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272689735 | 2409.10146 | 2024-09-16 | LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge | This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with the 23rd International Semantic Web Conference (ISWC) to explore the potential of Large Language Models (LLMs) in Ontology Learning (OL), a ... | [
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272689734 | 2409.10283 | 2024-09-16 | ASMA: An Adaptive Safety Margin Algorithm for Vision-Language Drone Navigation via Scene-Aware Control Barrier Functions | In the rapidly evolving field of vision-language navigation (VLN), ensuring safety for physical agents remains an open challenge. For a human-in-the-loop language-operated drone to navigate safely, it must understand natural language commands, perceive the environment, and simultaneously avoid hazards in real time. Con... | [
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272694062 | 2409.11242 | 2024-09-17 | Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse | LLMs are an integral component of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the overall quality of end-to-end RAG systems, there is a gap in understanding the appropriateness of LLMs for the RAG task. To address this, we introduce Trust-Score, a holistic metric that evaluates ... | [
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272693779 | 2409.11355 | 2024-09-17 | Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think | Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task. While the proposed model achieved state-of-the-art results, high computational demands due to multi-step inference limited its use in many ... | [
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273163356 | 2410.02768 | 2024-09-17 | Uncertainty-Guided Self-Questioning and Answering for Video-Language Alignment | The development of multi-modal models has been rapidly advancing, with some demonstrating remarkable capabilities. However, annotating video-text pairs remains expensive and insufficient. Take video question answering (VideoQA) tasks as an example, human annotated questions and answers often cover only part of the vide... | [
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272694047 | 2409.11241 | 2024-09-17 | Spontaneous Informal Speech Dataset for Punctuation Restoration | Presently, punctuation restoration models are evaluated almost solely on well-structured, scripted corpora. On the other hand, real-world ASR systems and post-processing pipelines typically apply towards spontaneous speech with significant irregularities, stutters, and deviations from perfect grammar. To address this d... | [
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272709146 | 2409.11597 | 2024-09-17 | The Sample Complexity of Smooth Boosting and the Tightness of the Hardcore Theorem | Smooth boosters generate distributions that do not place too much weight on any given example. Originally introduced for their noise-tolerant properties, such boosters have also found applications in differential privacy, reproducibility, and quantum learning theory. We study and settle the sample complexity of smooth ... | [
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272705831 | 2409.11452 | 2024-09-17 | Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation | Mobile robots should be capable of planning cost-efficient paths for autonomous navigation. Typically, the terrain and robot properties are subject to variations. For instance, properties of the terrain such as friction may vary across different locations. Also, properties of the robot may change such as payloads or we... | [
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272694045 | 2409.10979 | 2024-09-17 | A Symbol-Pair Decoder for CSS Codes | The relation between stabilizer codes and binary codes provided by Gottesman and Calderbank et al. is a celebrated result, as it allows the lifting of classical codes to quantum codes. An equivalent way to state this result is that the work allows us to lift decoders for classical codes over the Hamming metric to decod... | [
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272694156 | 2409.11008 | 2024-09-17 | Latent mixed-effect models for high-dimensional longitudinal data | Modelling longitudinal data is an important yet challenging task. These datasets can be high-dimensional, contain non-linear effects and time-varying covariates. Gaussian process (GP) prior-based variational autoencoders (VAEs) have emerged as a promising approach due to their ability to model time-series data. However... | [
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272703892 | 2409.11502 | 2024-09-17 | Super Resolution On Global Weather Forecasts | Weather forecasting is a vitally important tool for tasks ranging from planning day to day activities to disaster response planning. However, modeling weather has proven to be challenging task due to its chaotic and unpredictable nature. Each variable, from temperature to precipitation to wind, all influence the path t... | [
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282354670 | 2409.11393 | 2024-09-17 | LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Design of Multi Active/Passive Core-Agent Architectures | In an era where vast amounts of data are collected and processed from diverse sources, there is a growing demand for sophisticated AI systems capable of intelligently fusing and analyzing this information. To address these challenges, researchers have turned towards integrating tools into LLM-powered agents to enhance ... | [
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272693932 | 2409.10803 | 2024-09-17 | Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact | Modeling complex semiconductor fabrication processes such as Ohmic contact formation remains challenging due to high-dimensional parameter spaces and limited experimental data. While classical machine learning (CML) approaches have been successful in many domains, their performance degrades in small-sample, nonlinear s... | [
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272694078 | 2409.11256 | 2024-09-17 | Temporal As a Plugin: Unsupervised Video Denoising with Pre-Trained Image Denoisers | Recent advancements in deep learning have shown impressive results in image and video denoising, leveraging extensive pairs of noisy and noise-free data for supervision. However, the challenge of acquiring paired videos for dynamic scenes hampers the practical deployment of deep video denoising techniques. In contrast,... | [
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272693801 | 2409.10969 | 2024-09-17 | Enhancing Code-switched Text-to-Speech Synthesis Capability in Large Language Models with only Monolingual Corpora | While Large Language Models (LLMs) have shown potential in speech generation and recognition, their applications are mainly confined to monolingual scenarios, with limited explorations in code-switched (CS) contexts. In this paper, we propose a Code-Switched Large Language Model (CS-LLM) to enhance the code-switched te... | [
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272703899 | 2409.11547 | 2024-09-17 | Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs | In this paper, we evaluate the creative fiction writing abilities of a fine-tuned small language model (SLM), BART-large, and compare its performance to human writers and two large language models (LLMs): GPT-3.5 and GPT-4o. Our evaluation consists of two experiments: (i) a human study in which 68 participants rated sh... | [
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272703836 | 2409.11504 | 2024-09-17 | Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph | The ability of message-passing neural networks (MPNNs) to fit complex functions over graphs is limited as most graph convolutions amplify the same signal across all feature channels, a phenomenon known as rank collapse, and over-smoothing as a special case. Most approaches to mitigate over-smoothing extend common messa... | [
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272987673 | 2409.18989 | 2024-09-17 | SC-Phi2: A Fine-tuned Small Language Model for StarCraft II Macromanagement Tasks | This paper introduces SC-Phi2, a fine-tuned StarCraft II small language model for macromanagement tasks. Small language models, like Phi2, Gemma, and DistilBERT, are streamlined versions of large language models (LLMs) with fewer parameters that require less power and memory to run. To teach Microsoft's Phi2 model abou... | [
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272693971 | 2409.11353 | 2024-09-17 | THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models | Hallucination, the generation of factually incorrect content, is a growing challenge in Large Language Models (LLMs). Existing detection and mitigation methods are often isolated and insufficient for domain-specific needs, lacking a standardized pipeline. This paper introduces THaMES (Tool for Hallucination Mitigations... | [
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272694042 | 2409.11018 | 2024-09-17 | Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation | The LiDAR-based 3D object detector that strikes a balance between accuracy and speed is crucial for achieving real-time perception in autonomous driving and robotic navigation systems. To enhance the accuracy of point cloud detection, integrating global context for visual understanding improves the point clouds ability... | [
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272694540 | 2409.11195 | 2024-09-17 | SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholds | This paper introduces a Spiking Diffusion Policy (SDP) learning method for robotic manipulation by integrating Spiking Neurons and Learnable Channel-wise Membrane Thresholds (LCMT) into the diffusion policy model, thereby enhancing computational efficiency and achieving high performance in evaluated tasks. Specifically... | [
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272694291 | 2409.10966 | 2024-09-17 | CUNSB-RFIE: Context-aware Unpaired Neural Schr\"odinger Bridge in Retinal Fundus Image Enhancement | Retinal fundus photography is significant in diagnosing and monitoring retinal diseases. However, systemic imperfections and operator/patient-related factors can hinder the acquisition of high-quality retinal images. Previous efforts in retinal image enhancement primarily relied on GANs, which are limited by the trade-... | [
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272706556 | 2409.11511 | 2024-09-17 | A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network | This paper addresses the problem of ranking Content Providers for Content Recommendation System. Content Providers are the sources of news and other types of content, such as lifestyle, travel, gardening. We propose a framework that leverages explicit user feedback, such as clicks and reactions, and content-based featu... | [
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272694649 | 2409.11147 | 2024-09-17 | Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning | Large language models (LLMs) have exhibited remarkable few-shot learning capabilities and unified the paradigm of NLP tasks through the in-context learning (ICL) technique. Despite the success of ICL, the quality of the exemplar demonstrations can significantly influence the LLM's performance. Existing exemplar selecti... | [
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272705015 | 2409.11509 | 2024-09-17 | FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction | Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. Despite its broad applications in fields such as computer vision, graph learning, and natural language processing, the development of a data ... | [
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272694104 | 2409.11104 | 2024-09-17 | Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB | Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the same 2D projection on the image. In this context, depth data could help to disambiguate the 2D information by providing additional constrain... | [
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272703902 | 2409.11501 | 2024-09-17 | Egalitarian Language Representation in Language Models: It All Begins with Tokenizers | Tokenizers act as a bridge between human language and the latent space of language models, influencing how language is represented in these models. Due to the immense popularity of English-Centric Large Language Models (LLMs), efforts are being made to adapt them for other languages. However, we demonstrate that, from ... | [
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272693841 | 2409.10980 | 2024-09-17 | PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images | Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires spe... | [
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272694163 | 2409.11003 | 2024-09-17 | Single-stage TTS with Masked Audio Token Modeling and Semantic Knowledge Distillation | Audio token modeling has become a powerful framework for speech synthesis, with two-stage approaches employing semantic tokens remaining prevalent. In this paper, we aim to simplify this process by introducing a semantic knowledge distillation method that enables high-quality speech generation in a single stage. Our pr... | [
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278500788 | 2409.10983 | 2024-09-17 | MoDex: Planning High-Dimensional Dexterous Control via Learning Neural Internal Models | Controlling hands in high-dimensional action space has been a longstanding challenge, yet humans naturally perform dexterous tasks with ease. In this paper, we draw inspiration from the concept of internal model exhibited in human behavior and reconsider dexterous hands as learnable systems. Specifically, we introduce ... | [
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272709136 | 2409.11534 | 2024-09-17 | Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening Mammogram | Out-of-distribution (OOD) detection is crucial for enhancing the generalization of AI models used in mammogram screening. Given the challenge of limited prior knowledge about OOD samples in external datasets, unsupervised generative learning is a preferable solution which trains the model to discern the normal characte... | [
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272694438 | 2409.11360 | 2024-09-17 | AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances | Large language models (LLMs) are being increasingly integrated into everyday products and services, such as coding tools and writing assistants. As these embedded AI applications are deployed globally, there is a growing concern that the AI models underlying these applications prioritize Western values. This paper inve... | [
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272694021 | 2409.11263 | 2024-09-17 | Bio-Inspired Mamba: Temporal Locality and Bioplausible Learning in Selective State Space Models | This paper introduces Bio-Inspired Mamba (BIM), a novel online learning framework for selective state space models that integrates biological learning principles with the Mamba architecture. BIM combines Real-Time Recurrent Learning (RTRL) with Spike-Timing-Dependent Plasticity (STDP)-like local learning rules, address... | [
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272693922 | 2409.11123 | 2024-09-17 | Gradient-free Post-hoc Explainability Using Distillation Aided Learnable Approach | The recent advancements in artificial intelligence (AI), with the release of several large models having only query access, make a strong case for explainability of deep models in a post-hoc gradient free manner. In this paper, we propose a framework, named distillation aided explainability (DAX), that attempts to gene... | [
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272988031 | 2409.18988 | 2024-09-17 | A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy | Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involves the creation of a centralized knowledge repository cataloguing historical waste-to-resource transactions, which subsequently enables the g... | [
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272694075 | 2409.10951 | 2024-09-17 | Fair Anomaly Detection For Imbalanced Groups | Anomaly detection (AD) has been widely studied for decades in many real-world applications, including fraud detection in finance, and intrusion detection for cybersecurity, etc. Due to the imbalanced nature between protected and unprotected groups and the imbalanced distributions of normal examples and anomalies, the l... | [
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272694470 | 2409.11192 | 2024-09-17 | Towards Ethical Personal AI Applications: Practical Considerations for AI Assistants with Long-Term Memory | One application area of long-term memory (LTM) capabilities with increasing traction is personal AI companions and assistants. With the ability to retain and contextualize past interactions and adapt to user preferences, personal AI companions and assistants promise a profound shift in how we interact with AI and are o... | [
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272694569 | 2409.10909 | 2024-09-17 | GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval | Query reformulation is a well-known problem in Information Retrieval (IR) aimed at enhancing single search successful completion rate by automatically modifying user's input query. Recent methods leverage Large Language Models (LLMs) to improve query reformulation, but often generate limited and redundant expansions, p... | [
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273163214 | 2410.02775 | 2024-09-17 | A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems | Contrary to conventional massive MIMO cellular configurations plagued by inter-cell interference, cell-free massive MIMO systems distribute network resources across the coverage area, enabling users to connect with multiple access points (APs) and boosting both system capacity and fairness across user. In such systems,... | [
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272694404 | 2409.11110 | 2024-09-17 | Beyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification | Machine learning models have become integral to many fields, but their reliability, defined as producing dependable, trustworthy, and domain-consistent predictions, remains a critical concern. Multiple Instance Learning (MIL) models designed for Whole Slide Image (WSI) classification in computational pathology are rare... | [
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272694191 | 2409.11365 | 2024-09-17 | CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration | The deployment of multimodal large language models (MLLMs) has demonstrated remarkable success in engaging in conversations involving visual inputs, thanks to the superior power of large language models (LLMs). Those MLLMs are typically built based on the LLMs, with an image encoder to process images into the token emb... | [
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273163236 | 2410.02776 | 2024-09-17 | Bypassing the Popularity Bias: Repurposing Models for Better Long-Tail Recommendation | Recommender systems play a crucial role in shaping information we encounter online, whether on social media or when using content platforms, thereby influencing our beliefs, choices, and behaviours. Many recent works address the issue of fairness in recommender systems, typically focusing on topics like ensuring equal ... | [
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272694206 | 2409.10887 | 2024-09-17 | Contrastive Learning in Memristor-based Neuromorphic Systems | Spiking neural networks, the third generation of artificial neural networks, have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks, including their high energy inefficiency and long-criticized biological implausibility. I... | [
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272694588 | 2409.10940 | 2024-09-17 | RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation | Autonomous robot navigation in off-road environments requires a comprehensive understanding of the terrain geometry and traversability. The degraded perceptual conditions and sparse geometric information at longer ranges make the problem challenging especially when driving at high speeds. Furthermore, the sensing-to-ma... | [
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272694258 | 2409.11218 | 2024-09-17 | Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) involves identifying sentiment towards specific aspect terms in a sentence and allows us to uncover nuanced perspectives and attitudes on particular aspects of a product, service, or topic. However, the scarcity of labeled data poses a significant challenge to training high-qualit... | [
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272703978 | 2409.11555 | 2024-09-17 | Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching | Underwater object-level mapping requires incorporating visual foundation models to handle the uncommon and often previously unseen object classes encountered in marine scenarios. In this work, a metric of semantic uncertainty for open-set object detections produced by visual foundation models is calculated and then inc... | [
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272703975 | 2409.11538 | 2024-09-17 | Chain-of-Thought Prompting for Speech Translation | Large language models (LLMs) have demonstrated remarkable advancements in language understanding and generation. Building on the success of text-based LLMs, recent research has adapted these models to use speech embeddings for prompting, resulting in Speech-LLM models that exhibit strong performance in automatic speech... | [
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272694199 | 2409.11138 | 2024-09-17 | Learning Generalized Hamiltonians using fully Symplectic Mappings | Many important physical systems can be described as the evolution of a Hamiltonian system, which has the important property of being conservative, that is, energy is conserved throughout the evolution. Physics Informed Neural Networks and in particular Hamiltonian Neural Networks have emerged as a mechanism to incorpor... | [
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272693933 | 2409.10855 | 2024-09-17 | Calibrated Multivariate Regression with Localized PIT Mappings | Calibration ensures that predicted uncertainties align with observed uncertainties. While there is an extensive literature on recalibration methods for univariate probabilistic forecasts, work on calibration for multivariate forecasts is much more limited. This paper introduces a novel post-hoc recalibration approach t... | [
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272693912 | 2409.11057 | 2024-09-17 | KVPruner: Structural Pruning for Faster and Memory-Efficient Large Language Models | The bottleneck associated with the key-value(KV) cache presents a significant challenge during the inference processes of large language models. While depth pruning accelerates inference, it requires extensive recovery training, which can take up to two weeks. On the other hand, width pruning retains much of the perfor... | [
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