id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.06240 | The Convergence of Dynamic Routing between Capsules | [
"cs.LG",
"math.OC"
] | Capsule networks(CapsNet) are recently proposed neural network models with new processing layers, specifically for entity representation and discovery of images. It is well known that CapsNet have some advantages over traditional neural networks, especially in generalization capability. At the same time, some studies r... | {
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2501.06241 | Predicting House Rental Prices in Ghana Using Machine Learning | [
"cs.LG",
"stat.AP"
] | This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random ... | {
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2501.06242 | Intelligent Task Offloading: Advanced MEC Task Offloading and Resource
Management in 5G Networks | [
"cs.NI",
"cs.AI",
"cs.DC"
] | 5G technology enhances industries with high-speed, reliable, low-latency communication, revolutionizing mobile broadband and supporting massive IoT connectivity. With the increasing complexity of applications on User Equipment (UE), offloading resource-intensive tasks to robust servers is essential for improving latenc... | {
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2501.06243 | Agent TCP/IP: An Agent-to-Agent Transaction System | [
"cs.AI",
"cs.MA",
"cs.NI"
] | Autonomous agents represent an inevitable evolution of the internet. Current agent frameworks do not embed a standard protocol for agent-to-agent interaction, leaving existing agents isolated from their peers. As intellectual property is the native asset ingested by and produced by agents, a true agent economy requires... | {
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2501.06244 | Microservice Deployment in Space Computing Power Networks via Robust
Reinforcement Learning | [
"cs.NI",
"cs.AI",
"cs.DC",
"cs.LG"
] | With the growing demand for Earth observation, it is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements. The Space Computing Power Network (Space-CPN) offers a promising solution by providing onboard computing and extensive coverage capabilities for real-time ... | {
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2501.06246 | A partition cover approach to tokenization | [
"cs.CL",
"cs.AI",
"cs.DS"
] | Tokenization is the process of encoding strings into tokens from a fixed vocabulary of size $k$ and is widely utilized in Natural Language Processing applications. The leading tokenization algorithm today is Byte Pair Encoding (BPE), which formulates the tokenization problem as a compression problem and tackles it by p... | {
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2501.06247 | A Survey on Algorithmic Developments in Optimal Transport Problem with
Applications | [
"cs.DS",
"cs.AI",
"cs.LG",
"math.OC",
"stat.ML"
] | Optimal Transport (OT) has established itself as a robust framework for quantifying differences between distributions, with applications that span fields such as machine learning, data science, and computer vision. This paper offers a detailed examination of the OT problem, beginning with its theoretical foundations, i... | {
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2501.06248 | Utility-inspired Reward Transformations Improve Reinforcement Learning
Training of Language Models | [
"cs.LG",
"cs.AI",
"cs.CL",
"econ.GN",
"q-fin.EC"
] | Current methods that train large language models (LLMs) with reinforcement learning feedback, often resort to averaging outputs of multiple rewards functions during training. This overlooks crucial aspects of individual reward dimensions and inter-reward dependencies that can lead to sub-optimal outcomes in generations... | {
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2501.06249 | Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI | [
"cs.CV",
"astro-ph.IM"
] | Large-scale astronomical image data processing and prediction is essential for astronomers, providing crucial insights into celestial objects, the universe's history, and its evolution. While modern deep learning models offer high predictive accuracy, they often demand substantial computational resources, making them r... | {
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2501.06250 | Generative AI for Cel-Animation: A Survey | [
"cs.CV",
"cs.AI",
"cs.HC"
] | Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colorization, which demand substantial manual effort, technical expertise, and significant time investment. These challenges have historically imp... | {
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2501.06252 | Transformer-Squared: Self-adaptive LLMs | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Self-adaptive large language models (LLMs) aim to solve the challenges posed by traditional fine-tuning methods, which are often computationally intensive and static in their ability to handle diverse tasks. We introduce Transformer-Squared, a novel self-adaptation framework that adapts LLMs for unseen tasks in real-ti... | {
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2501.06253 | The State of Post-Hoc Local XAI Techniques for Image Processing:
Challenges and Motivations | [
"cs.CV",
"cs.AI"
] | As complex AI systems further prove to be an integral part of our lives, a persistent and critical problem is the underlying black-box nature of such products and systems. In pursuit of productivity enhancements, one must not forget the need for various technology to boost the overall trustworthiness of such AI systems... | {
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2501.06254 | Rethinking Evaluation of Sparse Autoencoders through the Representation
of Polysemous Words | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Sparse autoencoders (SAEs) have gained a lot of attention as a promising tool to improve the interpretability of large language models (LLMs) by mapping the complex superposition of polysemantic neurons into monosemantic features and composing a sparse dictionary of words. However, traditional performance metrics like ... | {
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2501.06255 | Progressive Supervision via Label Decomposition: An Long-Term and
Large-Scale Wireless Traffic Forecasting Method | [
"cs.LG",
"cs.AI"
] | Long-term and Large-scale Wireless Traffic Forecasting (LL-WTF) is pivotal for strategic network management and comprehensive planning on a macro scale. However, LL-WTF poses greater challenges than short-term ones due to the pronounced non-stationarity of extended wireless traffic and the vast number of nodes distribu... | {
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2501.06256 | What Matters for In-Context Learning: A Balancing Act of Look-up and
In-Weight Learning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated impressive performance in various tasks, including In-Context Learning (ICL), where the model performs new tasks by conditioning solely on the examples provided in the context, without updating the model's weights. While prior research has explored the roles of pretraining... | {
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2501.06258 | Contextual Bandit Optimization with Pre-Trained Neural Networks | [
"cs.LG"
] | Bandit optimization is a difficult problem, especially if the reward model is high-dimensional. When rewards are modeled by neural networks, sublinear regret has only been shown under strong assumptions, usually when the network is extremely wide. In this thesis, we investigate how pre-training can help us in the regim... | {
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2501.06259 | Quantum Down Sampling Filter for Variational Auto-encoder | [
"cs.CV"
] | Variational Autoencoders (VAEs) are essential tools in generative modeling and image reconstruction, with their performance heavily influenced by the encoder-decoder architecture. This study aims to improve the quality of reconstructed images by enhancing their resolution and preserving finer details, particularly when... | {
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2501.06261 | CAMs as Shapley Value-based Explainers | [
"cs.CV",
"cs.GT"
] | Class Activation Mapping (CAM) methods are widely used to visualize neural network decisions, yet their underlying mechanisms remain incompletely understood. To enhance the understanding of CAM methods and improve their explainability, we introduce the Content Reserved Game-theoretic (CRG) Explainer. This theoretical f... | {
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2501.06262 | Towards smart and adaptive agents for active sensing on edge devices | [
"cs.RO",
"cs.AI",
"eess.IV"
] | TinyML has made deploying deep learning models on low-power edge devices feasible, creating new opportunities for real-time perception in constrained environments. However, the adaptability of such deep learning methods remains limited to data drift adaptation, lacking broader capabilities that account for the environm... | {
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2501.06263 | GelBelt: A Vision-based Tactile Sensor for Continuous Sensing of Large
Surfaces | [
"cs.CV",
"cs.RO"
] | Scanning large-scale surfaces is widely demanded in surface reconstruction applications and detecting defects in industries' quality control and maintenance stages. Traditional vision-based tactile sensors have shown promising performance in high-resolution shape reconstruction while suffering limitations such as small... | {
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2501.06265 | AgoraSpeech: A multi-annotated comprehensive dataset of political
discourse through the lens of humans and AI | [
"cs.CL"
] | Political discourse datasets are important for gaining political insights, analyzing communication strategies or social science phenomena. Although numerous political discourse corpora exist, comprehensive, high-quality, annotated datasets are scarce. This is largely due to the substantial manual effort, multidisciplin... | {
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2501.06268 | Cluster Catch Digraphs with the Nearest Neighbor Distance | [
"cs.LG",
"stat.ME",
"stat.ML"
] | We introduce a new method for clustering based on Cluster Catch Digraphs (CCDs). The new method addresses the limitations of RK-CCDs by employing a new variant of spatial randomness test that employs the nearest neighbor distance (NND) instead of the Ripley's K function used by RK-CCDs. We conduct a comprehensive Monte... | {
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2501.06269 | OpenAI ChatGPT interprets Radiological Images: GPT-4 as a Medical Doctor
for a Fast Check-Up | [
"cs.CV"
] | OpenAI released version GPT-4 on March 14, 2023, following the success of ChatGPT, which was announced in November 2022. In addition to the existing GPT-3 features, GPT-4 can interpret images. To achieve this, the processing power and model have been significantly improved. The ability to process and interpret images g... | {
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2501.06271 | Large Language Models for Bioinformatics | [
"q-bio.QM",
"cs.AI",
"cs.CE"
] | With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by pro... | {
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2501.06273 | Underwater Image Enhancement using Generative Adversarial Networks: A
Survey | [
"eess.IV",
"cs.CV"
] | In recent years, there has been a surge of research focused on underwater image enhancement using Generative Adversarial Networks (GANs), driven by the need to overcome the challenges posed by underwater environments. Issues such as light attenuation, scattering, and color distortion severely degrade the quality of und... | {
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2501.06274 | Polarized Patterns of Language Toxicity and Sentiment of Debunking Posts
on Social Media | [
"cs.CY",
"cs.AI",
"cs.CL"
] | The rise of misinformation and fake news in online political discourse poses significant challenges to democratic processes and public engagement. While debunking efforts aim to counteract misinformation and foster fact-based dialogue, these discussions often involve language toxicity and emotional polarization. We exa... | {
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2501.06276 | PROEMO: Prompt-Driven Text-to-Speech Synthesis Based on Emotion and
Intensity Control | [
"cs.SD",
"cs.CL",
"eess.AS"
] | Speech synthesis has significantly advanced from statistical methods to deep neural network architectures, leading to various text-to-speech (TTS) models that closely mimic human speech patterns. However, capturing nuances such as emotion and style in speech synthesis is challenging. To address this challenge, we intro... | {
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2501.06277 | Environmental large language model Evaluation (ELLE) dataset: A
Benchmark for Evaluating Generative AI applications in Eco-environment Domain | [
"cs.CL",
"cs.IR"
] | Generative AI holds significant potential for ecological and environmental applications such as monitoring, data analysis, education, and policy support. However, its effectiveness is limited by the lack of a unified evaluation framework. To address this, we present the Environmental Large Language model Evaluation (EL... | {
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2501.06278 | Aligning Brain Activity with Advanced Transformer Models: Exploring the
Role of Punctuation in Semantic Processing | [
"cs.CL",
"cs.LG"
] | This research examines the congruence between neural activity and advanced transformer models, emphasizing the semantic significance of punctuation in text understanding. Utilizing an innovative approach originally proposed by Toneva and Wehbe, we evaluate four advanced transformer models RoBERTa, DistiliBERT, ALBERT, ... | {
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2501.06280 | Visualizing Uncertainty in Image Guided Surgery a Review | [
"cs.CV",
"cs.GR"
] | During tumor resection surgery, surgeons rely on neuronavigation to locate tumors and other critical structures in the brain. Most neuronavigation is based on preoperative images, such as MRI and ultrasound, to navigate through the brain. Neuronavigation acts like GPS for the brain, guiding neurosurgeons during the pro... | {
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2501.06282 | MinMo: A Multimodal Large Language Model for Seamless Voice Interaction | [
"cs.CL",
"cs.AI",
"cs.HC",
"cs.SD",
"eess.AS"
] | Recent advancements in large language models (LLMs) and multimodal speech-text models have laid the groundwork for seamless voice interactions, enabling real-time, natural, and human-like conversations. Previous models for voice interactions are categorized as native and aligned. Native models integrate speech and text... | {
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2501.06283 | Dafny as Verification-Aware Intermediate Language for Code Generation | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.LO",
"cs.PL"
] | Using large language models (LLMs) to generate source code from natural language prompts is a popular and promising idea with a wide range of applications. One of its limitations is that the generated code can be faulty at times, often in a subtle way, despite being presented to the user as correct. In this paper, we e... | {
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2501.06286 | Bactrainus: Optimizing Large Language Models for Multi-hop Complex
Question Answering Tasks | [
"cs.CL",
"cs.AI"
] | In recent years, the use of large language models (LLMs) has significantly increased, and these models have demonstrated remarkable performance in a variety of general language tasks. However, the evaluation of their performance in domain-specific tasks, particularly those requiring deep natural language understanding,... | {
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2501.06293 | LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent
Neural Networks in the Korea Microlensing Telescope Network | [
"astro-ph.IM",
"astro-ph.EP",
"astro-ph.GA",
"cs.AI"
] | Traditional microlensing event vetting methods require highly trained human experts, and the process is both complex and time-consuming. This reliance on manual inspection often leads to inefficiencies and constrains the ability to scale for widespread exoplanet detection, ultimately hindering discovery rates. To addre... | {
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2501.06300 | Tensorization of neural networks for improved privacy and
interpretability | [
"math.NA",
"cs.LG",
"cs.NA",
"physics.comp-ph",
"quant-ph"
] | We present a tensorization algorithm for constructing tensor train representations of functions, drawing on sketching and cross interpolation ideas. The method only requires black-box access to the target function and a small set of sample points defining the domain of interest. Thus, it is particularly well-suited for... | {
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2501.06308 | Uncertainty Estimation for Path Loss and Radio Metric Models | [
"cs.LG",
"stat.ML"
] | This research leverages Conformal Prediction (CP) in the form of Conformal Predictive Systems (CPS) to accurately estimate uncertainty in a suite of machine learning (ML)-based radio metric models [1] as well as in a 2-D map-based ML path loss model [2]. Utilizing diverse difficulty estimators, we construct 95% confide... | {
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2501.06312 | Towards Iris Presentation Attack Detection with Foundation Models | [
"cs.CV"
] | Foundation models are becoming increasingly popular due to their strong generalization capabilities resulting from being trained on huge datasets. These generalization capabilities are attractive in areas such as NIR Iris Presentation Attack Detection (PAD), in which databases are limited in the number of subjects and ... | {
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2501.06314 | BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent
Systems | [
"cs.AI",
"cs.MA"
] | Creating end-to-end bioinformatics workflows requires diverse domain expertise, which poses challenges for both junior and senior researchers as it demands a deep understanding of both genomics concepts and computational techniques. While large language models (LLMs) provide some assistance, they often fall short in pr... | {
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2501.06316 | Trends in urban flows: A transfer entropy approach | [
"cs.IT",
"math.IT",
"stat.ME"
] | The accurate estimation of human activity in cities is one of the first steps towards understanding the structure of the urban environment. Human activities are highly granular and dynamic in spatial and temporal dimensions. Estimating confidence is crucial for decision-making in numerous applications such as urban man... | {
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2501.06317 | Understanding How Paper Writers Use AI-Generated Captions in Figure
Caption Writing | [
"cs.HC",
"cs.AI",
"cs.CL"
] | Figures and their captions play a key role in scientific publications. However, despite their importance, many captions in published papers are poorly crafted, largely due to a lack of attention by paper authors. While prior AI research has explored caption generation, it has mainly focused on reader-centered use cases... | {
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2501.06320 | TTS-Transducer: End-to-End Speech Synthesis with Neural Transducer | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.SD"
] | This work introduces TTS-Transducer - a novel architecture for text-to-speech, leveraging the strengths of audio codec models and neural transducers. Transducers, renowned for their superior quality and robustness in speech recognition, are employed to learn monotonic alignments and allow for avoiding using explicit du... | {
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2501.06322 | Multi-Agent Collaboration Mechanisms: A Survey of LLMs | [
"cs.AI"
] | With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively. These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex t... | {
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2501.06326 | On Creating A Brain-To-Text Decoder | [
"cs.LG",
"eess.IV",
"eess.SP"
] | Brain decoding has emerged as a rapidly advancing and extensively utilized technique within neuroscience. This paper centers on the application of raw electroencephalogram (EEG) signals for decoding human brain activity, offering a more expedited and efficient methodology for enhancing our understanding of the human br... | {
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2501.06332 | Aggregating Low Rank Adapters in Federated Fine-tuning | [
"cs.LG",
"cs.AI"
] | Fine-tuning large language models requires high computational and memory resources, and is therefore associated with significant costs. When training on federated datasets, an increased communication effort is also needed. For this reason, parameter-efficient methods (PEFT) are becoming increasingly important. In this ... | {
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2501.06335 | A Comparison of Strategies to Embed Physics-Informed Neural Networks in
Nonlinear Model Predictive Control Formulations Solved via Direct
Transcription | [
"eess.SY",
"cs.SY",
"math.OC"
] | This study aims to benchmark candidate strategies for embedding neural network (NN) surrogates in nonlinear model predictive control (NMPC) formulations that are subject to systems described with partial differential equations and that are solved via direct transcription (i.e., simultaneous methods). This study focuses... | {
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2501.06336 | MEt3R: Measuring Multi-View Consistency in Generated Images | [
"cs.CV",
"cs.LG",
"eess.IV"
] | We introduce MEt3R, a metric for multi-view consistency in generated images. Large-scale generative models for multi-view image generation are rapidly advancing the field of 3D inference from sparse observations. However, due to the nature of generative modeling, traditional reconstruction metrics are not suitable to m... | {
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2501.06339 | On The Statistical Complexity of Offline Decision-Making | [
"cs.LG",
"cs.AI",
"stat.ML"
] | We study the statistical complexity of offline decision-making with function approximation, establishing (near) minimax-optimal rates for stochastic contextual bandits and Markov decision processes. The performance limits are captured by the pseudo-dimension of the (value) function class and a new characterization of t... | {
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2501.06346 | Large Language Models Share Representations of Latent Grammatical
Concepts Across Typologically Diverse Languages | [
"cs.CL"
] | Human bilinguals often use similar brain regions to process multiple languages, depending on when they learned their second language and their proficiency. In large language models (LLMs), how are multiple languages learned and encoded? In this work, we explore the extent to which LLMs share representations of morphosy... | {
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2501.06348 | Why Automate This? Exploring the Connection between Time Use, Well-being
and Robot Automation Across Social Groups | [
"cs.HC",
"cs.RO"
] | Understanding the motivations underlying the human inclination to automate tasks is vital to developing truly helpful robots integrated into daily life. Accordingly, we ask: are individuals more inclined to automate chores based on the time they consume or the feelings experienced while performing them? This study expl... | {
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2501.06353 | Event Constrained Programming | [
"math.OC",
"cs.SY",
"eess.SY"
] | In this paper, we present event constraints as a new modeling paradigm that generalizes joint chance constraints from stochastic optimization to (1) enforce a constraint on the probability of satisfying a set of constraints aggregated via application-specific logic (constituting an event) and (2) to be applied to gener... | {
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2501.06355 | Low-Complexity Detection of Multiple Preambles in the Presence of
Mobility and Delay Spread | [
"eess.SP",
"cs.IT",
"math.IT"
] | Current wireless infrastructure is optimized to support downlink applications. This paper anticipates the emergence of applications where engineering focus shifts from downlink to uplink. The current paradigm of scheduling users on reserved uplink resources is not able to deal efficiently with unpredictable traffic pat... | {
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2501.06356 | Ultrasound Image Synthesis Using Generative AI for Lung Ultrasound
Detection | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Developing reliable healthcare AI models requires training with representative and diverse data. In imbalanced datasets, model performance tends to plateau on the more prevalent classes while remaining low on less common cases. To overcome this limitation, we propose DiffUltra, the first generative AI technique capable... | {
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2501.06357 | Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by
Layer Importance and Quantization Sensitivity | [
"cs.CV"
] | In this paper, we propose Mix-QViT, an explainability-driven MPQ framework that systematically allocates bit-widths to each layer based on two criteria: layer importance, assessed via Layer-wise Relevance Propagation (LRP), which identifies how much each layer contributes to the final classification, and quantization s... | {
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2501.06362 | Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next
Basket Recommendation | [
"cs.IR"
] | In next basket recommendation (NBR) a set of items is recommended to users based on their historical basket sequences. In many domains, the recommended baskets consist of both repeat items and explore items. Some state-of-the-art NBR methods are heavily biased to recommend repeat items so as to maximize utility. The ev... | {
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2501.06363 | On the Rate-Distortion-Perception Function for Gaussian Processes | [
"cs.IT",
"math.IT"
] | In this paper, we investigate the rate-distortion-perception function (RDPF) of a source modeled by a Gaussian Process (GP) on a measure space $\Omega$ under mean squared error (MSE) distortion and squared Wasserstein-2 perception metrics. First, we show that the optimal reconstruction process is itself a GP, character... | {
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2501.06365 | Gender-Neutral Large Language Models for Medical Applications: Reducing
Bias in PubMed Abstracts | [
"cs.CL",
"cs.AI",
"cs.IR"
] | This paper presents a pipeline for mitigating gender bias in large language models (LLMs) used in medical literature by neutralizing gendered occupational pronouns. A dataset of 379,000 PubMed abstracts from 1965-1980 was processed to identify and modify pronouns tied to professions. We developed a BERT-based model, ``... | {
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2501.06366 | Counterfactually Fair Reinforcement Learning via Sequential Data
Preprocessing | [
"stat.ML",
"cs.CY",
"cs.LG",
"stat.ME"
] | When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy may disproportionately allocate efficacious actions to one subpopulation, creating or exacerbating disparities in other socioeconomically-dis... | {
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} |
2501.06368 | Towards Robust Nonlinear Subspace Clustering: A Kernel Learning Approach | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Kernel-based subspace clustering, which addresses the nonlinear structures in data, is an evolving area of research. Despite noteworthy progressions, prevailing methodologies predominantly grapple with limitations relating to (i) the influence of predefined kernels on model performance; (ii) the difficulty of preservin... | {
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2501.06370 | Towards a Probabilistic Framework for Analyzing and Improving
LLM-Enabled Software | [
"cs.SE",
"cs.AI"
] | Ensuring the reliability and verifiability of large language model (LLM)-enabled systems remains a significant challenge in software engineering. We propose a probabilistic framework for systematically analyzing and improving these systems by modeling and refining distributions over clusters of semantically equivalent ... | {
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2501.06374 | AFRIDOC-MT: Document-level MT Corpus for African Languages | [
"cs.CL"
] | This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yor\`ub\'a, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We condu... | {
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2501.06376 | On the Partial Identifiability in Reward Learning: Choosing the Best
Reward | [
"cs.LG",
"stat.ML"
] | In Reward Learning (ReL), we are given feedback on an unknown *target reward*, and the goal is to use this information to find it. When the feedback is not informative enough, the target reward is only *partially identifiable*, i.e., there exists a set of rewards (the feasible set) that are equally-compatible with the ... | {
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2501.06382 | Dynamics of "Spontaneous" Topic Changes in Next Token Prediction with
Self-Attention | [
"cs.CL",
"cs.AI",
"stat.ML"
] | Human cognition can spontaneously shift conversation topics, often triggered by emotional or contextual signals. In contrast, self-attention-based language models depend on structured statistical cues from input tokens for next-token prediction, lacking this spontaneity. Motivated by this distinction, we investigate th... | {
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2501.06386 | Using Pre-trained LLMs for Multivariate Time Series Forecasting | [
"cs.LG",
"cs.CL"
] | Pre-trained Large Language Models (LLMs) encapsulate large amounts of knowledge and take enormous amounts of compute to train. We make use of this resource, together with the observation that LLMs are able to transfer knowledge and performance from one domain or even modality to another seemingly-unrelated area, to hel... | {
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2501.06389 | Kolmogorov-Arnold networks for metal surface defect classification | [
"cs.LG",
"cs.AI",
"cs.NE"
] | This paper presents the application of Kolmogorov-Arnold Networks (KAN) in classifying metal surface defects. Specifically, steel surfaces are analyzed to detect defects such as cracks, inclusions, patches, pitted surfaces, and scratches. Drawing on the Kolmogorov-Arnold theorem, KAN provides a novel approach compared ... | {
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2501.06394 | Unispeaker: A Unified Approach for Multimodality-driven Speaker
Generation | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Recent advancements in personalized speech generation have brought synthetic speech increasingly close to the realism of target speakers' recordings, yet multimodal speaker generation remains on the rise. This paper introduces UniSpeaker, a unified approach for multimodality-driven speaker generation. Specifically, we ... | {
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2501.06399 | Has an AI model been trained on your images? | [
"cs.CV",
"cs.AI",
"cs.CR",
"cs.CY",
"cs.LG"
] | From a simple text prompt, generative-AI image models can create stunningly realistic and creative images bounded, it seems, by only our imagination. These models have achieved this remarkable feat thanks, in part, to the ingestion of billions of images collected from nearly every corner of the internet. Many creators ... | {
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2501.06400 | Mathematics of Digital Twins and Transfer Learning for PDE Models | [
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] | We define a digital twin (DT) of a physical system governed by partial differential equations (PDEs) as a model for real-time simulations and control of the system behavior under changing conditions. We construct DTs using the Karhunen-Lo\`{e}ve Neural Network (KL-NN) surrogate model and transfer learning (TL). The sur... | {
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2501.06404 | A Hybrid Framework for Reinsurance Optimization: Integrating Generative
Models and Reinforcement Learning | [
"econ.EM",
"cs.AI",
"cs.LG",
"stat.ML"
] | Reinsurance optimization is critical for insurers to manage risk exposure, ensure financial stability, and maintain solvency. Traditional approaches often struggle with dynamic claim distributions, high-dimensional constraints, and evolving market conditions. This paper introduces a novel hybrid framework that integrat... | {
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2501.06405 | FocusDD: Real-World Scene Infusion for Robust Dataset Distillation | [
"cs.CV",
"cs.AI"
] | Dataset distillation has emerged as a strategy to compress real-world datasets for efficient training. However, it struggles with large-scale and high-resolution datasets, limiting its practicality. This paper introduces a novel resolution-independent dataset distillation method Focus ed Dataset Distillation (FocusDD),... | {
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2501.06408 | Computational and Statistical Asymptotic Analysis of the JKO Scheme for
Iterative Algorithms to update distributions | [
"stat.ML",
"cs.LG"
] | The seminal paper of Jordan, Kinderlehrer, and Otto introduced what is now widely known as the JKO scheme, an iterative algorithmic framework for computing distributions. This scheme can be interpreted as a Wasserstein gradient flow and has been successfully applied in machine learning contexts, such as deriving policy... | {
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2501.06410 | Task Delay and Energy Consumption Minimization for Low-altitude MEC via
Evolutionary Multi-objective Deep Reinforcement Learning | [
"cs.LG",
"cs.NE",
"cs.NI"
] | The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments... | {
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2501.06414 | IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss
Radio Map Prediction | [
"eess.SP",
"cs.LG"
] | In this paper, we propose a generalizable deep neural network model for indoor pathloss radio map prediction (termed as IPP-Net). IPP-Net is based on a UNet architecture and learned from both large-scale ray tracing simulation data and a modified 3GPP indoor hotspot model. The performance of IPP-Net is evaluated in the... | {
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2501.06416 | Influencing Humans to Conform to Preference Models for RLHF | [
"cs.LG",
"cs.AI",
"cs.HC"
] | Designing a reinforcement learning from human feedback (RLHF) algorithm to approximate a human's unobservable reward function requires assuming, implicitly or explicitly, a model of human preferences. A preference model that poorly describes how humans generate preferences risks learning a poor approximation of the hum... | {
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2501.06417 | DiscQuant: A Quantization Method for Neural Networks Inspired by
Discrepancy Theory | [
"cs.LG",
"cs.AI",
"cs.DS"
] | Quantizing the weights of a neural network has two steps: (1) Finding a good low bit-complexity representation for weights (which we call the quantization grid) and (2) Rounding the original weights to values in the quantization grid. In this paper, we study the problem of rounding optimally given any quantization grid... | {
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2501.06423 | AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written
Programs | [
"cs.AI"
] | Program synthesis has traditionally relied on human-provided specifications, examples, or prior knowledge to generate functional algorithms. Existing methods either emulate human-written algorithms or solve specific tasks without generating reusable programmatic logic, limiting their ability to create novel algorithms.... | {
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2501.06425 | Tensor Product Attention Is All You Need | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory overhead during inference. In this paper, we propose Tensor Product Attention (TPA), a novel attention mechanism that uses tensor decompositions to represent queries, keys, and va... | {
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2501.06429 | Reliable Imputed-Sample Assisted Vertical Federated Learning | [
"cs.LG",
"stat.ML"
] | Vertical Federated Learning (VFL) is a well-known FL variant that enables multiple parties to collaboratively train a model without sharing their raw data. Existing VFL approaches focus on overlapping samples among different parties, while their performance is constrained by the limited number of these samples, leaving... | {
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2501.06430 | Open Eyes, Then Reason: Fine-grained Visual Mathematical Understanding
in MLLMs | [
"cs.CV"
] | Current multimodal large language models (MLLMs) often underperform on mathematical problem-solving tasks that require fine-grained visual understanding. The limitation is largely attributable to inadequate perception of geometric primitives during image-level contrastive pre-training (e.g., CLIP). While recent efforts... | {
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2501.06431 | Aug3D: Augmenting large scale outdoor datasets for Generalizable Novel
View Synthesis | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Recent photorealistic Novel View Synthesis (NVS) advances have increasingly gained attention. However, these approaches remain constrained to small indoor scenes. While optimization-based NVS models have attempted to address this, generalizable feed-forward methods, offering significant advantages, remain underexplored... | {
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2501.06432 | Deep Learning on Hester Davis Scores for Inpatient Fall Prediction | [
"cs.LG",
"cs.AI"
] | Fall risk prediction among hospitalized patients is a critical aspect of patient safety in clinical settings, and accurate models can help prevent adverse events. The Hester Davis Score (HDS) is commonly used to assess fall risk, with current clinical practice relying on a threshold-based approach. In this method, a pa... | {
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2501.06434 | Synthetic Feature Augmentation Improves Generalization Performance of
Language Models | [
"cs.CL",
"cs.AI"
] | Training and fine-tuning deep learning models, especially large language models (LLMs), on limited and imbalanced datasets poses substantial challenges. These issues often result in poor generalization, where models overfit to dominant classes and underperform on minority classes, leading to biased predictions and redu... | {
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2501.06438 | Qffusion: Controllable Portrait Video Editing via Quadrant-Grid
Attention Learning | [
"cs.CV"
] | This paper presents Qffusion, a dual-frame-guided framework for portrait video editing. Specifically, we consider a design principle of ``animation for editing'', and train Qffusion as a general animation framework from two still reference images while we can use it for portrait video editing easily by applying modifie... | {
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2501.06440 | UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image
Segmentation | [
"cs.CV",
"eess.IV"
] | Recent advancements in meteorology involve the use of ground-based sky cameras for cloud observation. Analyzing images from these cameras helps in calculating cloud coverage and understanding atmospheric phenomena. Traditionally, cloud image segmentation relied on conventional computer vision techniques. However, with ... | {
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2501.06441 | CPDR: Towards Highly-Efficient Salient Object Detection via Crossed
Post-decoder Refinement | [
"cs.CV"
] | Most of the current salient object detection approaches use deeper networks with large backbones to produce more accurate predictions, which results in a significant increase in computational complexity. A great number of network designs follow the pure UNet and Feature Pyramid Network (FPN) architecture which has limi... | {
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2501.06442 | ARES: Auxiliary Range Expansion for Outlier Synthesis | [
"cs.AI"
] | Recent successes of artificial intelligence and deep learning often depend on the well-collected training dataset which is assumed to have an identical distribution with the test dataset. However, this assumption, which is called closed-set learning, is hard to meet in realistic scenarios for deploying deep learning mo... | {
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2501.06444 | On the Computational Capability of Graph Neural Networks: A Circuit
Complexity Bound Perspective | [
"cs.LG",
"cs.AI",
"cs.CC"
] | Graph Neural Networks (GNNs) have become the standard approach for learning and reasoning over relational data, leveraging the message-passing mechanism that iteratively propagates node embeddings through graph structures. While GNNs have achieved significant empirical success, their theoretical limitations remain an a... | {
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2501.06446 | Cross-Technology Interference: Detection, Avoidance, and Coexistence
Mechanisms in the ISM Bands | [
"cs.NI",
"cs.LG"
] | A large number of heterogeneous wireless networks share the unlicensed spectrum designated as the ISM (Industry, Scientific, and Medicine) radio band. These networks do not adhere to a common medium access rule and differ in their specifications considerably. As a result, when concurrently active, they cause cross-tech... | {
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2501.06448 | Discovering an Image-Adaptive Coordinate System for Photography
Processing | [
"cs.CV"
] | Curve & Lookup Table (LUT) based methods directly map a pixel to the target output, making them highly efficient tools for real-time photography processing. However, due to extreme memory complexity to learn full RGB space mapping, existing methods either sample a discretized 3D lattice to build a 3D LUT or decompose i... | {
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2501.06454 | Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC
Systems with UAV Swarms | [
"eess.SP",
"cs.LG"
] | This paper introduces a novel Multi-Agent Reinforcement Learning (MARL) framework to enhance integrated sensing and communication (ISAC) networks using unmanned aerial vehicle (UAV) swarms as sensing radars. By framing the positioning and trajectory optimization of UAVs as a Partially Observable Markov Decision Process... | {
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} |
2501.06457 | Automated Detection and Analysis of Minor Deformations in Flat Walls Due
to Railway Vibrations Using LiDAR and Machine Learning | [
"cs.LG"
] | This study introduces an advanced methodology for automatically identifying minor deformations in flat walls caused by vibrations from nearby railway tracks. It leverages high-density Terrestrial Laser Scanner (TLS) LiDAR surveys and AI/ML techniques to collect and analyze data. The scan data is processed into a detail... | {
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} |
2501.06458 | O1 Replication Journey -- Part 3: Inference-time Scaling for Medical
Reasoning | [
"cs.CL"
] | Building upon our previous investigations of O1 replication (Part 1: Journey Learning [Qin et al., 2024] and Part 2: Distillation [Huang et al., 2024]), this work explores the potential of inference-time scaling in large language models (LLMs) for medical reasoning tasks, ranging from diagnostic decision-making to trea... | {
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} |
2501.06461 | Assessing instructor-AI cooperation for grading essay-type questions in
an introductory sociology course | [
"cs.AI"
] | This study explores the use of artificial intelligence (AI) as a complementary tool for grading essay-type questions in higher education, focusing on its consistency with human grading and potential to reduce biases. Using 70 handwritten exams from an introductory sociology course, we evaluated generative pre-trained t... | {
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} |
2501.06465 | MedCT: A Clinical Terminology Graph for Generative AI Applications in
Healthcare | [
"cs.CL",
"cs.AI"
] | We introduce the world's first clinical terminology for the Chinese healthcare community, namely MedCT, accompanied by a clinical foundation model MedBERT and an entity linking model MedLink. The MedCT system enables standardized and programmable representation of Chinese clinical data, successively stimulating the dev... | {
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} |
2501.06466 | CNN-powered micro- to macro-scale flow modeling in deformable porous
media | [
"physics.flu-dyn",
"cs.CV",
"cs.LG"
] | This work introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of micro-CT images of real microgeometries. The primary goal is to develop an efficient, machine-learning (ML)-based method that overcomes the limitations of traditional ... | {
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} |
2501.06467 | Retrieval-Augmented Dialogue Knowledge Aggregation for Expressive
Conversational Speech Synthesis | [
"cs.CL"
] | Conversational speech synthesis (CSS) aims to take the current dialogue (CD) history as a reference to synthesize expressive speech that aligns with the conversational style. Unlike CD, stored dialogue (SD) contains preserved dialogue fragments from earlier stages of user-agent interaction, which include style expressi... | {
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} |
2501.06468 | First Token Probability Guided RAG for Telecom Question Answering | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have garnered significant attention for their impressive general-purpose capabilities. For applications requiring intricate domain knowledge, Retrieval-Augmented Generation (RAG) has shown a distinct advantage in incorporating domain-specific information into LLMs. However, existing RAG res... | {
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} |
2501.06469 | SP-SLAM: Neural Real-Time Dense SLAM With Scene Priors | [
"cs.CV"
] | Neural implicit representations have recently shown promising progress in dense Simultaneous Localization And Mapping (SLAM). However, existing works have shortcomings in terms of reconstruction quality and real-time performance, mainly due to inflexible scene representation strategy without leveraging any prior inform... | {
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} |
2501.06471 | The Internet of Large Language Models: An Orchestration Framework for
LLM Training and Knowledge Exchange Toward Artificial General Intelligence | [
"cs.AI"
] | This paper explores the multi-dimensional challenges faced during the development of Large Language Models (LLMs), including the massive scale of model parameters and file sizes, the complexity of development environment configuration, the singularity of model functionality, and the high costs of computational resource... | {
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} |
2501.06472 | YO-CSA-T: A Real-time Badminton Tracking System Utilizing YOLO Based on
Contextual and Spatial Attention | [
"cs.CV",
"cs.AI"
] | The 3D trajectory of a shuttlecock required for a badminton rally robot for human-robot competition demands real-time performance with high accuracy. However, the fast flight speed of the shuttlecock, along with various visual effects, and its tendency to blend with environmental elements, such as court lines and light... | {
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} |
2501.06475 | Enhancing Multi-Modal Video Sentiment Classification Through
Semi-Supervised Clustering | [
"cs.CV",
"cs.LG"
] | Understanding emotions in videos is a challenging task. However, videos contain several modalities which make them a rich source of data for machine learning and deep learning tasks. In this work, we aim to improve video sentiment classification by focusing on two key aspects: the video itself, the accompanying text, a... | {
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} |
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