id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.00037 | Effects of Turbulence Modeling and Parcel Approach on Dispersed
Two-Phase Swirling Flow | [
"physics.flu-dyn",
"cs.CE",
"cs.NA",
"math.NA"
] | Several numerical simulations of a co-axial particle-laden swirling air flow in a vertical circular pipe were performed. The air flow was modeled using the unsteady Favre-averaged Navier-Stokes equations. A Lagrangian model was used for the particle motion. The gas and particles are coupled through two-way momentum exc... | {
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2501.00038 | Sound-Based Recognition of Touch Gestures and Emotions for Enhanced
Human-Robot Interaction | [
"cs.HC",
"cs.RO",
"cs.SD",
"eess.AS"
] | Emotion recognition and touch gesture decoding are crucial for advancing human-robot interaction (HRI), especially in social environments where emotional cues and tactile perception play important roles. However, many humanoid robots, such as Pepper, Nao, and Furhat, lack full-body tactile skin, limiting their ability ... | {
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2501.00039 | Speech Recognition With LLMs Adapted to Disordered Speech Using
Reinforcement Learning | [
"eess.AS",
"cs.CL",
"cs.LG",
"cs.SD"
] | We introduce a large language model (LLM) capable of processing speech inputs and show that tuning it further with reinforcement learning on human preference (RLHF) enables it to adapt better to disordered speech than traditional fine-tuning. Our method replaces low-frequency text tokens in an LLM's vocabulary with aud... | {
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2501.00042 | Resource-Efficient Transformer Architecture: Optimizing Memory and
Execution Time for Real-Time Applications | [
"cs.LG",
"cs.AI"
] | This paper describes a memory-efficient transformer model designed to drive a reduction in memory usage and execution time by substantial orders of magnitude without impairing the model's performance near that of the original model. Recently, new architectures of transformers were presented, focused on parameter effici... | {
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2501.00045 | Cross-Linguistic Examination of Machine Translation Transfer Learning | [
"cs.CL",
"cs.LG"
] | This study investigates the effectiveness of transfer learning in machine translation across diverse linguistic families by evaluating five distinct language pairs. Leveraging pre-trained models on high-resource languages, these models were fine-tuned on low-resource languages, examining variations in hyperparameters s... | {
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2501.00046 | Numerical solutions of fixed points in two-dimensional
Kuramoto-Sivashinsky equation expedited by reinforcement learning | [
"cs.LG"
] | This paper presents a combined approach to enhancing the effectiveness of Jacobian-Free Newton-Krylov (JFNK) method by deep reinforcement learning (DRL) in identifying fixed points within the 2D Kuramoto-Sivashinsky Equation (KSE). JFNK approach entails a good initial guess for improved convergence when searching for f... | {
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2501.00048 | Stroke Prediction using Clinical and Social Features in Machine Learning | [
"cs.LG",
"cs.AI"
] | Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. While individual factors vary, certain predictors are more prevalent in determining stroke risk. As strokes are the second leading cause of death and disability worldwide, predi... | {
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2501.00049 | Seq2Seq Model-Based Chatbot with LSTM and Attention Mechanism for
Enhanced User Interaction | [
"cs.CL",
"cs.ET"
] | A chatbot is an intelligent software application that automates conversations and engages users in natural language through messaging platforms. Leveraging artificial intelligence (AI), chatbots serve various functions, including customer service, information gathering, and casual conversation. Existing virtual assista... | {
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2501.00050 | Learning in Multiple Spaces: Few-Shot Network Attack Detection with
Metric-Fused Prototypical Networks | [
"cs.CR",
"cs.LG"
] | Network intrusion detection systems face significant challenges in identifying emerging attack patterns, especially when limited data samples are available. To address this, we propose a novel Multi-Space Prototypical Learning (MSPL) framework tailored for few-shot attack detection. The framework operates across multip... | {
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2501.00051 | DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY"
] | Digital twin (DT) technology has emerged as a transformative approach to simulate, predict, and optimize the behavior of physical systems, with applications that span manufacturing, healthcare, climate science, and more. However, the development of DT models often faces challenges such as high data requirements, integr... | {
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2501.00052 | Efficient and Scalable Deep Reinforcement Learning for Mean Field
Control Games | [
"cs.LG",
"cs.GT",
"cs.MA"
] | Mean Field Control Games (MFCGs) provide a powerful theoretical framework for analyzing systems of infinitely many interacting agents, blending elements from Mean Field Games (MFGs) and Mean Field Control (MFC). However, solving the coupled Hamilton-Jacobi-Bellman and Fokker-Planck equations that characterize MFCG equi... | {
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2501.00053 | Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a
Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images | [
"eess.IV",
"cs.AI",
"cs.LG"
] | Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns ari... | {
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2501.00054 | AdvAnchor: Enhancing Diffusion Model Unlearning with Adversarial Anchors | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Security concerns surrounding text-to-image diffusion models have driven researchers to unlearn inappropriate concepts through fine-tuning. Recent fine-tuning methods typically align the prediction distributions of unsafe prompts with those of predefined text anchors. However, these techniques exhibit a considerable pe... | {
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2501.00055 | LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models | [
"cs.CR",
"cs.AI",
"cs.CL"
] | While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems, they still suffer from potential adversarial queries, such as jailbreak attacks, which attempt to induce harmful content. Researching attac... | {
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2501.00056 | Transforming CCTV cameras into NO$_2$ sensors at city scale for adaptive
policymaking | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Air pollution in cities, especially NO\textsubscript{2}, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental... | {
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2501.00057 | VisTabNet: Adapting Vision Transformers for Tabular Data | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Although deep learning models have had great success in natural language processing and computer vision, we do not observe comparable improvements in the case of tabular data, which is still the most common data type used in biological, industrial and financial applications. In particular, it is challenging to transfer... | {
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2501.00059 | Large Language Models for Mathematical Analysis | [
"cs.CL",
"cs.AI"
] | Mathematical problem-solving is a key field in artificial intelligence (AI) and a critical benchmark for evaluating the capabilities of large language models (LLMs). While extensive research has focused on mathematical problem-solving, most existing work and datasets concentrate on computational tasks, leaving gaps in ... | {
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2501.00061 | Training-free Heterogeneous Model Merging | [
"cs.LG",
"cs.AI"
] | Model merging has attracted significant attention as a powerful paradigm for model reuse, facilitating the integration of task-specific models into a singular, versatile framework endowed with multifarious capabilities. Previous studies, predominantly utilizing methods such as Weight Average (WA), have shown that model... | {
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2501.00062 | ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis | [
"cs.CL",
"cs.AI"
] | Bidirectional transformers excel at sentiment analysis, and Large Language Models (LLM) are effective zero-shot learners. Might they perform better as a team? This paper explores collaborative approaches between ELECTRA and GPT-4o for three-way sentiment classification. We fine-tuned (FT) four models (ELECTRA Base/Larg... | {
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2501.00063 | "Generative Models for Financial Time Series Data: Enhancing
Signal-to-Noise Ratio and Addressing Data Scarcity in A-Share Market | [
"cs.LG",
"cs.AI"
] | The financial industry is increasingly seeking robust methods to address the challenges posed by data scarcity and low signal-to-noise ratios, which limit the application of deep learning techniques in stock market analysis. This paper presents two innovative generative model-based approaches to synthesize stock data, ... | {
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2501.00064 | Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound
Classification | [
"cs.SD",
"cs.LG",
"eess.AS"
] | Respiratory sound classification plays a pivotal role in diagnosing respiratory diseases. While deep learning models have shown success with various respiratory sound datasets, our experiments indicate that models trained on one dataset often fail to generalize effectively to others, mainly due to data collection and a... | {
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2501.00065 | Predicting Preschoolers' Externalizing Problems with Mother-Child
Interaction Dynamics and Deep Learning | [
"cs.LG",
"cs.AI"
] | Objective: Predicting children's future levels of externalizing problems helps to identify children at risk and guide targeted prevention. Existing studies have shown that mothers providing support in response to children's dysregulation was associated with children's lower levels of externalizing problems. The current... | {
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2501.00066 | On Adversarial Robustness of Language Models in Transfer Learning | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | We investigate the adversarial robustness of LLMs in transfer learning scenarios. Through comprehensive experiments on multiple datasets (MBIB Hate Speech, MBIB Political Bias, MBIB Gender Bias) and various model architectures (BERT, RoBERTa, GPT-2, Gemma, Phi), we reveal that transfer learning, while improving standar... | {
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2501.00067 | Ensemble of classifiers for speech evaluation | [
"cs.SD",
"cs.AI",
"eess.AS"
] | The article describes an attempt to apply an ensemble of binary classifiers to solve the problem of speech assessment in medicine. A dataset was compiled based on quantitative and expert assessments of syllable pronunciation quality. Quantitative assessments of 7 selected metrics were used as features: dynamic time war... | {
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2501.00068 | Dynamic Optimization of Storage Systems Using Reinforcement Learning
Techniques | [
"cs.OS",
"cs.DC",
"cs.LG"
] | The exponential growth of data-intensive applications has placed unprecedented demands on modern storage systems, necessitating dynamic and efficient optimization strategies. Traditional heuristics employed for storage performance optimization often fail to adapt to the variability and complexity of contemporary worklo... | {
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2501.00069 | Adversarial Negotiation Dynamics in Generative Language Models | [
"cs.CL",
"cs.AI"
] | Generative language models are increasingly used for contract drafting and enhancement, creating a scenario where competing parties deploy different language models against each other. This introduces not only a game-theory challenge but also significant concerns related to AI safety and security, as the language model... | {
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2501.00070 | ICLR: In-Context Learning of Representations | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recent work has demonstrated that semantics specified by pretraining data influence how representations of different concepts are organized in a large language model (LLM). However, given the open-ended nature of LLMs, e.g., their ability to in-context learn, we can ask whether models alter these pretraining semantics ... | {
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2501.00072 | Open-Book Neural Algorithmic Reasoning | [
"cs.LG",
"cs.AI"
] | Neural algorithmic reasoning is an emerging area of machine learning that focuses on building neural networks capable of solving complex algorithmic tasks. Recent advancements predominantly follow the standard supervised learning paradigm -- feeding an individual problem instance into the network each time and training... | {
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2501.00073 | Position Information Emerges in Causal Transformers Without Positional
Encodings via Similarity of Nearby Embeddings | [
"cs.CL",
"cs.LG"
] | Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more simil... | {
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2501.00076 | A Novel Framework for Learning Stochastic Representations for Sequence
Generation and Recognition | [
"cs.LG",
"cs.AI",
"cs.RO"
] | The ability to generate and recognize sequential data is fundamental for autonomous systems operating in dynamic environments. Inspired by the key principles of the brain-predictive coding and the Bayesian brain-we propose a novel stochastic Recurrent Neural Network with Parametric Biases (RNNPB). The proposed model in... | {
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2501.00078 | Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors | [
"cs.HC",
"cs.AI",
"cs.LG"
] | Artificial intelligence (AI) has enabled agents to master complex video games, from first-person shooters like Counter-Strike to real-time strategy games such as StarCraft II and racing games like Gran Turismo. While these achievements are notable, applying these AI methods in commercial video game production remains c... | {
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2501.00083 | AI Agent for Education: von Neumann Multi-Agent System Framework | [
"cs.MA",
"cs.AI",
"cs.CY"
] | The development of large language models has ushered in new paradigms for education. This paper centers on the multi-Agent system in education and proposes the von Neumann multi-Agent system framework. It breaks down each AI Agent into four modules: control unit, logic unit, storage unit, and input-output devices, defi... | {
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2501.00085 | Machine Learning-Based Security Policy Analysis | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Security-Enhanced Linux (SELinux) is a robust security mechanism that enforces mandatory access controls (MAC), but its policy language's complexity creates challenges for policy analysis and management. This research investigates the automation of SELinux policy analysis using graph-based techniques combined with mach... | {
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2501.00087 | High-Dimensional Markov-switching Ordinary Differential Processes | [
"stat.ME",
"cs.LG",
"math.ST",
"stat.AP",
"stat.TH"
] | We investigate the parameter recovery of Markov-switching ordinary differential processes from discrete observations, where the differential equations are nonlinear additive models. This framework has been widely applied in biological systems, control systems, and other domains; however, limited research has been condu... | {
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2501.00089 | Insights on Galaxy Evolution from Interpretable Sparse Feature Networks | [
"astro-ph.GA",
"cs.LG"
] | Galaxy appearances reveal the physics of how they formed and evolved. Machine learning models can now exploit galaxies' information-rich morphologies to predict physical properties directly from image cutouts. Learning the relationship between pixel-level features and galaxy properties is essential for building a physi... | {
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2501.00093 | Machine Learning Gravity Compactifications on Negatively Curved
Manifolds | [
"hep-th",
"cs.LG",
"gr-qc"
] | Constructing the landscape of vacua of higher-dimensional theories of gravity by directly solving the low-energy (semi-)classical equations of motion is notoriously difficult. In this work, we investigate the feasibility of Machine Learning techniques as tools for solving the equations of motion for general warped grav... | {
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2501.00097 | CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S.
Supreme Court Opinions | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | This paper introduces CaseSumm, a novel dataset for long-context summarization in the legal domain that addresses the need for longer and more complex datasets for summarization evaluation. We collect 25.6K U.S. Supreme Court (SCOTUS) opinions and their official summaries, known as "syllabuses." Our dataset is the larg... | {
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2501.00103 | LTX-Video: Realtime Video Latent Diffusion | [
"cs.CV"
] | We introduce LTX-Video, a transformer-based latent diffusion model that adopts a holistic approach to video generation by seamlessly integrating the responsibilities of the Video-VAE and the denoising transformer. Unlike existing methods, which treat these components as independent, LTX-Video aims to optimize their int... | {
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2501.00106 | LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset
License Compliance | [
"cs.SE",
"cs.AI"
] | Dataset license compliance is a critical yet complex aspect of developing commercial AI products, particularly with the increasing use of publicly available datasets. Ambiguities in dataset licenses pose significant legal risks, making it challenging even for software IP lawyers to accurately interpret rights and oblig... | {
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2501.00107 | An Unsupervised Anomaly Detection in Electricity Consumption Using
Reinforcement Learning and Time Series Forest Based Framework | [
"cs.LG",
"cs.AI"
] | Anomaly detection (AD) plays a crucial role in time series applications, primarily because time series data is employed across real-world scenarios. Detecting anomalies poses significant challenges since anomalies take diverse forms making them hard to pinpoint accurately. Previous research has explored different AD mo... | {
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2501.00110 | Modelling and Control of Spatial Behaviours in Multi-Agent Systems with
Applications to Biology and Robotics | [
"eess.SY",
"cs.MA",
"cs.RO",
"cs.SY"
] | Large-Scale Multi-Agent Systems (LS-MAS) consist of several autonomous components, interacting in a non-trivial way, so that the emerging behaviour of the ensemble depends on the individual dynamics of the components and their reciprocal interactions. These models can describe a rich variety of natural systems, as well... | {
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2501.00112 | Steppability-informed Quadrupedal Contact Planning through Deep Visual
Search Heuristics | [
"cs.RO"
] | In this work, we introduce a method for predicting environment steppability -- the ability of a legged robot platform to place a foothold at a particular location in the local environment -- in the image space. This novel environment representation captures this critical geometric property of the local terrain while al... | {
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2501.00113 | AltGen: AI-Driven Alt Text Generation for Enhancing EPUB Accessibility | [
"cs.AI"
] | Digital accessibility is a cornerstone of inclusive content delivery, yet many EPUB files fail to meet fundamental accessibility standards, particularly in providing descriptive alt text for images. Alt text plays a critical role in enabling visually impaired users to understand visual content through assistive technol... | {
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2501.00116 | Text-to-Image GAN with Pretrained Representations | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Generating desired images conditioned on given text descriptions has received lots of attention. Recently, diffusion models and autoregressive models have demonstrated their outstanding expressivity and gradually replaced GAN as the favored architectures for text-to-image synthesis. However, they still face some obstac... | {
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2501.00119 | Post Launch Evaluation of Policies in a High-Dimensional Setting | [
"stat.ML",
"cs.LG",
"stat.AP",
"stat.ME"
] | A/B tests, also known as randomized controlled experiments (RCTs), are the gold standard for evaluating the impact of new policies, products, or decisions. However, these tests can be costly in terms of time and resources, potentially exposing users, customers, or other test subjects (units) to inferior options. This p... | {
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2501.00124 | PQD: Post-training Quantization for Efficient Diffusion Models | [
"cs.CV",
"cs.LG"
] | Diffusionmodels(DMs)havedemonstratedremarkableachievements in synthesizing images of high fidelity and diversity. However, the extensive computational requirements and slow generative speed of diffusion models have limited their widespread adoption. In this paper, we propose a novel post-training quantization for diffu... | {
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2501.00129 | A Data-Centric Approach to Detecting and Mitigating Demographic Bias in
Pediatric Mental Health Text: A Case Study in Anxiety Detection | [
"cs.CL",
"cs.AI"
] | Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of... | {
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2501.00135 | GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum
Searching | [
"quant-ph",
"cs.AI",
"cs.LG"
] | Quantum computing is an exciting non-Von Neumann paradigm, offering provable speedups over classical computing for specific problems. However, the practical limits of classical simulatability for quantum circuits remain unclear, especially with current noisy quantum devices. In this work, we explore the potential of le... | {
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2501.00136 | Detection-Fusion for Knowledge Graph Extraction from Videos | [
"cs.CV",
"cs.AI",
"cs.LG"
] | One of the challenging tasks in the field of video understanding is extracting semantic content from video inputs. Most existing systems use language models to describe videos in natural language sentences, but this has several major shortcomings. Such systems can rely too heavily on the language model component and ba... | {
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2501.00138 | NiaAutoARM: Automated generation and evaluation of Association Rule
Mining pipelines | [
"cs.NE",
"cs.AI"
] | The Numerical Association Rule Mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets consisting of both features. The process is not considered as easy since it incorporates several processing steps running sequentially that f... | {
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2501.00142 | Minimalist Vision with Freeform Pixels | [
"cs.CV",
"eess.IV"
] | A minimalist vision system uses the smallest number of pixels needed to solve a vision task. While traditional cameras use a large grid of square pixels, a minimalist camera uses freeform pixels that can take on arbitrary shapes to increase their information content. We show that the hardware of a minimalist camera can... | {
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2501.00149 | LASSE: Learning Active Sampling for Storm Tide Extremes in
Non-Stationary Climate Regimes | [
"physics.ao-ph",
"cs.LG",
"physics.geo-ph"
] | Identifying tropical cyclones that generate destructive storm tides for risk assessment, such as from large downscaled storm catalogs for climate studies, is often intractable because it entails many expensive Monte Carlo hydrodynamic simulations. Here, we show that surrogate models are promising from accuracy, recall,... | {
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2501.00152 | Temporal reasoning for timeline summarisation in social media | [
"cs.CL",
"cs.AI"
] | This paper explores whether enhancing temporal reasoning capabilities in Large Language Models (LLMs) can improve the quality of timeline summarisation, the task of summarising long texts containing sequences of events, such as social media threads. We first introduce NarrativeReason, a novel dataset focused on tempora... | {
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2501.00154 | Probabilistic Explanations for Linear Models | [
"cs.AI",
"cs.CC"
] | Formal XAI is an emerging field that focuses on providing explanations with mathematical guarantees for the decisions made by machine learning models. A significant amount of work in this area is centered on the computation of "sufficient reasons". Given a model $M$ and an input instance $\vec{x}$, a sufficient reason ... | {
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2501.00158 | Urban Water Consumption Forecasting Using Deep Learning and Correlated
District Metered Areas | [
"cs.LG",
"cs.CY"
] | Accurate water consumption forecasting is a crucial tool for water utilities and policymakers, as it helps ensure a reliable supply, optimize operations, and support infrastructure planning. Urban Water Distribution Networks (WDNs) are divided into District Metered Areas (DMAs), where water flow is monitored to efficie... | {
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2501.00160 | Deterministic Model of Incremental Multi-Agent Boltzmann Q-Learning:
Transient Cooperation, Metastability, and Oscillations | [
"cs.MA",
"nlin.AO",
"physics.soc-ph"
] | Multi-Agent Reinforcement Learning involves agents that learn together in a shared environment, leading to emergent dynamics sensitive to initial conditions and parameter variations. A Dynamical Systems approach, which studies the evolution of multi-component systems over time, has uncovered some of the underlying dyna... | {
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2501.00162 | Class-based Subset Selection for Transfer Learning under Extreme Label
Shift | [
"cs.LG",
"cs.AI"
] | Existing work within transfer learning often follows a two-step process -- pre-training over a large-scale source domain and then finetuning over limited samples from the target domain. Yet, despite its popularity, this methodology has been shown to suffer in the presence of distributional shift -- specifically when th... | {
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2501.00164 | Measuring Large Language Models Capacity to Annotate Journalistic
Sourcing | [
"cs.CL",
"cs.CY"
] | Since the launch of ChatGPT in late 2022, the capacities of Large Language Models and their evaluation have been in constant discussion and evaluation both in academic research and in the industry. Scenarios and benchmarks have been developed in several areas such as law, medicine and math (Bommasani et al., 2023) and ... | {
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2501.00165 | Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement
Learning | [
"cs.MA"
] | This work presents a novel communication framework for decentralized multi-agent systems operating in dynamic network environments. Integrated into a multi-agent reinforcement learning system, the framework is designed to enhance decision-making by optimizing the network's collective knowledge through efficient communi... | {
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2501.00167 | On Functional Observability of Nonlinear Systems and the Design of
Functional Observers with Assignable Error Dynamics | [
"eess.SY",
"cs.SY"
] | This paper proposes a novel approach for designing functional observers for nonlinear systems, with linear error dynamics and assignable poles. Sufficient conditions for functional observability are first derived, leading to functional relationships between the Lie derivatives of the output to be estimated and the ones... | {
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2501.00169 | DeepLL: Considering Linear Logic for the Analysis of Deep Learning
Experiments | [
"cs.PL",
"cs.AI",
"cs.CL",
"cs.SE"
] | Deep Learning experiments have critical requirements regarding the careful handling of their datasets as well as the efficient and correct usage of APIs that interact with hardware accelerators. On the one hand, software mistakes during data handling can contaminate experiments and lead to incorrect results. On the oth... | {
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2501.00170 | Federated Learning with Workload Reduction through Partial Training of
Client Models and Entropy-Based Data Selection | [
"cs.LG",
"cs.AI",
"cs.DC"
] | With the rapid expansion of edge devices, such as IoT devices, where crucial data needed for machine learning applications is generated, it becomes essential to promote their participation in privacy-preserving Federated Learning (FL) systems. The best way to achieve this desiderate is by reducing their training worklo... | {
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2501.00172 | Algebraic Control: Complete Stable Inversion with Necessary and
Sufficient Conditions | [
"math.OC",
"cs.SY",
"eess.SY"
] | Recent advances in learning-based control have increased interest in stable inversion to meet growing performance demands. Here, we establish necessary and sufficient conditions for stable inversion, addressing challenges in non-minimum phase, non-square, and singular systems. An H-Infinity based algebraic approximatio... | {
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2501.00174 | The Text Classification Pipeline: Starting Shallow going Deeper | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Text Classification (TC) stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through the lens of computer science and engineering. The past decade has seen deep learning revolutionize TC, propelling advancements in text retrieval, categorization, information extractio... | {
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2501.00184 | TrajLearn: Trajectory Prediction Learning using Deep Generative Models | [
"cs.LG",
"cs.CV",
"cs.RO"
] | Trajectory prediction aims to estimate an entity's future path using its current position and historical movement data, benefiting fields like autonomous navigation, robotics, and human movement analytics. Deep learning approaches have become key in this area, utilizing large-scale trajectory datasets to model movement... | {
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2501.00190 | SepsisCalc: Integrating Clinical Calculators into Early Sepsis
Prediction via Dynamic Temporal Graph Construction | [
"cs.LG",
"cs.AI",
"cs.HC"
] | Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improved clinical outcomes. Clinical calculators (e.g., the six-organ dysfunction assessment of SOFA) play a vital role in sepsis identification wi... | {
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2501.00191 | Equilibria in Network Constrained Markets with Market Maker | [
"cs.GT",
"cs.MA",
"cs.SI",
"cs.SY",
"eess.SY",
"math.OC"
] | We study a networked economic system composed of $n$ producers supplying a single homogeneous good to a number of geographically separated markets and of a centralized authority, called the market maker. Producers compete \`a la Cournot, by choosing the quantities of good to supply to each market they have access to in... | {
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2501.00192 | MLLM-as-a-Judge for Image Safety without Human Labeling | [
"cs.CV",
"cs.CL",
"cs.CY",
"cs.LG"
] | Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identi... | {
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2501.00193 | A Pseudo-random Number Generator for Multi-Sequence Generation with
Programmable Statistics | [
"cs.CR",
"cs.IT",
"math.IT"
] | Pseudo-random number generators (PRNGs) are essential in a wide range of applications, from cryptography to statistical simulations and optimization algorithms. While uniform randomness is crucial for security-critical areas like cryptography, many domains, such as simulated annealing and CMOS-based Ising Machines, ben... | {
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2501.00195 | Towards Unraveling and Improving Generalization in World Models | [
"cs.LG",
"cs.AI"
] | World models have recently emerged as a promising approach to reinforcement learning (RL), achieving state-of-the-art performance across a wide range of visual control tasks. This work aims to obtain a deep understanding of the robustness and generalization capabilities of world models. Thus motivated, we develop a sto... | {
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2501.00199 | GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study | [
"cs.CL",
"cs.AI"
] | Depression has impacted millions of people worldwide and has become one of the most prevalent mental disorders. Early mental disorder detection can lead to cost savings for public health agencies and avoid the onset of other major comorbidities. Additionally, the shortage of specialized personnel is a critical issue be... | {
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2501.00200 | Scalable Neural Network Verification with Branch-and-bound Inferred
Cutting Planes | [
"cs.LG",
"cs.CR",
"math.OC"
] | Recently, cutting-plane methods such as GCP-CROWN have been explored to enhance neural network verifiers and made significant advances. However, GCP-CROWN currently relies on generic cutting planes (cuts) generated from external mixed integer programming (MIP) solvers. Due to the poor scalability of MIP solvers, large ... | {
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2501.00201 | Hierarchical Functionality Prioritization in Multicast ISAC: Optimal
Admission Control and Discrete-Phase Beamforming | [
"eess.SP",
"cs.IT",
"math.IT"
] | We investigate the joint admission control and discrete-phase multicast beamforming design for integrated sensing and communications (ISAC) systems, where sensing and communications functionalities have different hierarchies. Specifically, the ISAC system first allocates resources to the higher-hierarchy functionality ... | {
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2501.00204 | MSM-BD: Multimodal Social Media Bot Detection Using Heterogeneous
Information | [
"cs.MM",
"cs.SI"
] | Although social bots can be engineered for constructive applications, their potential for misuse in manipulative schemes and malware distribution cannot be overlooked. This dichotomy underscores the critical need to detect social bots on social media platforms. Advances in artificial intelligence have improved the abil... | {
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2501.00208 | An Empirical Evaluation of Large Language Models on Consumer Health
Questions | [
"cs.CL",
"cs.AI"
] | This study evaluates the performance of several Large Language Models (LLMs) on MedRedQA, a dataset of consumer-based medical questions and answers by verified experts extracted from the AskDocs subreddit. While LLMs have shown proficiency in clinical question answering (QA) benchmarks, their effectiveness on real-worl... | {
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2501.00210 | Debunking the CUDA Myth Towards GPU-based AI Systems | [
"cs.DC",
"cs.AI",
"cs.AR"
] | With the rise of AI, NVIDIA GPUs have become the de facto standard for AI system design. This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs for AI model serving. First, we create a suite of microbenchmarks to compare Intel Gaudi-2 with NVIDIA A100, showing that Gaudi-2 a... | {
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2501.00214 | OciorMVBA: Near-Optimal Error-Free Asynchronous MVBA | [
"cs.CR",
"cs.DC",
"cs.IT",
"math.IT"
] | In this work, we propose an error-free, information-theoretically secure, asynchronous multi-valued validated Byzantine agreement (MVBA) protocol, called OciorMVBA. This protocol achieves MVBA consensus on a message $\boldsymbol{w}$ with expected $O(n |\boldsymbol{w}|\log n + n^2 \log q)$ communication bits, expected $... | {
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2501.00217 | The Potential of LLMs in Automating Software Testing: From Generation to
Reporting | [
"cs.SE",
"cs.AI"
] | Having a high quality software is essential in software engineering, which requires robust validation and verification processes during testing activities. Manual testing, while effective, can be time consuming and costly, leading to an increased demand for automated methods. Recent advancements in Large Language Model... | {
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2501.00219 | Autonomous Minibus Service with Semi-on-demand Routes in Grid Networks | [
"eess.SY",
"cs.SY",
"math.OC"
] | This paper investigates the potential of autonomous minibuses which take on-demand directional routes for pick-up and drop-off in a grid network of wider area with low density, followed by fixed routes in areas with demand. Mathematical formulation for generalized costs demonstrates its benefits, with indicators propos... | {
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2501.00220 | DecoratingFusion: A LiDAR-Camera Fusion Network with the Combination of
Point-level and Feature-level Fusion | [
"cs.CV",
"cs.LG"
] | Lidars and cameras play essential roles in autonomous driving, offering complementary information for 3D detection. The state-of-the-art fusion methods integrate them at the feature level, but they mostly rely on the learned soft association between point clouds and images, which lacks interpretability and neglects the... | {
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2501.00223 | CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM
Hybrid for Assisting with Optimal Cancer Treatment and Care | [
"cs.AI",
"cs.IR",
"cs.LG"
] | Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer. It is currently being evaluated to assist with both medical research and clinical information retrieval tasks at Moffitt Cancer Center, whi... | {
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2501.00224 | Extracting effective solutions hidden in large language models via
generated comprehensive specialists: case studies in developing electronic
devices | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recently, many studies have increasingly explored the use of large language models (LLMs) to generate research ideas and scientific hypotheses. However, real-world research and development often require solving complex, interdisciplinary challenges where solutions may not be readily found through existing knowledge rel... | {
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2501.00226 | Generative Emergent Communication: Large Language Model is a Collective
World Model | [
"cs.AI",
"cs.CL"
] | This study proposes a unifying theoretical framework called generative emergent communication (generative EmCom) that bridges emergent communication, world models, and large language models (LLMs) through the lens of collective predictive coding (CPC). The proposed framework formalizes the emergence of language and sym... | {
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2501.00230 | Federated Deep Subspace Clustering | [
"cs.LG",
"cs.AI",
"cs.CR"
] | This paper introduces FDSC, a private-protected subspace clustering (SC) approach with federated learning (FC) schema. In each client, there is a deep subspace clustering network accounting for grouping the isolated data, composed of a encode network, a self-expressive layer, and a decode network. FDSC is achieved by u... | {
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} |
2501.00233 | Zero-Shot Strategies for Length-Controllable Summarization | [
"cs.CL"
] | Large language models (LLMs) struggle with precise length control, particularly in zero-shot settings. We conduct a comprehensive study evaluating LLMs' length control capabilities across multiple measures and propose practical methods to improve controllability. Our experiments with LLaMA 3 reveal stark differences in... | {
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2501.00237 | Make Domain Shift a Catastrophic Forgetting Alleviator in
Class-Incremental Learning | [
"cs.CV",
"cs.LG"
] | In the realm of class-incremental learning (CIL), alleviating the catastrophic forgetting problem is a pivotal challenge. This paper discovers a counter-intuitive observation: by incorporating domain shift into CIL tasks, the forgetting rate is significantly reduced. Our comprehensive studies demonstrate that incorpora... | {
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2501.00241 | Exploring Variability in Fine-Tuned Models for Text Classification with
DistilBERT | [
"cs.CL",
"cs.AI"
] | This study evaluates fine-tuning strategies for text classification using the DistilBERT model, specifically the distilbert-base-uncased-finetuned-sst-2-english variant. Through structured experiments, we examine the influence of hyperparameters such as learning rate, batch size, and epochs on accuracy, F1-score, and l... | {
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2501.00242 | Automotive Speed Estimation: Sensor Types and Error Characteristics from
OBD-II to ADAS | [
"eess.SP",
"cs.RO"
] | Modern on-road navigation systems heavily depend on integrating speed measurements with inertial navigation systems (INS) and global navigation satellite systems (GNSS). Telemetry-based applications typically source speed data from the On-Board Diagnostic II (OBD-II) system. However, the method of deriving speed, as we... | {
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2501.00243 | Cross-Layer Cache Aggregation for Token Reduction in Ultra-Fine-Grained
Image Recognition | [
"cs.CV"
] | Ultra-fine-grained image recognition (UFGIR) is a challenging task that involves classifying images within a macro-category. While traditional FGIR deals with classifying different species, UFGIR goes beyond by classifying sub-categories within a species such as cultivars of a plant. In recent times the usage of Vision... | {
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"cs.SY": 0
} |
2501.00244 | Have We Designed Generalizable Structural Knowledge Promptings?
Systematic Evaluation and Rethinking | [
"cs.CL"
] | Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) is a prominent paradigm to integrate external knowledge into LLMs by in... | {
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} |
2501.00249 | A Universal Controller for Grid-Tied Inverters | [
"eess.SY",
"cs.SY"
] | This paper presents the development of "Control-Sync," a novel firmware for universal inverters in microgrids, designed to enhance grid stability and flexibility. As hybrid PV-battery systems become increasingly prevalent, there is a critical need for inverters capable of efficiently transitioning between grid-forming ... | {
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} |
2501.00252 | Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph
Completion | [
"cs.LG",
"cs.DB",
"cs.IR"
] | Predicting missing facts for temporal knowledge graphs (TKGs) is a fundamental task, called temporal knowledge graph completion (TKGC). One key challenge in this task is the imbalance in data distribution, where facts are unevenly spread across entities and timestamps. This imbalance can lead to poor completion perform... | {
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} |
2501.00254 | Automatically Planning Optimal Parallel Strategy for Large Language
Models | [
"cs.AI",
"cs.CL"
] | The number of parameters in large-scale language models based on transformers is gradually increasing, and the scale of computing clusters is also growing. The technology of quickly mobilizing large amounts of computing resources for parallel computing is becoming increasingly important. In this paper, we propose an au... | {
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} |
2501.00257 | EQUATOR: A Deterministic Framework for Evaluating LLM Reasoning with
Open-Ended Questions. # v1.0.0-beta | [
"cs.CL"
] | Despite the remarkable coherence of Large Language Models (LLMs), existing evaluation methods often suffer from fluency bias and rely heavily on multiple-choice formats, making it difficult to assess factual accuracy and complex reasoning effectively. LLMs thus frequently generate factually inaccurate responses, especi... | {
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} |
2501.00258 | Optimal design of frame structures with mixed categorical and continuous
design variables using the Gumbel-Softmax method | [
"cs.CE",
"math.OC"
] | In optimizing real-world structures, due to fabrication or budgetary restraints, the design variables may be restricted to a set of standard engineering choices. Such variables, commonly called categorical variables, are discrete and unordered in essence, precluding the utilization of gradient-based optimizers for the ... | {
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} |
2501.00260 | Detection and Prevention of Smishing Attacks | [
"cs.CR",
"cs.SI"
] | Phishing is an online identity theft technique where attackers steal users personal information, leading to financial losses for individuals and organizations. With the increasing adoption of smartphones, which provide functionalities similar to desktop computers, attackers are targeting mobile users. Smishing, a phish... | {
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} |
2501.00261 | Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by
AI-Driven Threat Detection | [
"cs.CR",
"cs.AI"
] | The introduction sets the stage for exploring collaborative approaches to bolstering smart vehicle cybersecurity through AI-driven threat detection. As the automotive industry increasingly adopts connected and automated vehicles (CAVs), the need for robust cybersecurity measures becomes paramount. With the emergence of... | {
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} |
2501.00262 | Integrating Cascade Pumped Micro-Hydro Storage: A Sustainable Approach
to Energy and Water Management | [
"eess.SY",
"cs.SY"
] | As traditional large hydropower has been extensively exploited, micro-hydro systems have caught research increasing interest. New engineering challenges arise in developing micro-hydro systems in areas with significant elevation but prohibitive horizontal distances between primary reservoirs. This study addresses these... | {
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} |
2501.00264 | Enhancing Wireless Sensor Network Security through Integration with the
ServiceNow Cloud Platform | [
"cs.CR",
"cs.AI"
] | Wireless Sensor Networks (WSNs) continue to experience rapid developments and integration into modern-day applications. Overall, WSNs collect and process relevant data through sensors or nodes and communicate with different networks for superior information management. Nevertheless, a primary concern relative to WSNs i... | {
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} |
2501.00265 | Outlier-Robust Training of Machine Learning Models | [
"cs.LG",
"cs.CV"
] | Robust training of machine learning models in the presence of outliers has garnered attention across various domains. The use of robust losses is a popular approach and is known to mitigate the impact of outliers. We bring to light two literatures that have diverged in their ways of designing robust losses: one using M... | {
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} |
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