id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.01866 | NSI-IBP: A General Numerical Singular Integral Method via Integration by
Parts | [
"cs.CE"
] | A general framework of Numerical Singular Integrals (NSI) method based on the Integration By Parts (IBP) has been developed for integrals involving singular and nearly singular integrands, or NSI-IBP. Through a general integration by parts formula and by choosing some analytically integrable function to approximate the... | {
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2412.01868 | Composition of Experts: A Modular Compound AI System Leveraging Large
Language Models | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] | Large Language Models (LLMs) have achieved remarkable advancements, but their monolithic nature presents challenges in terms of scalability, cost, and customization. This paper introduces the Composition of Experts (CoE), a modular compound AI system leveraging multiple expert LLMs. CoE leverages a router to dynamicall... | {
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2412.01876 | Understanding Bias in Large-Scale Visual Datasets | [
"cs.CV",
"cs.LG"
] | A recent study has shown that large-scale visual datasets are very biased: they can be easily classified by modern neural networks. However, the concrete forms of bias among these datasets remain unclear. In this study, we propose a framework to identify the unique visual attributes distinguishing these datasets. Our a... | {
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2412.01909 | Event-Based Framework for Agile Resilience in Criticality-Aware Wireless
Networks | [
"cs.IT",
"math.IT"
] | As mission- and safety-critical wireless applications grow in complexity and diversity, next-generation wireless systems must meet increasingly stringent and multifaceted requirements. These systems demand resilience along with enhanced intelligence and adaptability to ensure reliable communication under diverse condit... | {
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2412.01919 | Diffusion models learn distributions generated by complex Langevin
dynamics | [
"hep-lat",
"cs.LG"
] | The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to lea... | {
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2412.01926 | Beyond Pairwise Correlations: Higher-Order Redundancies in
Self-Supervised Representation Learning | [
"cs.LG"
] | Several self-supervised learning (SSL) approaches have shown that redundancy reduction in the feature embedding space is an effective tool for representation learning. However, these methods consider a narrow notion of redundancy, focusing on pairwise correlations between features. To address this limitation, we formal... | {
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2412.01928 | MALT: Improving Reasoning with Multi-Agent LLM Training | [
"cs.LG",
"cs.AI"
] | Enabling effective collaboration among LLMs is a crucial step toward developing autonomous systems capable of solving complex problems. While LLMs are typically used as single-model generators, where humans critique and refine their outputs, the potential for jointly-trained collaborative models remains largely unexplo... | {
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2412.01929 | ECG-SleepNet: Deep Learning-Based Comprehensive Sleep Stage
Classification Using ECG Signals | [
"cs.AI",
"cs.LG",
"eess.SP"
] | Accurate sleep stage classification is essential for understanding sleep disorders and improving overall health. This study proposes a novel three-stage approach for sleep stage classification using ECG signals, offering a more accessible alternative to traditional methods that often rely on complex modalities like EEG... | {
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2412.01930 | PROFIT: A Specialized Optimizer for Deep Fine Tuning | [
"cs.CV"
] | Fine-tuning pre-trained models has become invaluable in computer vision and robotics. Recent fine-tuning approaches focus on improving efficiency rather than accuracy by using a mixture of smaller learning rates or frozen backbones. To return the spotlight to model accuracy, we present PROFIT (Proximally Restricted Opt... | {
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2412.01931 | Planar Gaussian Splatting | [
"cs.CV"
] | This paper presents Planar Gaussian Splatting (PGS), a novel neural rendering approach to learn the 3D geometry and parse the 3D planes of a scene, directly from multiple RGB images. The PGS leverages Gaussian primitives to model the scene and employ a hierarchical Gaussian mixture approach to group them. Similar Gauss... | {
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2412.01933 | Recurrent Neural Network on PICTURE Model | [
"cs.AI"
] | Intensive Care Units (ICUs) provide critical care and life support for most severely ill and injured patients in the hospital. With the need for ICUs growing rapidly and unprecedentedly, especially during COVID-19, accurately identifying the most critical patients helps hospitals to allocate resources more efficiently ... | {
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2412.01935 | Cross Domain Adaptation using Adversarial networks with Cyclic loss | [
"cs.LG",
"cs.AI"
] | Deep Learning methods are highly local and sensitive to the domain of data they are trained with. Even a slight deviation from the domain distribution affects prediction accuracy of deep networks significantly. In this work, we have investigated a set of techniques aimed at increasing accuracy of generator networks whi... | {
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2412.01936 | Kernel-Free Universum Quadratic Surface Twin Support Vector Machines for
Imbalanced Data | [
"cs.LG",
"cs.AI",
"math.OC"
] | Binary classification tasks with imbalanced classes pose significant challenges in machine learning. Traditional classifiers often struggle to accurately capture the characteristics of the minority class, resulting in biased models with subpar predictive performance. In this paper, we introduce a novel approach to tack... | {
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2412.01937 | Approximately Optimal Search on a Higher-dimensional Sliding Puzzle | [
"cs.AI",
"cs.DM",
"cs.LG",
"cs.NE"
] | Higher-dimensional sliding puzzles are constructed on the vertices of a $d$-dimensional hypercube, where $2^d-l$ vertices are distinctly coloured. Rings with the same colours are initially set randomly on the vertices of the hypercube. The goal of the puzzle is to move each of the $2^d-l$ rings to pre-defined target ve... | {
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2412.01940 | Down with the Hierarchy: The 'H' in HNSW Stands for "Hubs" | [
"cs.LG",
"cs.DB",
"cs.IR"
] | Driven by recent breakthrough advances in neural representation learning, approximate near-neighbor (ANN) search over vector embeddings has emerged as a critical computational workload. With the introduction of the seminal Hierarchical Navigable Small World (HNSW) algorithm, graph-based indexes have established themsel... | {
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2412.01941 | Global Average Feature Augmentation for Robust Semantic Segmentation
with Transformers | [
"cs.CV"
] | Robustness to out-of-distribution data is crucial for deploying modern neural networks. Recently, Vision Transformers, such as SegFormer for semantic segmentation, have shown impressive robustness to visual corruptions like blur or noise affecting the acquisition device. In this paper, we propose Channel Wise Feature A... | {
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2412.01944 | Enhancing Crop Segmentation in Satellite Image Time Series with
Transformer Networks | [
"cs.CV",
"eess.IV"
] | Recent studies have shown that Convolutional Neural Networks (CNNs) achieve impressive results in crop segmentation of Satellite Image Time Series (SITS). However, the emergence of transformer networks in various vision tasks raises the question of whether they can outperform CNNs in this task as well. This paper prese... | {
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2412.01946 | The Reality of AI and Biorisk | [
"cs.AI"
] | To accurately and confidently answer the question 'could an AI model or system increase biorisk', it is necessary to have both a sound theoretical threat model for how AI models or systems could increase biorisk and a robust method for testing that threat model. This paper provides an analysis of existing available res... | {
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2412.01948 | The Evolution and Future Perspectives of Artificial Intelligence
Generated Content | [
"cs.AI"
] | Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolut... | {
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2412.01949 | Identifying Key Nodes for the Influence Spread using a Machine Learning
Approach | [
"cs.SI",
"cs.AI"
] | The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional,... | {
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2412.01950 | A Novel Generative Multi-Task Representation Learning Approach for
Predicting Postoperative Complications in Cardiac Surgery Patients | [
"cs.LG",
"eess.IV"
] | Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical V... | {
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2412.01951 | Self-Improvement in Language Models: The Sharpening Mechanism | [
"cs.AI",
"cs.CL",
"cs.LG",
"stat.ML"
] | Recent work in language modeling has raised the possibility of self-improvement, where a language models evaluates and refines its own generations to achieve higher performance without external feedback. It is impossible for this self-improvement to create information that is not already in the model, so why should we ... | {
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2412.01953 | The Landscape of Causal Discovery Data: Grounding Causal Discovery in
Real-World Applications | [
"cs.LG",
"stat.ME"
] | Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, o... | {
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2412.01954 | Geometry-aware PINNs for Turbulent Flow Prediction | [
"cs.LG",
"cs.NA",
"math.NA",
"physics.flu-dyn"
] | Design exploration or optimization using computational fluid dynamics (CFD) is commonly used in the industry. Geometric variation is a key component of such design problems, especially in turbulent flow scenarios, which involves running costly simulations at every design iteration. While parametric RANS-PINN type appro... | {
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2412.01955 | The use of large language models to enhance cancer clinical trial
educational materials | [
"cs.CL",
"cs.AI"
] | Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the potential of Large Language Models (LLMs), specifically GPT4, in generating patient-friendly educational content from clinical trial informe... | {
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2412.01957 | Usage Governance Advisor: From Intent to AI Governance | [
"cs.AI"
] | Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational damage incurred by the use of models that are unsafe. Safety covers bot... | {
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2412.01958 | Enhancing Deep Learning Model Robustness through Metamorphic Re-Training | [
"cs.CV",
"cs.AI"
] | This paper evaluates the use of metamorphic relations to enhance the robustness and real-world performance of machine learning models. We propose a Metamorphic Retraining Framework, which applies metamorphic relations to data and utilizes semi-supervised learning algorithms in an iterative and adaptive multi-cycle proc... | {
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2412.01971 | Learning a Filtered Backprojection Reconstruction Method for
Photoacoustic Computed Tomography with Hemispherical Measurement Geometries | [
"physics.med-ph",
"cs.AI"
] | In certain three-dimensional (3D) applications of photoacoustic computed tomography (PACT), including \textit{in vivo} breast imaging, hemispherical measurement apertures that enclose the object within their convex hull are employed for data acquisition. Data acquired with such measurement geometries are referred to as... | {
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2412.01973 | MPBD-LSTM: A Predictive Model for Colorectal Liver Metastases Using Time
Series Multi-phase Contrast-Enhanced CT Scans | [
"eess.IV",
"cs.CV"
] | Colorectal cancer is a prevalent form of cancer, and many patients develop colorectal cancer liver metastasis (CRLM) as a result. Early detection of CRLM is critical for improving survival rates. Radiologists usually rely on a series of multi-phase contrast-enhanced computed tomography (CECT) scans done during follow-u... | {
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2412.01975 | Reactive Synthesis of Sensor Revealing Strategies in Hypergames on
Graphs | [
"cs.GT",
"cs.RO",
"cs.SY",
"eess.SY"
] | In many security applications of cyber-physical systems, a system designer must guarantee that critical missions are satisfied against attacks in the sensors and actuators of the CPS. Traditional security design of CPSs often assume that attackers have complete knowledge of the system. In this article, we introduce a c... | {
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2412.01978 | Human-centred test and evaluation of military AI | [
"cs.HC",
"cs.AI"
] | The REAIM 2024 Blueprint for Action states that AI applications in the military domain should be ethical and human-centric and that humans must remain responsible and accountable for their use and effects. Developing rigorous test and evaluation, verification and validation (TEVV) frameworks will contribute to robust o... | {
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2412.01979 | FGATT: A Robust Framework for Wireless Data Imputation Using Fuzzy Graph
Attention Networks and Transformer Encoders | [
"cs.LG",
"cs.IR",
"cs.NE"
] | Missing data is a pervasive challenge in wireless networks and many other domains, often compromising the performance of machine learning and deep learning models. To address this, we propose a novel framework, FGATT, that combines the Fuzzy Graph Attention Network (FGAT) with the Transformer encoder to perform robust ... | {
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2412.01981 | Free Process Rewards without Process Labels | [
"cs.LG",
"cs.CL"
] | Different from its counterpart outcome reward models (ORMs), which evaluate the entire responses, a process reward model (PRM) scores a reasoning trajectory step by step, providing denser and more fine grained rewards. However, training a PRM requires labels annotated at every intermediate step, presenting significant ... | {
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2412.01983 | Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using
YOLOv8, YOLOv9, YOLOv10, and YOLOv11 | [
"cs.CV",
"cs.LG"
] | The increasing urbanization and the growing number of vehicles in cities have underscored the need for efficient parking management systems. Traditional smart parking solutions often rely on sensors or cameras for occupancy detection, each with its limitations. Recent advancements in deep learning have introduced new Y... | {
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2412.01985 | Improving feature interactions at Pinterest under industry constraints | [
"cs.IR"
] | Adopting advances in recommendation systems is often challenging in industrial settings due to unique constraints. This paper aims to highlight these constraints through the lens of feature interactions. Feature interactions are critical for accurately predicting user behavior in recommendation systems and online adver... | {
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2412.01986 | HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh
Quality Assessment | [
"cs.CV",
"cs.MM"
] | Mesh quality assessment (MQA) models play a critical role in the design, optimization, and evaluation of mesh operation systems in a wide variety of applications. Current MQA models, whether model-based methods using topology-aware features or projection-based approaches working on rendered 2D projections, often fail t... | {
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2412.01987 | ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions | [
"cs.CV"
] | The goal of this work is to generate step-by-step visual instructions in the form of a sequence of images, given an input image that provides the scene context and the sequence of textual instructions. This is a challenging problem as it requires generating multi-step image sequences to achieve a complex goal while bei... | {
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2412.01991 | Real-Time Multilingual Sign Language Processing | [
"cs.CL",
"cs.AI"
] | Sign Language Processing (SLP) is an interdisciplinary field comprised of Natural Language Processing (NLP) and Computer Vision. It is focused on the computational understanding, translation, and production of signed languages. Traditional approaches have often been constrained by the use of gloss-based systems that ar... | {
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2412.01992 | ChatCollab: Exploring Collaboration Between Humans and AI Agents in
Software Teams | [
"cs.HC",
"cs.AI"
] | We explore the potential for productive team-based collaboration between humans and Artificial Intelligence (AI) by presenting and conducting initial tests with a general framework that enables multiple human and AI agents to work together as peers. ChatCollab's novel architecture allows agents - human or AI - to join ... | {
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2412.01993 | Generalized EXTRA stochastic gradient Langevin dynamics | [
"cs.LG",
"math.OC"
] | Langevin algorithms are popular Markov Chain Monte Carlo methods for Bayesian learning, particularly when the aim is to sample from the posterior distribution of a parametric model, given the input data and the prior distribution over the model parameters. Their stochastic versions such as stochastic gradient Langevin ... | {
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2412.02000 | Who's Gaming the System? A Causally-Motivated Approach for Detecting
Strategic Adaptation | [
"cs.LG",
"cs.AI"
] | In many settings, machine learning models may be used to inform decisions that impact individuals or entities who interact with the model. Such entities, or agents, may game model decisions by manipulating their inputs to the model to obtain better outcomes and maximize some utility. We consider a multi-agent setting w... | {
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2412.02004 | Open Source Evolutionary Computation with Chips-n-Salsa | [
"cs.NE",
"cs.MS"
] | When it was first introduced, the Chips-n-Salsa Java library provided stochastic local search and related algorithms, with a focus on self-adaptation and parallel execution. For the past four years, we expanded its scope to include evolutionary computation. This paper concerns the evolutionary algorithms that Chips-n-S... | {
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2412.02006 | Unveiling Interpretability in Self-Supervised Speech Representations for
Parkinson's Diagnosis | [
"cs.CV"
] | Recent works in pathological speech analysis have increasingly relied on powerful self-supervised speech representations, leading to promising results. However, the complex, black-box nature of these embeddings and the limited research on their interpretability significantly restrict their adoption for clinical diagnos... | {
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2412.02009 | Optimizing Genetic Algorithms Using the Binomial Distribution | [
"cs.NE"
] | Evolutionary algorithms rely very heavily on randomized behavior. Execution speed, therefore, depends strongly on how we implement randomness, such as our choice of pseudorandom number generator, or the algorithms used to map pseudorandom values to specific intervals or distributions. In this paper, we observe that the... | {
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2412.02012 | INSIGHT: Explainable Weakly-Supervised Medical Image Analysis | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Due to their large sizes, volumetric scans and whole-slide pathology images (WSIs) are often processed by extracting embeddings from local regions and then an aggregator makes predictions from this set. However, current methods require post-hoc visualization techniques (e.g., Grad-CAM) and often fail to localize small ... | {
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2412.02016 | Explore Reinforced: Equilibrium Approximation with Reinforcement
Learning | [
"cs.LG",
"cs.AI",
"cs.GT"
] | Current approximate Coarse Correlated Equilibria (CCE) algorithms struggle with equilibrium approximation for games in large stochastic environments but are theoretically guaranteed to converge to a strong solution concept. In contrast, modern Reinforcement Learning (RL) algorithms provide faster training yet yield wea... | {
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2412.02025 | PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large
Language Models in Autonomous Driving | [
"cs.RO",
"cs.AI"
] | There is growing interest in leveraging the capabilities of robust Multi-Modal Large Language Models (MLLMs) directly within autonomous driving contexts. However, the high costs and complexity of designing and training end-to-end autonomous driving models make them challenging for many enterprises and research entities... | {
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2412.02029 | Learning Ensembles of Vision-based Safety Control Filters | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.RO",
"cs.SY",
"eess.SY"
] | Safety filters in control systems correct nominal controls that violate safety constraints. Designing such filters as functions of visual observations in uncertain and complex environments is challenging. Several deep learning-based approaches to tackle this challenge have been proposed recently. However, formally veri... | {
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2412.02030 | NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic
Adversarial Training | [
"cs.CV"
] | We introduce NitroFusion, a fundamentally different approach to single-step diffusion that achieves high-quality generation through a dynamic adversarial framework. While one-step methods offer dramatic speed advantages, they typically suffer from quality degradation compared to their multi-step counterparts. Just as a... | {
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2412.02033 | Linear Supervision for Nonlinear, High-Dimensional Neural Control and
Differential Games | [
"math.OC",
"cs.SY",
"eess.SY"
] | As the dimension of a system increases, traditional methods for control and differential games rapidly become intractable, making the design of safe autonomous agents challenging in complex or team settings. Deep-learning approaches avoid discretization and yield numerous successes in robotics and autonomy, but at a hi... | {
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2412.02035 | LLMs4Life: Large Language Models for Ontology Learning in Life Sciences | [
"cs.AI"
] | Ontology learning in complex domains, such as life sciences, poses significant challenges for current Large Language Models (LLMs). Existing LLMs struggle to generate ontologies with multiple hierarchical levels, rich interconnections, and comprehensive class coverage due to constraints on the number of tokens they can... | {
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2412.02039 | Mutli-View 3D Reconstruction using Knowledge Distillation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Large Foundation Models like Dust3r can produce high quality outputs such as pointmaps, camera intrinsics, and depth estimation, given stereo-image pairs as input. However, the application of these outputs on tasks like Visual Localization requires a large amount of inference time and compute resources. To address thes... | {
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2412.02041 | Predicting the Impact of Scope Changes on Project Cost and Schedule
Using Machine Learning Techniques | [
"cs.LG"
] | In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequenc... | {
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2412.02043 | Future of Information Retrieval Research in the Age of Generative AI | [
"cs.IR",
"cs.AI"
] | In the fast-evolving field of information retrieval (IR), the integration of generative AI technologies such as large language models (LLMs) is transforming how users search for and interact with information. Recognizing this paradigm shift at the intersection of IR and generative AI (IR-GenAI), a visioning workshop su... | {
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2412.02044 | ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land
cover classification | [
"eess.IV",
"cs.CV"
] | Synthetic Aperture Radar (SAR) images have proven to be a valuable cue for multimodal Land Cover Classification (LCC) when combined with RGB images. Most existing studies on cross-modal fusion assume that consistent feature information is necessary between the two modalities, and as a result, they construct networks wi... | {
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2412.02048 | Impact of Data Snooping on Deep Learning Models for Locating
Vulnerabilities in Lifted Code | [
"cs.CR",
"cs.CL",
"cs.LG",
"cs.SE"
] | This study examines the impact of data snooping on neural networks used to detect vulnerabilities in lifted code, and builds on previous research that used word2vec and unidirectional and bidirectional transformer-based embeddings. The research specifically focuses on how model performance is affected when embedding mo... | {
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2412.02052 | FoveaSPAD: Exploiting Depth Priors for Adaptive and Efficient
Single-Photon 3D Imaging | [
"eess.IV",
"cs.CV"
] | Fast, efficient, and accurate depth-sensing is important for safety-critical applications such as autonomous vehicles. Direct time-of-flight LiDAR has the potential to fulfill these demands, thanks to its ability to provide high-precision depth measurements at long standoff distances. While conventional LiDAR relies on... | {
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2412.02053 | GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach | [
"cs.LG",
"cs.IT",
"math.IT"
] | This paper presents a novel auto-encoder based end-to-end channel encoding and decoding. It integrates deep reinforcement learning (DRL) and graph neural networks (GNN) in code design by modeling the generation of code parity-check matrices as a Markov Decision Process (MDP), to optimize key coding performance metrics ... | {
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2412.02054 | Redundant Queries in DETR-Based 3D Detection Methods: Unnecessary and
Prunable | [
"cs.CV"
] | Query-based models are extensively used in 3D object detection tasks, with a wide range of pre-trained checkpoints readily available online. However, despite their popularity, these models often require an excessive number of object queries, far surpassing the actual number of objects to detect. The redundant queries r... | {
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2412.02056 | A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil
and Sinhala | [
"cs.CL"
] | This paper presents a multi-way parallel English-Tamil-Sinhala corpus annotated with Named Entities (NEs), where Sinhala and Tamil are low-resource languages. Using pre-trained multilingual Language Models (mLMs), we establish new benchmark Named Entity Recognition (NER) results on this dataset for Sinhala and Tamil. W... | {
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2412.02057 | Comparative Analysis of Multi-Agent Reinforcement Learning Policies for
Crop Planning Decision Support | [
"cs.LG",
"cs.AI",
"cs.CY"
] | In India, the majority of farmers are classified as small or marginal, making their livelihoods particularly vulnerable to economic losses due to market saturation and climate risks. Effective crop planning can significantly impact their expected income, yet existing decision support systems (DSS) often provide generic... | {
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2412.02058 | BN-AuthProf: Benchmarking Machine Learning for Bangla Author Profiling
on Social Media Texts | [
"cs.CL",
"cs.SI"
] | Author profiling, the analysis of texts to uncover attributes such as gender and age of the author, has become essential with the widespread use of social media platforms. This paper focuses on author profiling in the Bangla language, aiming to extract valuable insights about anonymous authors based on their writing st... | {
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2412.02059 | Lean classical-quantum hybrid neural network model for image
classification | [
"quant-ph",
"cs.CV"
] | The integration of algorithms from quantum information with neural networks has enabled unprecedented advancements in various domains. Nonetheless, the application of quantum machine learning algorithms for image classiffcation predominantly relies on traditional architectures such as variational quantum circuits. The ... | {
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2412.02061 | Strong Friendship Paradox in Social Networks | [
"cs.SI"
] | The friendship paradox in social networks states that your friends have more friends than you do, on average. Recently, a stronger variant of the paradox was shown to hold for most people within a network: `most of your friends have more friends than you do.' Unlike the original paradox, which arises trivially because ... | {
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2412.02062 | Construction and optimization of health behavior prediction model for
the elderly in smart elderly care | [
"cs.AI",
"cs.CY"
] | With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements a smart elderly care service model to address issues such as data diversity, health status complexity, long-term dependence and data loss, sudden changes in behavior, and ... | {
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2412.02065 | Leveraging Large Language Models to Democratize Access to Costly
Financial Datasets for Academic Research | [
"q-fin.GN",
"cs.AI",
"cs.CE",
"cs.LG",
"econ.GN",
"q-fin.EC"
] | Unequal access to costly datasets essential for empirical research has long hindered researchers from disadvantaged institutions, limiting their ability to contribute to their fields and advance their careers. Recent breakthroughs in Large Language Models (LLMs) have the potential to democratize data access by automati... | {
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2412.02066 | CLERF: Contrastive LEaRning for Full Range Head Pose Estimation | [
"cs.CV"
] | We introduce a novel framework for representation learning in head pose estimation (HPE). Previously such a scheme was difficult due to head pose data sparsity, making triplet sampling infeasible. Recent progress in 3D generative adversarial networks (3D-aware GAN) has opened the door for easily sampling triplets (anch... | {
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2412.02071 | Progress-Aware Video Frame Captioning | [
"cs.CV"
] | While image captioning provides isolated descriptions for individual images, and video captioning offers one single narrative for an entire video clip, our work explores an important middle ground: progress-aware video captioning at the frame level. This novel task aims to generate temporally fine-grained captions that... | {
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2412.02072 | Performance Comparison of Deep Learning Techniques in Naira
Classification | [
"cs.CV"
] | The Naira is Nigeria's official currency in daily transactions. This study presents the deployment and evaluation of Deep Learning (DL) models to classify Currency Notes (Naira) by denomination. Using a diverse dataset of 1,808 images of Naira notes captured under different conditions, trained the models employing diff... | {
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2412.02075 | Gaussian Object Carver: Object-Compositional Gaussian Splatting with
surfaces completion | [
"cs.CV",
"cs.RO"
] | 3D scene reconstruction is a foundational problem in computer vision. Despite recent advancements in Neural Implicit Representations (NIR), existing methods often lack editability and compositional flexibility, limiting their use in scenarios requiring high interactivity and object-level manipulation. In this paper, we... | {
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2412.02076 | Topology-Preserving Image Segmentation with Spatial-Aware Persistent
Feature Matching | [
"cs.CV"
] | Topological correctness is critical for segmentation of tubular structures. Existing topological segmentation loss functions are primarily based on the persistent homology of the image. They match the persistent features from the segmentation with the persistent features from the ground truth and minimize the differenc... | {
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2412.02081 | Let's Think Var-by-Var: Large Language Models Enable Ad Hoc
Probabilistic Reasoning | [
"cs.CL"
] | A hallmark of intelligence is the ability to flesh out underspecified situations using "common sense." We propose to extract that common sense from large language models (LLMs), in a form that can feed into probabilistic inference. We focus our investigation on $\textit{guesstimation}$ questions such as "How much are A... | {
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2412.02083 | Implementing An Artificial Quantum Perceptron | [
"quant-ph",
"cs.AI"
] | A Perceptron is a fundamental building block of a neural network. The flexibility and scalability of perceptron make it ubiquitous in building intelligent systems. Studies have shown the efficacy of a single neuron in making intelligent decisions. Here, we examined and compared two perceptrons with distinct mechanisms,... | {
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2412.02084 | Comparative Analysis of Black-Box and White-Box Machine Learning Model
in Phishing Detection | [
"cs.CR",
"cs.AI"
] | Background: Explainability in phishing detection model can support a further solution of phishing attack mitigation by increasing trust and understanding how phishing can be detected. Objective: The aims of this study to determine and best recommendation to apply an approach which has several components with abilities ... | {
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2412.02085 | Evolution of Collective AI Beyond Individual Optimization | [
"cs.MA",
"cs.AI"
] | This study investigates collective behaviors that emerge from a group of homogeneous individuals optimized for a specific capability. We created a group of simple, identical neural network based agents modeled after chemotaxis-driven vehicles that follow pheromone trails and examined multi-agent simulations using clone... | {
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2412.02089 | Offline Stochastic Optimization of Black-Box Objective Functions | [
"cs.LG"
] | Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search spaces. Thus, it is essential to leverage existing data to avoid costly active queries of these black-box functions. To this end, while Off... | {
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2412.02090 | MEP-Net: Generating Solutions to Scientific Problems with Limited
Knowledge by Maximum Entropy Principle | [
"stat.ML",
"cs.LG",
"physics.data-an"
] | Maximum entropy principle (MEP) offers an effective and unbiased approach to inferring unknown probability distributions when faced with incomplete information, while neural networks provide the flexibility to learn complex distributions from data. This paper proposes a novel neural network architecture, the MEP-Net, w... | {
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2412.02091 | The Problem of Social Cost in Multi-Agent General Reinforcement
Learning: Survey and Synthesis | [
"cs.AI",
"cs.GT",
"cs.LG",
"cs.MA"
] | The AI safety literature is full of examples of powerful AI agents that, in blindly pursuing a specific and usually narrow objective, ends up with unacceptable and even catastrophic collateral damage to others. In this paper, we consider the problem of social harms that can result from actions taken by learning and uti... | {
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2412.02094 | Crash Severity Risk Modeling Strategies under Data Imbalance | [
"cs.LG",
"cs.CY",
"stat.AP"
] | This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) when there are crash data imbalance between low-severity (LS) and high-severity (HS) crashes. We utilized crash data, involving large vehicles in South Carolina work zones for the peri... | {
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2412.02097 | Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale
Financial Tabular Data | [
"cs.LG"
] | Tabular data plays a critical role in real-world financial scenarios. Traditionally, tree models have dominated in handling tabular data. However, financial datasets in the industry often encounter some challenges, such as data heterogeneity, the predominance of numerical features and the large scale of the data, which... | {
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2412.02099 | AccDiffusion v2: Towards More Accurate Higher-Resolution Diffusion
Extrapolation | [
"cs.CV",
"cs.AI"
] | Diffusion models suffer severe object repetition and local distortion when the inference resolution differs from its pre-trained resolution. We propose AccDiffusion v2, an accurate method for patch-wise higher-resolution diffusion extrapolation without training. Our in-depth analysis in this paper shows that using an i... | {
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2412.02101 | Improving Language Transfer Capability of Decoder-only Architecture in
Multilingual Neural Machine Translation | [
"cs.CL"
] | Existing multilingual neural machine translation (MNMT) approaches mainly focus on improving models with the encoder-decoder architecture to translate multiple languages. However, decoder-only architecture has been explored less in MNMT due to its underperformance when trained on parallel data solely. In this work, we ... | {
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2412.02104 | Explainable and Interpretable Multimodal Large Language Models: A
Comprehensive Survey | [
"cs.CL"
] | The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in natural language understanding and visual processing, respectively. The convergence of these technologies has catalyzed the rise of multimo... | {
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2412.02108 | Evaluating the Impact of Data Augmentation on Predictive Model
Performance | [
"cs.LG",
"cs.CY"
] | In supervised machine learning (SML) research, large training datasets are essential for valid results. However, obtaining primary data in learning analytics (LA) is challenging. Data augmentation can address this by expanding and diversifying data, though its use in LA remains underexplored. This paper systematically ... | {
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2412.02109 | Direct Coloring for Self-Supervised Enhanced Feature Decoupling | [
"cs.CV"
] | The success of self-supervised learning (SSL) has been the focus of multiple recent theoretical and empirical studies, including the role of data augmentation (in feature decoupling) as well as complete and dimensional representation collapse. While complete collapse is well-studied and addressed, dimensional collapse ... | {
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2412.02112 | Machine Learning Methods for Automated Interstellar Object
Classification with LSST | [
"astro-ph.EP",
"astro-ph.GA",
"astro-ph.IM",
"cs.LG"
] | The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for stud... | {
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2412.02113 | Trust & Safety of LLMs and LLMs in Trust & Safety | [
"cs.AI"
] | In recent years, Large Language Models (LLMs) have garnered considerable attention for their remarkable abilities in natural language processing tasks. However, their widespread adoption has raised concerns pertaining to trust and safety. This systematic review investigates the current research landscape on trust and s... | {
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2412.02114 | OmniCreator: Self-Supervised Unified Generation with Universal Editing | [
"cs.CV",
"cs.AI"
] | We introduce OmniCreator, a novel framework that can conduct text-prompted unified (image+video) generation as well as editing all in one place. OmniCreator acquires generative and universal editing capabilities in a self-supervised manner, taking original text-video pairs as conditions while utilizing the same video a... | {
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2412.02116 | ILASH: A Predictive Neural Architecture Search Framework for Multi-Task
Applications | [
"cs.LG",
"cs.CV"
] | Artificial intelligence (AI) is widely used in various fields including healthcare, autonomous vehicles, robotics, traffic monitoring, and agriculture. Many modern AI applications in these fields are multi-tasking in nature (i.e. perform multiple analysis on same data) and are deployed on resource-constrained edge devi... | {
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2412.02119 | Understanding Particles From Video: Property Estimation of Granular
Materials via Visuo-Haptic Learning | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Granular materials (GMs) are ubiquitous in daily life. Understanding their properties is also important, especially in agriculture and industry. However, existing works require dedicated measurement equipment and also need large human efforts to handle a large number of particles. In this paper, we introduce a method f... | {
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2412.02121 | Rethinking Self-Supervised Learning Within the Framework of Partial
Information Decomposition | [
"cs.CV"
] | Self Supervised learning (SSL) has demonstrated its effectiveness in feature learning from unlabeled data. Regarding this success, there have been some arguments on the role that mutual information plays within the SSL framework. Some works argued for increasing mutual information between representation of augmented vi... | {
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} |
2412.02122 | Improving Sequential Recommender Systems with Online and In-store User
Behavior | [
"cs.IR"
] | Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shopping. However, the growing transition between online and in-store becomes a challenge to sequential recommender systems for future online inter... | {
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} |
2412.02125 | Optimizing Latent Goal by Learning from Trajectory Preference | [
"cs.AI",
"cs.LG"
] | A glowing body of work has emerged focusing on instruction-following policies for open-world agents, aiming to better align the agent's behavior with human intentions. However, the performance of these policies is highly susceptible to the initial prompt, which leads to extra efforts in selecting the best instructions.... | {
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} |
2412.02126 | Benchmarking symbolic regression constant optimization schemes | [
"cs.LG",
"cs.AI",
"physics.comp-ph"
] | Symbolic regression is a machine learning technique, and it has seen many advancements in recent years, especially in genetic programming approaches (GPSR). Furthermore, it has been known for many years that constant optimization of parameters, during the evolutionary search, greatly increases GPSR performance However,... | {
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} |
2412.02127 | Streamlining Video Analysis for Efficient Violence Detection | [
"cs.CV"
] | This paper addresses the challenge of automated violence detection in video frames captured by surveillance cameras, specifically focusing on classifying scenes as "fight" or "non-fight." This task is critical for enhancing unmanned security systems, online content filtering, and related applications. We propose an app... | {
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} |
2412.02129 | GSOT3D: Towards Generic 3D Single Object Tracking in the Wild | [
"cs.CV"
] | In this paper, we present a novel benchmark, GSOT3D, that aims at facilitating development of generic 3D single object tracking (SOT) in the wild. Specifically, GSOT3D offers 620 sequences with 123K frames, and covers a wide selection of 54 object categories. Each sequence is offered with multiple modalities, including... | {
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} |
2412.02130 | A privacy-preserving distributed credible evidence fusion algorithm for
collective decision-making | [
"cs.AI"
] | The theory of evidence reasoning has been applied to collective decision-making in recent years. However, existing distributed evidence fusion methods lead to participants' preference leakage and fusion failures as they directly exchange raw evidence and do not assess evidence credibility like centralized credible evid... | {
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} |
2412.02136 | Graph Learning for Planning: The Story Thus Far and Open Challenges | [
"cs.AI"
] | Graph learning is naturally well suited for use in planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrary number of objects. In this paper, we study the usage of graph learning for planning thus far by studying the theoretical an... | {
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} |
2412.02137 | From Pixels to Planes: Minimum Ground Sample Distance for Aircraft | [
"cs.CV"
] | This study investigates the impact of ground sample distance (GSD) on the detection performance of various sized aircraft using the proprietary AllPlanes 120 dataset. The data set comprises 120 civilian, military and museum aircraft from multiple satellite/aerial sources collected over two years. Resolutions ranging fr... | {
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} |
2412.02138 | Misalignment of Semantic Relation Knowledge between WordNet and Human
Intuition | [
"cs.CL"
] | WordNet provides a carefully constructed repository of semantic relations, created by specialists. But there is another source of information on semantic relations, the intuition of language users. We present the first systematic study of the degree to which these two sources are aligned. Investigating the cases of mis... | {
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} |
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