mappings from environmental states to symbols. However,... | {
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2501.18850 | Equivariant Hypergraph Diffusion for Crystal Structure Prediction | [
"cs.CE"
] | Crystal Structure Prediction (CSP) remains a fundamental challenge with significant implications for the development of new materials and the advancement of various scientific disciplines. Recent developments have shown that generative models, particularly diffusion models, hold great promise for CSP. However, traditio... | {
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2501.18851 | Project-and-Fuse: Improving RGB-D Semantic Segmentation via Graph
Convolution Networks | [
"cs.CV"
] | Most existing RGB-D semantic segmentation methods focus on the feature level fusion, including complex cross-modality and cross-scale fusion modules. However, these methods may cause misalignment problem in the feature fusion process and counter-intuitive patches in the segmentation results. Inspired by the popular pix... | {
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2501.18852 | Tracking Error Based Fault Tolerant Scheme for Marine Vehicles with
Thruster Redundancy | [
"eess.SY",
"cs.SY"
] | This paper proposes an active model-based fault and failure tolerant control scheme for a class of marine vehicles with thruster redundancy. Unlike widely used state and parameter estimation methods, where the estimation errors are utilized to generate residual, in this paper we directly apply the trajectory tracking e... | {
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2501.18853 | Non-Asymptotic Analysis of Subspace Identification for Stochastic
Systems Using Multiple Trajectories | [
"eess.SY",
"cs.SY"
] | This paper is concerned with the analysis of identification errors for $n$-dimensional discrete-time Linear Time-Invariant (LTI) systems with $m$ outputs and no external inputs, using Subspace Identification Methods (SIM) with finite sample data. We provide non-asymptotic high-probability upper bounds for matrices $A,C... | {
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} |
2501.18855 | FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with
General Features Transfered from SAM | [
"cs.CV"
] | Automatic crack segmentation is a cornerstone technology for intelligent visual perception modules in road safety maintenance and structural integrity systems. Existing deep learning models and ``pre-training + fine-tuning'' paradigms often face challenges of limited adaptability in resource-constrained environments an... | {
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} |
2501.18858 | BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language
Model Reasoning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, yet generating reliable reasoning processes remains a significant challenge. We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model incorporating latent thinking process... | {
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} |
2501.18859 | A Deep Spatio-Temporal Architecture for Dynamic Effective Connectivity
Network Analysis Based on Dynamic Causal Discovery | [
"cs.LG"
] | Dynamic effective connectivity networks (dECNs) reveal the changing directed brain activity and the dynamic causal influences among brain regions, which facilitate the identification of individual differences and enhance the understanding of human brain. Although the existing causal discovery methods have shown promisi... | {
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} |
2501.18862 | Scalable Distributed Reproduction Numbers of Network Epidemics with
Differential Privacy | [
"eess.SY",
"cs.SY"
] | Reproduction numbers are widely used for the estimation and prediction of epidemic spreading processes over networks. However, conventional reproduction numbers of an overall network do not indicate where an epidemic is spreading. Therefore, we propose a novel notion of local distributed reproduction numbers to capture... | {
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} |
2501.18863 | Adaptivity and Convergence of Probability Flow ODEs in Diffusion
Generative Models | [
"stat.ML",
"cs.LG"
] | Score-based generative models, which transform noise into data by learning to reverse a diffusion process, have become a cornerstone of modern generative AI. This paper contributes to establishing theoretical guarantees for the probability flow ODE, a widely used diffusion-based sampler known for its practical efficien... | {
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} |
2501.18864 | Test-time Loss Landscape Adaptation for Zero-Shot Generalization in
Vision-Language Models | [
"cs.CV"
] | Test-time adaptation of pre-trained vision-language models has emerged as a technique for tackling distribution shifts during the test time. Although existing methods, especially those based on Test-time Prompt Tuning (TPT), have shown promising results, their high computational cost associated with parameter optimizat... | {
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} |
2501.18865 | REG: Rectified Gradient Guidance for Conditional Diffusion Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Guidance techniques are simple yet effective for improving conditional generation in diffusion models. Albeit their empirical success, the practical implementation of guidance diverges significantly from its theoretical motivation. In this paper, we reconcile this discrepancy by replacing the scaled marginal distributi... | {
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} |
2501.18867 | UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent | [
"cs.CV",
"cs.AI"
] | Recent advancements in Vision-Language-Action (VLA) models have leveraged pre-trained Vision-Language Models (VLMs) to improve the generalization capabilities. VLMs, typically pre-trained on vision-language understanding tasks, provide rich semantic knowledge and reasoning abilities. However, prior research has shown t... | {
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} |
2501.18870 | Continuous-Time Analysis of Federated Averaging | [
"cs.LG",
"cs.DC",
"math.OC"
] | Federated averaging (FedAvg) is a popular algorithm for horizontal federated learning (FL), where samples are gathered across different clients and are not shared with each other or a central server. Extensive convergence analysis of FedAvg exists for the discrete iteration setting, guaranteeing convergence for a range... | {
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} |
2501.18871 | Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling | [
"cs.LG",
"stat.ML"
] | Inspired by the ubiquitous use of differential equations to model continuous dynamics across diverse scientific and engineering domains, we propose a novel and intuitive approach to continuous sequence modeling. Our method interprets time-series data as \textit{discrete samples from an underlying continuous dynamical s... | {
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} |
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