id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.16757 | ITVTON:Virtual Try-On Diffusion Transformer Model Based on Integrated
Image and Text | [
"cs.CV"
] | Recent advancements in virtual fitting for characters and clothing have leveraged diffusion models to improve the realism of garment fitting. However, challenges remain in handling complex scenes and poses, which can result in unnatural garment fitting and poorly rendered intricate patterns. In this work, we introduce ... | {
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2501.16758 | Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow
Management | [
"cs.LG",
"cs.DC",
"eess.SP"
] | Efficient management of traffic flow in urban environments presents a significant challenge, exacerbated by dynamic changes and the sheer volume of data generated by modern transportation networks. Traditional centralized traffic management systems often struggle with scalability and privacy concerns, hindering their e... | {
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2501.16759 | Are Joins over LSM-trees Ready: Take RocksDB as an Example | [
"cs.DB"
] | LSM-tree-based data stores are widely adopted in industries for their excellent performance. As data scales increase, disk-based join operations become indispensable yet costly for the database, making the selection of suitable join methods crucial for system optimization. Current LSM-based stores generally adhere to c... | {
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2501.16760 | AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies | [
"cs.CV",
"cs.LG"
] | Automated interpretation of seismic images using deep learning methods is challenging because of the limited availability of training data. Few-shot learning is a suitable learning paradigm in such scenarios due to its ability to adapt to a new task with limited supervision (small training budget). Existing few-shot se... | {
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2501.16762 | Rate-Distortion under Neural Tracking of Speech: A Directed Redundancy
Approach | [
"cs.IT",
"math.IT"
] | The data acquired at different scalp EEG electrodes when human subjects are exposed to speech stimuli are highly redundant. The redundancy is partly due to volume conduction effects and partly due to localized regions of the brain synchronizing their activity in response to the stimuli. In a competing talker scenario, ... | {
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2501.16764 | DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian
Splat Generation | [
"cs.CV"
] | Recent advancements in 3D content generation from text or a single image struggle with limited high-quality 3D datasets and inconsistency from 2D multi-view generation. We introduce DiffSplat, a novel 3D generative framework that natively generates 3D Gaussian splats by taming large-scale text-to-image diffusion models... | {
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2501.16767 | Target-driven Self-Distillation for Partial Observed Trajectories
Forecasting | [
"cs.CV"
] | Accurate prediction of future trajectories of traffic agents is essential for ensuring safe autonomous driving. However, partially observed trajectories can significantly degrade the performance of even state-of-the-art models. Previous approaches often rely on knowledge distillation to transfer features from fully obs... | {
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2501.16768 | Towards the Generalization of Multi-view Learning: An
Information-theoretical Analysis | [
"stat.ML",
"cs.LG"
] | Multiview learning has drawn widespread attention for its efficacy in leveraging cross-view consensus and complementarity information to achieve a comprehensive representation of data. While multi-view learning has undergone vigorous development and achieved remarkable success, the theoretical understanding of its gene... | {
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2501.16769 | Beyond-Labels: Advancing Open-Vocabulary Segmentation With
Vision-Language Models | [
"cs.CV"
] | Self-supervised learning can resolve numerous image or linguistic processing problems when effectively trained. This study investigated simple yet efficient methods for adapting previously learned foundation models for open-vocabulary semantic segmentation tasks. Our research proposed "Beyond-Labels," a lightweight tra... | {
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2501.16778 | FlexMotion: Lightweight, Physics-Aware, and Controllable Human Motion
Generation | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Lightweight, controllable, and physically plausible human motion synthesis is crucial for animation, virtual reality, robotics, and human-computer interaction applications. Existing methods often compromise between computational efficiency, physical realism, or spatial controllability. We propose FlexMotion, a novel fr... | {
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2501.16783 | A Stochastic Dynamical Theory of LLM Self-Adversariality: Modeling
Severity Drift as a Critical Process | [
"cs.CL",
"cs.AI",
"nlin.AO"
] | This paper introduces a continuous-time stochastic dynamical framework for understanding how large language models (LLMs) may self-amplify latent biases or toxicity through their own chain-of-thought reasoning. The model posits an instantaneous "severity" variable $x(t) \in [0,1]$ evolving under a stochastic differenti... | {
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2501.16786 | Exploring the Role of Explicit Temporal Modeling in Multimodal Large
Language Models for Video Understanding | [
"cs.CV",
"cs.CL"
] | Applying Multimodal Large Language Models (MLLMs) to video understanding presents significant challenges due to the need to model temporal relations across frames. Existing approaches adopt either implicit temporal modeling, relying solely on the LLM decoder, or explicit temporal modeling, employing auxiliary temporal ... | {
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2501.16787 | Dynamic Hypergraph Representation for Bone Metastasis Cancer Analysis | [
"cs.CV"
] | Bone metastasis analysis is a significant challenge in pathology and plays a critical role in determining patient quality of life and treatment strategies. The microenvironment and specific tissue structures are essential for pathologists to predict the primary bone cancer origins and primary bone cancer subtyping. By ... | {
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2501.16790 | Exponential Family Attention | [
"stat.ML",
"cs.LG"
] | The self-attention mechanism is the backbone of the transformer neural network underlying most large language models. It can capture complex word patterns and long-range dependencies in natural language. This paper introduces exponential family attention (EFA), a probabilistic generative model that extends self-attenti... | {
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2501.16794 | Algorithm for Automatic Legislative Text Consolidation | [
"cs.CL"
] | This study introduces a method for automating the consolidation process in a legal context, a time-consuming task traditionally performed by legal professionals. We present a generative approach that processes legislative texts to automatically apply amendments. Our method employs light quantized generative model, fine... | {
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2501.16800 | DIRIGENt: End-To-End Robotic Imitation of Human Demonstrations Based on
a Diffusion Model | [
"cs.RO",
"cs.AI"
] | There has been substantial progress in humanoid robots, with new skills continuously being taught, ranging from navigation to manipulation. While these abilities may seem impressive, the teaching methods often remain inefficient. To enhance the process of teaching robots, we propose leveraging a mechanism effectively u... | {
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2501.16803 | RG-Attn: Radian Glue Attention for Multi-modality Multi-agent
Cooperative Perception | [
"cs.RO",
"cs.CV",
"cs.NI",
"eess.IV"
] | Cooperative perception offers an optimal solution to overcome the perception limitations of single-agent systems by leveraging Vehicle-to-Everything (V2X) communication for data sharing and fusion across multiple agents. However, most existing approaches focus on single-modality data exchange, limiting the potential of... | {
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2501.16811 | Not Every Patch is Needed: Towards a More Efficient and Effective
Backbone for Video-based Person Re-identification | [
"cs.CV"
] | This paper proposes a new effective and efficient plug-and-play backbone for video-based person re-identification (ReID). Conventional video-based ReID methods typically use CNN or transformer backbones to extract deep features for every position in every sampled video frame. Here, we argue that this exhaustive feature... | {
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2501.16813 | Multimodal Magic Elevating Depression Detection with a Fusion of Text
and Audio Intelligence | [
"cs.CL",
"cs.SD",
"eess.AS"
] | This study proposes an innovative multimodal fusion model based on a teacher-student architecture to enhance the accuracy of depression classification. Our designed model addresses the limitations of traditional methods in feature fusion and modality weight allocation by introducing multi-head attention mechanisms and ... | {
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2501.16817 | Enhancing Non-Intrusive Load Monitoring with Features Extracted by
Independent Component Analysis | [
"eess.SY",
"cs.LG",
"cs.SY"
] | In this paper, a novel neural network architecture is proposed to address the challenges in energy disaggregation algorithms. These challenges include the limited availability of data and the complexity of disaggregating a large number of appliances operating simultaneously. The proposed model utilizes independent comp... | {
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2501.16823 | Phase Noise Resilient Codebook Design for Sparse Code Multiple Access | [
"cs.IT",
"math.IT"
] | Sparse code multiple access (SCMA) is a promising technique for future machine type communication systems due to its superior spectral efficiency and capability for supporting massive connectivity. This paper proposes a novel class of sparse codebooks to improve the error rate performance of SCMA in the presence of pha... | {
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2501.16825 | Can Transformers Learn Full Bayesian Inference in Context? | [
"cs.LG"
] | Transformers have emerged as the dominant architecture in the field of deep learning, with a broad range of applications and remarkable in-context learning (ICL) capabilities. While not yet fully understood, ICL has already proved to be an intriguing phenomenon, allowing transformers to learn in context -- without requ... | {
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2501.16828 | Late Breaking Results: Energy-Efficient Printed Machine Learning
Classifiers with Sequential SVMs | [
"cs.LG",
"cs.SY",
"eess.IV",
"eess.SY"
] | Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers are limited by high power, area, and energy overheads, which restricts the realization of battery-powe... | {
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2501.16830 | Statistical Analysis of Risk Assessment Factors and Metrics to Evaluate
Radicalisation in Twitter | [
"cs.SI",
"cs.CY",
"cs.LG"
] | Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are being used to radicalise people. In this paper, we study the performance of a se... | {
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2501.16831 | Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil
Temperature Estimation | [
"cs.LG"
] | Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. Therefore, monitoring the temperature of a power transformer is fundamental to ensure a long-term operational life. Models presented in the I... | {
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2501.16836 | Misspellings in Natural Language Processing: A survey | [
"cs.CL",
"cs.AI"
] | This survey provides an overview of the challenges of misspellings in natural language processing (NLP). While often unintentional, misspellings have become ubiquitous in digital communication, especially with the proliferation of Web 2.0, user-generated content, and informal text mediums such as social media, blogs, a... | {
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2501.16838 | Spread Codes from Abelian non-cyclic groups | [
"cs.IT",
"math.IT"
] | Given the finite field $\mathbb{F}_{q}$, for a prime power $q$, in this paper we present a way of constructing spreads of $\mathbb{F}_{q}^{n}$. They will arise as orbits under the action of an Abelian non-cyclic group. First, we construct a family of orbit codes of maximum distance using this group, and then we complet... | {
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2501.16839 | Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans | [
"cs.LG",
"math.PR"
] | Among generative neural models, flow matching techniques stand out for their simple applicability and good scaling properties. Here, velocity fields of curves connecting a simple latent and a target distribution are learned. Then the corresponding ordinary differential equation can be used to sample from a target distr... | {
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2501.16841 | Toward Explainable NILM: Real-Time Event-Based NILM Framework for
High-Frequency Data | [
"eess.SY",
"cs.SY"
] | Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption. However, its adaptability is hindered by the lack of transparency and explainability. To address this challenge, this paper presents an explainable, real-time, event-based NILM framework ... | {
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2501.16847 | Optimization and Learning in Open Multi-Agent Systems | [
"math.OC",
"cs.LG",
"cs.MA",
"cs.SY",
"eess.SY"
] | Modern artificial intelligence relies on networks of agents that collect data, process information, and exchange it with neighbors to collaboratively solve optimization and learning problems. This article introduces a novel distributed algorithm to address a broad class of these problems in "open networks", where the n... | {
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2501.16848 | Hybrid Phenology Modeling for Predicting Temperature Effects on Tree
Dormancy | [
"cs.LG"
] | Biophysical models offer valuable insights into climate-phenology relationships in both natural and agricultural settings. However, there are substantial structural discrepancies across models which require site-specific recalibration, often yielding inconsistent predictions under similar climate scenarios. Machine lea... | {
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2501.16863 | HD-CB: The First Exploration of Hyperdimensional Computing for
Contextual Bandits Problems | [
"cs.LG"
] | Hyperdimensional Computing (HDC), also known as Vector Symbolic Architectures, is a computing paradigm that combines the strengths of symbolic reasoning with the efficiency and scalability of distributed connectionist models in artificial intelligence. HDC has recently emerged as a promising alternative for performing ... | {
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2501.16865 | JRE-L: Journalist, Reader, and Editor LLMs in the Loop for Science
Journalism for the General Audience | [
"cs.CL"
] | Science journalism reports current scientific discoveries to non-specialists, aiming to enable public comprehension of the state of the art. This task is challenging as the audience often lacks specific knowledge about the presented research. We propose a JRE-L framework that integrates three LLMs mimicking the writing... | {
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2501.16867 | Empirical modeling and hybrid machine learning framework for nucleate
pool boiling on microchannel structured surfaces | [
"physics.app-ph",
"cs.LG"
] | Micro-structured surfaces influence nucleation characteristics and bubble dynamics besides increasing the heat transfer surface area, thus enabling efficient nucleate boiling heat transfer. Modeling the pool boiling heat transfer characteristics of these surfaces under varied conditions is essential in diverse applicat... | {
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2501.16868 | Event-Based Adaptive Koopman Framework for Optic Flow-Guided Landing on
Moving Platforms | [
"eess.SY",
"cs.RO",
"cs.SY"
] | This paper presents an optic flow-guided approach for achieving soft landings by resource-constrained unmanned aerial vehicles (UAVs) on dynamic platforms. An offline data-driven linear model based on Koopman operator theory is developed to describe the underlying (nonlinear) dynamics of optic flow output obtained from... | {
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2501.16870 | Experimenting with Affective Computing Models in Video Interviews with
Spanish-speaking Older Adults | [
"cs.CV"
] | Understanding emotional signals in older adults is crucial for designing virtual assistants that support their well-being. However, existing affective computing models often face significant limitations: (1) limited availability of datasets representing older adults, especially in non-English-speaking populations, and ... | {
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2501.16875 | Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal
Association and Frequency Domain Analysis | [
"cs.SE",
"cs.LG"
] | Anomaly detection is crucial for ensuring the stability and reliability of web service systems. Logs and metrics contain multiple information that can reflect the system's operational state and potential anomalies. Thus, existing anomaly detection methods use logs and metrics to detect web service systems' anomalies th... | {
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2501.16879 | Ultra-high resolution multimodal MRI dense labelled holistic brain atlas | [
"eess.IV",
"cs.CV"
] | In this paper, we introduce holiAtlas, a holistic, multimodal and high-resolution human brain atlas. This atlas covers different levels of details of the human brain anatomy, from the organ to the substructure level, using a new dense labelled protocol generated from the fusion of multiple local protocols at different ... | {
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2501.16884 | Irony Detection, Reasoning and Understanding in Zero-shot Learning | [
"cs.CL",
"cs.AI"
] | Irony is a powerful figurative language (FL) on social media that can potentially mislead various NLP tasks, such as recommendation systems, misinformation checks, and sentiment analysis. Understanding the implicit meaning of this kind of subtle language is essential to mitigate irony's negative impact on NLP tasks. Ho... | {
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2501.16888 | Secure Federated Graph-Filtering for Recommender Systems | [
"cs.IR",
"cs.CR"
] | Recommender systems often rely on graph-based filters, such as normalized item-item adjacency matrices and low-pass filters. While effective, the centralized computation of these components raises concerns about privacy, security, and the ethical use of user data. This work proposes two decentralized frameworks for sec... | {
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2501.16889 | Extending Information Bottleneck Attribution to Video Sequences | [
"cs.CV",
"cs.AI"
] | We introduce VIBA, a novel approach for explainable video classification by adapting Information Bottlenecks for Attribution (IBA) to video sequences. While most traditional explainability methods are designed for image models, our IBA framework addresses the need for explainability in temporal models used for video an... | {
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2501.16894 | DBSCAN in domains with periodic boundary conditions | [
"cs.LG",
"physics.comp-ph",
"physics.flu-dyn"
] | Many scientific problems involve data that is embedded in a space with periodic boundary conditions. This can for instance be related to an inherent cyclic or rotational symmetry in the data or a spatially extended periodicity. When analyzing such data, well-tailored methods are needed to obtain efficient approaches th... | {
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2501.16896 | Frequency Matters: Explaining Biases of Face Recognition in the
Frequency Domain | [
"cs.CV"
] | Face recognition (FR) models are vulnerable to performance variations across demographic groups. The causes for these performance differences are unclear due to the highly complex deep learning-based structure of face recognition models. Several works aimed at exploring possible roots of gender and ethnicity bias, iden... | {
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2501.16899 | RDMM: Fine-Tuned LLM Models for On-Device Robotic Decision Making with
Enhanced Contextual Awareness in Specific Domains | [
"cs.RO",
"cs.AI"
] | Large language models (LLMs) represent a significant advancement in integrating physical robots with AI-driven systems. We showcase the capabilities of our framework within the context of the real-world household competition. This research introduces a framework that utilizes RDMM (Robotics Decision-Making Models), whi... | {
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2501.16900 | RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for
Rainfall Patterns Prediction | [
"cs.LG"
] | Rainfall prediction remains a persistent challenge due to the highly nonlinear and complex nature of meteorological data. Existing approaches lack systematic utilization of grid search for optimal hyperparameter tuning, relying instead on heuristic or manual selection, frequently resulting in sub-optimal results. Addit... | {
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2501.16902 | Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks | [
"cs.IR"
] | Recent advancements in dense retrieval have introduced vision-language model (VLM)-based retrievers, such as DSE and ColPali, which leverage document screenshots embedded as vectors to enable effective search and offer a simplified pipeline over traditional text-only methods. In this study, we propose three pixel poiso... | {
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2501.16904 | Adversarial Masked Autoencoder Purifier with Defense Transferability | [
"cs.CV"
] | The study of adversarial defense still struggles to combat with advanced adversarial attacks. In contrast to most prior studies that rely on the diffusion model for test-time defense to remarkably increase the inference time, we propose Masked AutoEncoder Purifier (MAEP), which integrates Masked AutoEncoder (MAE) into ... | {
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2501.16912 | A Unified Evaluation Framework for Epistemic Predictions | [
"cs.LG"
] | Predictions of uncertainty-aware models are diverse, ranging from single point estimates (often averaged over prediction samples) to predictive distributions, to set-valued or credal-set representations. We propose a novel unified evaluation framework for uncertainty-aware classifiers, applicable to a wide range of mod... | {
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2501.16915 | Understanding the Effect of Long-Term Memory Model Parameters in
Pole-Zero Identification for Stability Analysis of Power Amplifiers | [
"eess.SY",
"cs.SY"
] | Understanding the nature of potential instabilities is indispensable for the stabilization of power amplifiers. Pole-zero identification is one of the techniques that can be used to determine the stability of a design in large-signal operation. In this work, the possible presence of poles at the fundamental frequency l... | {
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2501.16917 | B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM
Pruning | [
"cs.CV"
] | Face detection is a computer vision application that increasingly demands lightweight models to facilitate deployment on devices with limited computational resources. Neural network pruning is a promising technique that can effectively reduce network size without significantly affecting performance. In this work, we pr... | {
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2501.16918 | On Rollouts in Model-Based Reinforcement Learning | [
"cs.LG"
] | Model-based reinforcement learning (MBRL) seeks to enhance data efficiency by learning a model of the environment and generating synthetic rollouts from it. However, accumulated model errors during these rollouts can distort the data distribution, negatively impacting policy learning and hindering long-term planning. T... | {
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2501.16919 | Projection-free Algorithms for Online Convex Optimization with
Adversarial Constraints | [
"cs.LG"
] | We study a generalization of the Online Convex Optimization (OCO) framework with time-varying adversarial constraints. In this problem, after selecting a feasible action from the convex decision set $X,$ a convex constraint function is revealed alongside the cost function in each round. Our goal is to design a computat... | {
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2501.16921 | Data-Efficient Extremum-Seeking Control Using Kernel-Based Function
Approximation | [
"eess.SY",
"cs.SY"
] | Existing extremum-seeking control (ESC) approaches typically rely on applying repeated perturbations to input parameters and performing measurements of the corresponding performance output. Performing these measurements can be costly in practical applications, e.g., due to the use of resources, making it desirable to r... | {
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2501.16922 | Agential AI for Integrated Continual Learning, Deliberative Behavior,
and Comprehensible Models | [
"cs.AI",
"cs.LG"
] | Contemporary machine learning paradigm excels in statistical data analysis, solving problems that classical AI couldn't. However, it faces key limitations, such as a lack of integration with planning, incomprehensible internal structure, and inability to learn continually. We present the initial design for an AI system... | {
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2501.16923 | In-Circuit Characterization of Low-Frequency Stability Margins in Power
Amplifiers | [
"eess.SY",
"cs.SY"
] | Low-frequency resonances with low stability margins affect video bandwidth characteristics of power amplifiers. In this work, a non-connectorized measurement technique is presented to obtain the low-frequency critical poles at internal nodes of a hybrid amplifier. The experimental setup uses a high impedance probe conn... | {
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2501.16925 | Detecting harassment and defamation in cyberbullying with
emotion-adaptive training | [
"cs.CL"
] | Existing research on detecting cyberbullying incidents on social media has primarily concentrated on harassment and is typically approached as a binary classification task. However, cyberbullying encompasses various forms, such as denigration and harassment, which celebrities frequently face. Furthermore, suitable trai... | {
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2501.16928 | Detecting Critical Resonances in Microwave Amplifiers through Noise
Simulations | [
"physics.ins-det",
"cs.SY",
"eess.SY"
] | The presence of critical resonances in microwave power amplifiers has a negative impact on its behavior and performance. These critical resonances are usually predicted from pole-zero stability simulations. In this paper, a different and less demanding approach for the circuit designer is proposed. It is based on perfo... | {
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2501.16929 | Giving Sense to Inputs: Toward an Accessible Control Framework for
Shared Autonomy | [
"cs.RO",
"cs.HC"
] | While shared autonomy offers significant potential for assistive robotics, key questions remain about how to effectively map 2D control inputs to 6D robot motions. An intuitive framework should allow users to input commands effortlessly, with the robot responding as expected, without users needing to anticipate the imp... | {
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2501.16931 | Quantifying Uncertainty and Variability in Machine Learning: Confidence
Intervals for Quantiles in Performance Metric Distributions | [
"cs.LG",
"stat.AP"
] | Machine learning models are widely used in applications where reliability and robustness are critical. Model evaluation often relies on single-point estimates of performance metrics such as accuracy, F1 score, or mean squared error, that fail to capture the inherent variability in model performance. This variability ar... | {
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2501.16932 | Online-BLS: An Accurate and Efficient Online Broad Learning System for
Data Stream Classification | [
"cs.LG"
] | The state-of-the-art online learning models generally conduct a single online gradient descent when a new sample arrives and thus suffer from suboptimal model weights. To this end, we introduce an online broad learning system framework with closed-form solutions for each online update. Different from employing existing... | {
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2501.16935 | Beyond Human Intervention: Algorithmic Collusion through Multi-Agent
Learning Strategies | [
"econ.TH",
"cs.MA"
] | Collusion in market pricing is a concept associated with human actions to raise market prices through artificially limited supply. Recently, the idea of algorithmic collusion was put forward, where the human action in the pricing process is replaced by automated agents. Although experiments have shown that collusive ma... | {
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2501.16937 | TAID: Temporally Adaptive Interpolated Distillation for Efficient
Knowledge Transfer in Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Causal language models have demonstrated remarkable capabilities, but their size poses significant challenges for deployment in resource-constrained environments. Knowledge distillation, a widely-used technique for transferring knowledge from a large teacher model to a small student model, presents a promising approach... | {
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2501.16944 | Exact Computation of Any-Order Shapley Interactions for Graph Neural
Networks | [
"cs.LG",
"cs.AI"
] | Albeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In explainable artificial intelligence (XAI), the Shapley Value (SV) is the predominant method to quantify contributions of individual features... | {
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2501.16945 | ToolFactory: Automating Tool Generation by Leveraging LLM to Understand
REST API Documentations | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.SE"
] | LLM-based tool agents offer natural language interfaces, enabling users to seamlessly interact with computing services. While REST APIs are valuable resources for building such agents, they must first be transformed into AI-compatible tools. Automatically generating AI-compatible tools from REST API documents can great... | {
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2501.16947 | Image-based Geo-localization for Robotics: Are Black-box Vision-Language
Models there yet? | [
"cs.CV",
"cs.RO"
] | The advances in Vision-Language models (VLMs) offer exciting opportunities for robotic applications involving image geo-localization, the problem of identifying the geo-coordinates of a place based on visual data only. Recent research works have focused on using a VLM as embeddings extractor for geo-localization, howev... | {
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2501.16952 | Multiple Abstraction Level Retrieve Augment Generation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | A Retrieval-Augmented Generation (RAG) model powered by a large language model (LLM) provides a faster and more cost-effective solution for adapting to new data and knowledge. It also delivers more specialized responses compared to pre-trained LLMs. However, most existing approaches rely on retrieving prefix-sized chun... | {
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2501.16961 | Instantiation-based Formalization of Logical Reasoning Tasks using
Language Models and Logical Solvers | [
"cs.AI"
] | Robustness of reasoning remains a significant challenge for large language models, and addressing it is essential for the practical applicability of AI-driven reasoning systems. We introduce Semantic Self-Verification (SSV), a novel approach that addresses the key challenge in combining language models with the rigor o... | {
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2501.16964 | Few Edges Are Enough: Few-Shot Network Attack Detection with Graph
Neural Networks | [
"cs.LG",
"cs.CR"
] | Detecting cyberattacks using Graph Neural Networks (GNNs) has seen promising results recently. Most of the state-of-the-art models that leverage these techniques require labeled examples, hard to obtain in many real-world scenarios. To address this issue, unsupervised learning and Self-Supervised Learning (SSL) have em... | {
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2501.16966 | Heterogeneity-aware Personalized Federated Learning via Adaptive
Dual-Agent Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Federated Learning (FL) empowers multiple clients to collaboratively train machine learning models without sharing local data, making it highly applicable in heterogeneous Internet of Things (IoT) environments. However, intrinsic heterogeneity in clients' model architectures and computing capabilities often results in ... | {
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2501.16969 | What Really Matters for Learning-based LiDAR-Camera Calibration | [
"cs.CV"
] | Calibration is an essential prerequisite for the accurate data fusion of LiDAR and camera sensors. Traditional calibration techniques often require specific targets or suitable scenes to obtain reliable 2D-3D correspondences. To tackle the challenge of target-less and online calibration, deep neural networks have been ... | {
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2501.16971 | RODEO: Robust Outlier Detection via Exposing Adaptive
Out-of-Distribution Samples | [
"cs.CV",
"cs.LG"
] | In recent years, there have been significant improvements in various forms of image outlier detection. However, outlier detection performance under adversarial settings lags far behind that in standard settings. This is due to the lack of effective exposure to adversarial scenarios during training, especially on unseen... | {
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2501.16973 | Towards Open-Source and Modular Space Systems with ATMOS | [
"cs.RO"
] | In the near future, autonomous space systems will compose a large number of the spacecraft being deployed. Their tasks will involve autonomous rendezvous and proximity operations with large structures, such as inspections or assembly of orbiting space stations and maintenance and human-assistance tasks over shared work... | {
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2501.16974 | Excited-state nonadiabatic dynamics in explicit solvent using machine
learned interatomic potentials | [
"physics.chem-ph",
"cs.LG"
] | Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical calculations limits its application in combination with trajectory surface hoppin... | {
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2501.16975 | Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling | [
"cs.CL",
"cs.LG"
] | Tokenization is a fundamental component of large language models (LLMs), yet its influence on model scaling and performance is not fully explored. In this paper, we introduce Over-Tokenized Transformers, a novel framework that decouples input and output vocabularies to improve language modeling performance. Specificall... | {
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2501.16981 | Modulating CNN Features with Pre-Trained ViT Representations for
Open-Vocabulary Object Detection | [
"cs.CV"
] | Owing to large-scale image-text contrastive training, pre-trained vision language model (VLM) like CLIP shows superior open-vocabulary recognition ability. Most existing open-vocabulary object detectors attempt to utilize the pre-trained VLM to attain generative representation. F-ViT uses the pre-trained visual encoder... | {
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2501.16986 | Generative quantum combinatorial optimization by means of a novel
conditional generative quantum eigensolver | [
"quant-ph",
"cs.AI",
"cs.LG"
] | Quantum computing is entering a transformative phase with the emergence of logical quantum processors, which hold the potential to tackle complex problems beyond classical capabilities. While significant progress has been made, applying quantum algorithms to real-world problems remains challenging. Hybrid quantum-class... | {
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2501.16988 | Marginal and Conditional Importance Measures from Machine Learning
Models and Their Relationship with Conditional Average Treatment Effect | [
"stat.ML",
"cs.LG"
] | Interpreting black-box machine learning models is challenging due to their strong dependence on data and inherently non-parametric nature. This paper reintroduces the concept of importance through "Marginal Variable Importance Metric" (MVIM), a model-agnostic measure of predictor importance based on the true conditiona... | {
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2501.16992 | FedEFM: Federated Endovascular Foundation Model with Unseen Data | [
"cs.CV"
] | In endovascular surgery, the precise identification of catheters and guidewires in X-ray images is essential for reducing intervention risks. However, accurately segmenting catheter and guidewire structures is challenging due to the limited availability of labeled data. Foundation models offer a promising solution by e... | {
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2501.16997 | MAUCell: An Adaptive Multi-Attention Framework for Video Frame
Prediction | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Temporal sequence modeling stands as the fundamental foundation for video prediction systems and real-time forecasting operations as well as anomaly detection applications. The achievement of accurate predictions through efficient resource consumption remains an ongoing issue in contemporary temporal sequence modeling.... | {
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2501.17002 | Covert Adversarial Actuators in Finite MDPs | [
"cs.IT",
"math.IT"
] | We consider a Markov decision process (MDP) in which actions prescribed by the controller are executed by a separate actuator, which may behave adversarially. At each time step, the controller selects and transmits an action to the actuator; however, the actuator may deviate from the intended action to degrade the cont... | {
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2501.17010 | New Quantum MDS Codes with Flexible Parameters from Hermitian
Self-Orthogonal GRS Codes | [
"cs.IT",
"math.IT"
] | Let $q$ be a prime power. Let $\lambda>1$ be a divisor of $q-1$, and let $\tau>1$ and $\rho>1$ be divisors of $q+1$. Under certain conditions we prove that there exists an MDS stabilizer quantum code with length $n=\lambda \tau \sigma$ where $2\le \sigma \le \rho$. This is a flexible construction, which inclu... | {
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2501.17011 | MIDI-GPT: A Controllable Generative Model for Computer-Assisted
Multitrack Music Composition | [
"cs.SD",
"cs.LG",
"cs.MM",
"eess.AS"
] | We present and release MIDI-GPT, a generative system based on the Transformer architecture that is designed for computer-assisted music composition workflows. MIDI-GPT supports the infilling of musical material at the track and bar level, and can condition generation on attributes including: instrument type, musical st... | {
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2501.17015 | Revisit Mixture Models for Multi-Agent Simulation: Experimental Study
within a Unified Framework | [
"cs.AI",
"cs.MA",
"cs.RO"
] | Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect. In multi-agent simulation, the primary challenges include behavioral multimodality and closed-loop distributional shifts. In this study, we revisit mixture models for generat... | {
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2501.17018 | Six-Degree-of-Freedom Motion Emulation for Data-Driven Modeling of
Underwater Vehicles | [
"cs.RO"
] | This article presents a collaborative research effort aimed at developing a novel six-degree-of-freedom (6-DOF) motion platform for the empirical characterization of hydrodynamic forces crucial for the control and stability of surface and subsurface vehicles. Traditional experimental methods, such as the Planar Motion ... | {
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2501.17021 | On Oblivious Transfer Capacity of Noisy Multiple Access Channel | [
"cs.IT",
"cs.CR",
"math.IT"
] | This work investigates the problem of Oblivious Transfer (OT) over a noisy Multiple Access Channel (MAC) involving two non-colluding senders and a single receiver. The channel model is characterized by correlations among the parties, with the parties assumed to be either honest-but-curious or, in the receiver's case, p... | {
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2501.17022 | Mobile Manipulation Instruction Generation from Multiple Images with
Automatic Metric Enhancement | [
"cs.RO"
] | We consider the problem of generating free-form mobile manipulation instructions based on a target object image and receptacle image. Conventional image captioning models are not able to generate appropriate instructions because their architectures are typically optimized for single-image. In this study, we propose a m... | {
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2501.17030 | Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings
of Reinforcement Learning Strategies | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CR"
] | Large Language Models (LLMs) have achieved remarkable progress in reasoning, alignment, and task-specific performance. However, ensuring harmlessness in these systems remains a critical challenge, particularly in advanced models like DeepSeek-R1. This paper examines the limitations of Reinforcement Learning (RL) as the... | {
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} |
2501.17037 | Standardised schema and taxonomy for AI incident databases in critical
digital infrastructure | [
"cs.CY",
"cs.AI",
"cs.HC"
] | The rapid deployment of Artificial Intelligence (AI) in critical digital infrastructure introduces significant risks, necessitating a robust framework for systematically collecting AI incident data to prevent future incidents. Existing databases lack the granularity as well as the standardized structure required for co... | {
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} |
2501.17039 | Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block
Representations with Large Language Models | [
"cs.IR"
] | In recent years, large language models (LLMs) have demonstrated exceptional power in various domains, including information retrieval. Most of the previous practices involve leveraging these models to create a single embedding for each query, each passage, or each document individually, a strategy exemplified and used ... | {
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2501.17041 | Benchmarking Quantum Convolutional Neural Networks for Signal
Classification in Simulated Gamma-Ray Burst Detection | [
"astro-ph.HE",
"cs.AI",
"quant-ph"
] | This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Te... | {
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2501.17042 | Emergence of network communities driven by local rules | [
"physics.soc-ph",
"cond-mat.dis-nn",
"cs.DM",
"cs.SI",
"math.CO"
] | Natural systems are modeled by networks where nodes represent the system units and links their interactions. The networks nodes are often segregated into communities with different connectivity patterns. Node heterogeneity such as political affiliation in social networks or biological function in gene networks are high... | {
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} |
2501.17044 | Synthesizing 3D Abstractions by Inverting Procedural Buildings with
Transformers | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We generate abstractions of buildings, reflecting the essential aspects of their geometry and structure, by learning to invert procedural models. We first build a dataset of abstract procedural building models paired with simulated point clouds and then learn the inverse mapping through a transformer. Given a point clo... | {
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} |
2501.17047 | How Linguistics Learned to Stop Worrying and Love the Language Models | [
"cs.CL"
] | Language models can produce fluent, grammatical text. Nonetheless, some maintain that language models don't really learn language and also that, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the ... | {
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} |
2501.17049 | Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of
Entropy Functionals | [
"math.AP",
"cs.LG",
"math.OC",
"stat.ML"
] | We investigate a family of gradient flows of positive and probability measures, focusing on the Hellinger-Kantorovich (HK) geometry, which unifies transport mechanism of Otto-Wasserstein, and the birth-death mechanism of Hellinger (or Fisher-Rao). A central contribution is a complete characterization of global exponent... | {
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} |
2501.17053 | Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal
Video Grounding | [
"cs.CV"
] | In this work, we focus on Weakly Supervised Spatio-Temporal Video Grounding (WSTVG). It is a multimodal task aimed at localizing specific subjects spatio-temporally based on textual queries without bounding box supervision. Motivated by recent advancements in multi-modal foundation models for grounding tasks, we first ... | {
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} |
2501.17054 | Generative diffusion models from a PDE perspective | [
"math.PR",
"cs.LG"
] | Diffusion models have become the de facto framework for generating new datasets. The core of these models lies in the ability to reverse a diffusion process in time. The goal of this manuscript is to explain, from a PDE perspective, how this method works and how to derive the PDE governing the reverse dynamics as well ... | {
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} |
2501.17059 | Channel Estimation for XL-MIMO Systems with Decentralized Baseband
Processing: Integrating Local Reconstruction with Global Refinement | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized cha... | {
"Other": 0,
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} |
2501.17062 | EdgeMLOps: Operationalizing ML models with Cumulocity IoT and
thin-edge.io for Visual quality Inspection | [
"cs.LG",
"cs.AI",
"cs.CV"
] | This paper introduces EdgeMLOps, a framework leveraging Cumulocity IoT and thin-edge.io for deploying and managing machine learning models on resource-constrained edge devices. We address the challenges of model optimization, deployment, and lifecycle management in edge environments. The framework's efficacy is demonst... | {
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} |
2501.17070 | Context is Key for Agent Security | [
"cs.CR",
"cs.CL",
"cs.LG"
] | Judging the safety of an action, whether taken by a human or a system, must take into account the context in which the action takes place. For example, deleting an email from a user's mailbox may or may not be appropriate depending on the email's content, the user's goals, or even available space. Systems today that ma... | {
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} |
2501.17074 | DataLens: ML-Oriented Interactive Tabular Data Quality Dashboard | [
"cs.DB"
] | Maintaining high data quality is crucial for reliable data analysis and machine learning (ML). However, existing data quality management tools often lack automation, interactivity, and integration with ML workflows. This demonstration paper introduces DataLens, a novel interactive dashboard designed to streamline and a... | {
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} |
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