id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.15412 | LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring | [
"cs.LG"
] | Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research. The labor-intensive nature of manual sleep scoring, demanding substantial expertise, has prompted a surge of interest in automated alternatives. Sleep studies in mice play a significant rol... | {
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2412.15415 | Transcribing and Translating, Fast and Slow: Joint Speech Translation
and Recognition | [
"eess.AS",
"cs.CL"
] | We propose the joint speech translation and recognition (JSTAR) model that leverages the fast-slow cascaded encoder architecture for simultaneous end-to-end automatic speech recognition (ASR) and speech translation (ST). The model is transducer-based and uses a multi-objective training strategy that optimizes both ASR ... | {
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2412.15425 | Moment-optimal finitary isomorphism for i.i.d. processes of equal
entropy | [
"math.DS",
"cs.IT",
"math.IT",
"math.PR"
] | The finitary isomorphism theorem, due to Keane and Smorodinsky, raised the natural question of how "finite" the isomorphism can be, in terms of moments of the coding radius. More precisely, for which values does there exist an isomorphism between any two i.i.d. processes of equal entropy, with coding radii exhibiting f... | {
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2412.15426 | Dimension Reduction with Locally Adjusted Graphs | [
"cs.LG"
] | Dimension reduction (DR) algorithms have proven to be extremely useful for gaining insight into large-scale high-dimensional datasets, particularly finding clusters in transcriptomic data. The initial phase of these DR methods often involves converting the original high-dimensional data into a graph. In this graph, eac... | {
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2412.15427 | AdaCred: Adaptive Causal Decision Transformers with Feature Crediting | [
"cs.LG",
"cs.RO"
] | Reinforcement learning (RL) can be formulated as a sequence modeling problem, where models predict future actions based on historical state-action-reward sequences. Current approaches typically require long trajectory sequences to model the environment in offline RL settings. However, these models tend to over-rely on ... | {
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2412.15429 | Offline Safe Reinforcement Learning Using Trajectory Classification | [
"cs.LG",
"cs.AI"
] | Offline safe reinforcement learning (RL) has emerged as a promising approach for learning safe behaviors without engaging in risky online interactions with the environment. Most existing methods in offline safe RL rely on cost constraints at each time step (derived from global cost constraints) and this can result in e... | {
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2412.15430 | An Environment-Adaptive Position/Force Control Based on Physical
Property Estimation | [
"cs.RO"
] | The technology for generating robot actions has significantly contributed to the automation and efficiency of tasks. However, the ability to adapt to objects of different shapes and hardness remains a challenge for general industrial robots. Motion reproduction systems (MRS) replicate previously acquired actions using ... | {
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2412.15431 | Time Will Tell: Timing Side Channels via Output Token Count in Large
Language Models | [
"cs.LG",
"cs.CL",
"cs.CR"
] | This paper demonstrates a new side-channel that enables an adversary to extract sensitive information about inference inputs in large language models (LLMs) based on the number of output tokens in the LLM response. We construct attacks using this side-channel in two common LLM tasks: recovering the target language in m... | {
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2412.15433 | Quantifying detection rates for dangerous capabilities: a theoretical
model of dangerous capability evaluations | [
"cs.AI",
"cs.CY",
"cs.MA",
"econ.GN",
"q-fin.EC",
"stat.AP"
] | We present a quantitative model for tracking dangerous AI capabilities over time. Our goal is to help the policy and research community visualise how dangerous capability testing can give us an early warning about approaching AI risks. We first use the model to provide a novel introduction to dangerous capability testi... | {
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2412.15437 | Safety-Critical Control of Discontinuous Systems with Nonsmooth Safe
Sets | [
"eess.SY",
"cs.SY"
] | This paper studies the design of controllers for discontinuous dynamics that ensure the safety of non-smooth sets. The safe set is represented by arbitrarily nested unions and intersections of 0-superlevel sets of differentiable functions. We show that any optimization-based controller that satisfies only the point-wis... | {
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2412.15438 | Efficient Neural Network Encoding for 3D Color Lookup Tables | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | 3D color lookup tables (LUTs) enable precise color manipulation by mapping input RGB values to specific output RGB values. 3D LUTs are instrumental in various applications, including video editing, in-camera processing, photographic filters, computer graphics, and color processing for displays. While an individual LUT ... | {
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2412.15439 | Uncertainty Estimation for Super-Resolution using ESRGAN | [
"eess.IV",
"cs.CV"
] | Deep Learning-based image super-resolution (SR) has been gaining traction with the aid of Generative Adversarial Networks. Models like SRGAN and ESRGAN are constantly ranked between the best image SR tools. However, they lack principled ways for estimating predictive uncertainty. In the present work, we enhance these m... | {
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2412.15441 | Energy consumption of code small language models serving with runtime
engines and execution providers | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Background. The rapid growth of Language Models (LMs), particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impact. Optimizing LMs inference for energy efficiency is crucial, and Small Language Models (SLMs) offer a promising solution... | {
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2412.15443 | SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic
Retrieval | [
"cs.CL"
] | Retrieval-Augmented Generation (RAG) systems have become pivotal in leveraging vast corpora to generate informed and contextually relevant responses, notably reducing hallucinations in Large Language Models. Despite significant advancements, these systems struggle to efficiently process and retrieve information from la... | {
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2412.15444 | AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based
Assistant to Support Genetic Professionals | [
"cs.HC",
"cs.AI"
] | Generative AI has the potential to transform knowledge work, but further research is needed to understand how knowledge workers envision using and interacting with generative AI. We investigate the development of generative AI tools to support domain experts in knowledge work, examining task delegation and the design o... | {
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2412.15446 | Unified Control Scheme for Optimal Allocation of GFM and GFL Inverters
in Power Networks | [
"eess.SY",
"cs.SY"
] | With the rapid adoption of emerging inverter-based resources, it is crucial to understand their dynamic interactions across the network and ensure stability. This paper proposes a systematic and efficient method to determine the optimal allocation of grid-forming and grid-following inverters in power networks. The appr... | {
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2412.15447 | LiHi-GS: LiDAR-Supervised Gaussian Splatting for Highway Driving Scene
Reconstruction | [
"cs.CV",
"cs.RO"
] | Photorealistic 3D scene reconstruction plays an important role in autonomous driving, enabling the generation of novel data from existing datasets to simulate safety-critical scenarios and expand training data without additional acquisition costs. Gaussian Splatting (GS) facilitates real-time, photorealistic rendering ... | {
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2412.15449 | Effects of Line Dynamics on Stability Margin to Hopf Bifurcation in
Grid-Forming Inverters | [
"eess.SY",
"cs.SY"
] | This paper studies the parameter sensitivity of grid-forming inverters to Hopf bifurcations to address oscillatory instability. An analytical expression for the sensitivity of the stability margin is derived based on the normal vector to the bifurcation hypersurface. We identify the most effective control parameters th... | {
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2412.15450 | Fietje: An open, efficient LLM for Dutch | [
"cs.CL"
] | This paper introduces Fietje, a family of small language models (SLMs) specifically designed for the Dutch language. The model is based on Phi 2, an English-centric model of 2.7 billion parameters. Fietje demonstrated competitive results with larger language models upon its release. A core emphasis of this work is tran... | {
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2412.15453 | Northeastern Uni at Multilingual Counterspeech Generation: Enhancing
Counter Speech Generation with LLM Alignment through Direct Preference
Optimization | [
"cs.CL",
"cs.AI"
] | The automatic generation of counter-speech (CS) is a critical strategy for addressing hate speech by providing constructive and informed responses. However, existing methods often fail to generate high-quality, impactful, and scalable CS, particularly across diverse linguistic contexts. In this paper, we propose a nove... | {
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2412.15455 | Learning charges and long-range interactions from energies and forces | [
"physics.comp-ph",
"cond-mat.mtrl-sci",
"cs.LG"
] | Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems. However, standard machine learning interatomic potentials (MLIPs) often rely on short-range approximations, limiting their applicability to systems wi... | {
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2412.15462 | TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable
Industrial Robotics Through Large/Vision Language Models | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.HC",
"cs.LG"
] | TalkWithMachines aims to enhance human-robot interaction by contributing to interpretable industrial robotic systems, especially for safety-critical applications. The presented paper investigates recent advancements in Large Language Models (LLMs) and Vision Language Models (VLMs), in combination with robotic perceptio... | {
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2412.15467 | Non-Uniform Parameter-Wise Model Merging | [
"cs.LG",
"cs.AI"
] | Combining multiple machine learning models has long been a technique for enhancing performance, particularly in distributed settings. Traditional approaches, such as model ensembles, work well, but are expensive in terms of memory and compute. Recently, methods based on averaging model parameters have achieved good res... | {
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2412.15468 | Computing the Non-Dominated Flexible Skyline in Vertically Distributed
Datasets with No Random Access | [
"cs.DB"
] | In today's data-driven world, algorithms operating with vertically distributed datasets are crucial due to the increasing prevalence of large-scale, decentralized data storage. These algorithms enhance data privacy by processing data locally, reducing the need for data transfer and minimizing exposure to breaches. They... | {
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2412.15471 | A Review of the Marathi Natural Language Processing | [
"cs.CL"
] | Marathi is one of the most widely used languages in the world. One might expect that the latest advances in NLP research in languages like English reach such a large community. However, NLP advancements in English didn't immediately reach Indian languages like Marathi. There were several reasons for this. They included... | {
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2412.15473 | Predicting Long-Term Student Outcomes from Short-Term EdTech Log Data | [
"cs.CY",
"cs.HC",
"cs.LG"
] | Educational stakeholders are often particularly interested in sparse, delayed student outcomes, like end-of-year statewide exams. The rare occurrence of such assessments makes it harder to identify students likely to fail such assessments, as well as making it slow for researchers and educators to be able to assess the... | {
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2412.15476 | From your Block to our Block: How to Find Shared Structure between
Stochastic Block Models over Multiple Graphs | [
"cs.SI"
] | Stochastic Block Models (SBMs) are a popular approach to modeling single real-world graphs. The key idea of SBMs is to partition the vertices of the graph into blocks with similar edge densities within, as well as between different blocks. However, what if we are given not one but multiple graphs that are unaligned and... | {
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2412.15477 | Difficulty-aware Balancing Margin Loss for Long-tailed Recognition | [
"cs.CV",
"cs.AI",
"cs.LG"
] | When trained with severely imbalanced data, deep neural networks often struggle to accurately recognize classes with only a few samples. Previous studies in long-tailed recognition have attempted to rebalance biased learning using known sample distributions, primarily addressing different classification difficulties at... | {
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2412.15479 | Continual Learning Using Only Large Language Model Prompting | [
"cs.CL",
"cs.AI"
] | We introduce CLOB, a novel continual learning (CL) paradigm wherein a large language model (LLM) is regarded as a black box. Learning is done incrementally via only verbal prompting. CLOB does not fine-tune any part of the LLM or add any trainable parameters to it. It is particularly suitable for LLMs that are accessib... | {
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2412.15483 | Task-Specific Preconditioner for Cross-Domain Few-Shot Learning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Cross-Domain Few-Shot Learning~(CDFSL) methods typically parameterize models with task-agnostic and task-specific parameters. To adapt task-specific parameters, recent approaches have utilized fixed optimization strategies, despite their potential sub-optimality across varying domains or target tasks. To address this i... | {
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2412.15484 | Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and
Dual Evaluation Metrics for Factuality and Coverage | [
"cs.CV"
] | Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations. Our analysis reveals that existing hallucination detection methods struggle with detailed captions. We attribute this to the increasing reliance of MLLMs on their generated text, rather than the input... | {
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2412.15485 | An Agent-based Model for Competitive Agents | [
"cs.MA",
"math.PR"
] | In this paper, we analyze the behavior of a multi-agent system driven by the interactions of agents within a competitive environment. To achieve this, we describe the transition probabilities that underlie the system's stochastic nature. We also derive the Fokker-Planck equations for the density distribution of the num... | {
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2412.15486 | Toward Appearance-based Autonomous Landing Site Identification for
Multirotor Drones in Unstructured Environments | [
"cs.CV",
"cs.LG",
"cs.RO"
] | A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such clas... | {
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2412.15487 | Multi-LLM Text Summarization | [
"cs.CL"
] | In this work, we propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our multi-LLM summarization framework has two fundamentally important steps at each round of conversation: generation and evaluation. These steps are different depend... | {
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2412.15491 | GCA-3D: Towards Generalized and Consistent Domain Adaptation of 3D
Generators | [
"cs.CV"
] | Recently, 3D generative domain adaptation has emerged to adapt the pre-trained generator to other domains without collecting massive datasets and camera pose distributions. Typically, they leverage large-scale pre-trained text-to-image diffusion models to synthesize images for the target domain and then fine-tune the 3... | {
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2412.15492 | DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game | [
"cs.GT",
"cs.LG"
] | Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel Federat... | {
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2412.15494 | PolySmart and VIREO @ TRECVid 2024 Ad-hoc Video Search | [
"cs.IR"
] | This year, we explore generation-augmented retrieval for the TRECVid AVS task. Specifically, the understanding of textual query is enhanced by three generations, including Text2Text, Text2Image, and Image2Text, to address the out-of-vocabulary problem. Using different combinations of them and the rank list retrieved by... | {
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2412.15495 | TL-Training: A Task-Feature-Based Framework for Training Large Language
Models in Tool Use | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with external environments, a critical step toward generalized AI. However, the standard supervised fine-tuning (SFT) approach, which relies on large-scale datasets, often overlooks task-specific characteristics in tool use, le... | {
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2412.15496 | Understanding When and Why Graph Attention Mechanisms Work via Node
Classification | [
"cs.LG",
"stat.ML"
] | Despite the growing popularity of graph attention mechanisms, their theoretical understanding remains limited. This paper aims to explore the conditions under which these mechanisms are effective in node classification tasks through the lens of Contextual Stochastic Block Models (CSBMs). Our theoretical analysis reveal... | {
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2412.15497 | Lexicography Saves Lives (LSL): Automatically Translating
Suicide-Related Language | [
"cs.CL",
"cs.AI"
] | Recent years have seen a marked increase in research that aims to identify or predict risk, intention or ideation of suicide. The majority of new tasks, datasets, language models and other resources focus on English and on suicide in the context of Western culture. However, suicide is global issue and reducing suicide ... | {
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2412.15498 | The First Multilingual Model For The Detection of Suicide Texts | [
"cs.CL",
"cs.AI"
] | Suicidal ideation is a serious health problem affecting millions of people worldwide. Social networks provide information about these mental health problems through users' emotional expressions. We propose a multilingual model leveraging transformer architectures like mBERT, XML-R, and mT5 to detect suicidal text acros... | {
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2412.15499 | A Robust Prototype-Based Network with Interpretable RBF Classifier
Foundations | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Prototype-based classification learning methods are known to be inherently interpretable. However, this paradigm suffers from major limitations compared to deep models, such as lower performance. This led to the development of the so-called deep Prototype-Based Networks (PBNs), also known as prototypical parts models. ... | {
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2412.15501 | Humanlike Cognitive Patterns as Emergent Phenomena in Large Language
Models | [
"cs.CL",
"cs.AI"
] | Research on emergent patterns in Large Language Models (LLMs) has gained significant traction in both psychology and artificial intelligence, motivating the need for a comprehensive review that offers a synthesis of this complex landscape. In this article, we systematically review LLMs' capabilities across three import... | {
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2412.15504 | Mitigating Social Bias in Large Language Models: A Multi-Objective
Approach within a Multi-Agent Framework | [
"cs.CL"
] | Natural language processing (NLP) has seen remarkable advancements with the development of large language models (LLMs). Despite these advancements, LLMs often produce socially biased outputs. Recent studies have mainly addressed this problem by prompting LLMs to behave ethically, but this approach results in unaccepta... | {
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2412.15507 | Stylish and Functional: Guided Interpolation Subject to Physical
Constraints | [
"cs.LG",
"cs.CV"
] | Generative AI is revolutionizing engineering design practices by enabling rapid prototyping and manipulation of designs. One example of design manipulation involves taking two reference design images and using them as prompts to generate a design image that combines aspects of both. Real engineering designs have physic... | {
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2412.15508 | Analyzing Fundamental Diagrams of Mixed Traffic Control at Unsignalized
Intersections | [
"cs.RO"
] | This report examines the effect of mixed traffic, specifically the variation in robot vehicle (RV) penetration rates, on the fundamental diagrams at unsignalized intersections. Through a series of simulations across four distinct intersections, the relationship between traffic flow characteristics were analyzed. The RV... | {
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2412.15509 | PolySmart @ TRECVid 2024 Video Captioning (VTT) | [
"cs.CV",
"cs.MM"
] | In this paper, we present our methods and results for the Video-To-Text (VTT) task at TRECVid 2024, exploring the capabilities of Vision-Language Models (VLMs) like LLaVA and LLaVA-NeXT-Video in generating natural language descriptions for video content. We investigate the impact of fine-tuning VLMs on VTT datasets to ... | {
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2412.15510 | ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction
using Sequence-To-Sequence Transformers | [
"cs.CL",
"cs.IR"
] | Early identification of Adverse Drug Events (ADE) is critical for taking prompt actions while introducing new drugs into the market. These ADEs information are available through various unstructured data sources like clinical study reports, patient health records, social media posts, etc. Extracting ADEs and the relate... | {
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2412.15511 | RESQUE: Quantifying Estimator to Task and Distribution Shift for
Sustainable Model Reusability | [
"cs.LG",
"cs.AI",
"cs.CV"
] | As a strategy for sustainability of deep learning, reusing an existing model by retraining it rather than training a new model from scratch is critical. In this paper, we propose REpresentation Shift QUantifying Estimator (RESQUE), a predictive quantifier to estimate the retraining cost of a model to distributional shi... | {
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2412.15514 | PolySmart @ TRECVid 2024 Medical Video Question Answering | [
"cs.CV",
"cs.MM"
] | Video Corpus Visual Answer Localization (VCVAL) includes question-related video retrieval and visual answer localization in the videos. Specifically, we use text-to-text retrieval to find relevant videos for a medical question based on the similarity of video transcript and answers generated by GPT4. For the visual ans... | {
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2412.15515 | Reconstruction of Contour Lines During the Digitization of Contour Maps
to Build a Digital Elevation Model | [
"cs.CV"
] | Contour map has contour lines that are significant in building a Digital Elevation Model (DEM). During the digitization and pre-processing of contour maps, the contour line intersects with each other or break apart resulting in broken contour segments. These broken segments impose a greater risk while building DEM lead... | {
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2412.15516 | HIGGS: HIerarchy-Guided Graph Stream Summarization | [
"cs.DB"
] | Graph stream summarization refers to the process of processing a continuous stream of edges that form a rapidly evolving graph. The primary challenges in handling graph streams include the impracticality of fully storing the ever-growing datasets and the complexity of supporting graph queries that involve both topologi... | {
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2412.15517 | Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent
Reinforcement Learning | [
"cs.LG"
] | Recently, deep Multi-Agent Reinforcement Learning (MARL) has demonstrated its potential to tackle complex cooperative tasks, pushing the boundaries of AI in collaborative environments. However, the efficiency of these systems is often compromised by inadequate sample utilization and a lack of diversity in learning stra... | {
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2412.15519 | PreNeT: Leveraging Computational Features to Predict Deep Neural Network
Training Time | [
"cs.LG"
] | Training deep learning models, particularly Transformer-based architectures such as Large Language Models (LLMs), demands substantial computational resources and extended training periods. While optimal configuration and infrastructure selection can significantly reduce associated costs, this optimization requires prel... | {
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2412.15523 | InstructOCR: Instruction Boosting Scene Text Spotting | [
"cs.CV",
"cs.AI"
] | In the field of scene text spotting, previous OCR methods primarily relied on image encoders and pre-trained text information, but they often overlooked the advantages of incorporating human language instructions. To address this gap, we propose InstructOCR, an innovative instruction-based scene text spotting model tha... | {
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2412.15524 | HREF: Human Response-Guided Evaluation of Instruction Following in
Language Models | [
"cs.CL",
"cs.AI"
] | Evaluating the capability of Large Language Models (LLMs) in following instructions has heavily relied on a powerful LLM as the judge, introducing unresolved biases that deviate the judgments from human judges. In this work, we reevaluate various choices for automatic evaluation on a wide range of instruction-following... | {
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2412.15525 | Generalized Back-Stepping Experience Replay in Sparse-Reward
Environments | [
"cs.LG",
"cs.AI"
] | Back-stepping experience replay (BER) is a reinforcement learning technique that can accelerate learning efficiency in reversible environments. BER trains an agent with generated back-stepping transitions of collected experiences and normal forward transitions. However, the original algorithm is designed for a dense-re... | {
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2412.15526 | SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated
Semi-Supervised Medical Image Segmentation | [
"cs.CV"
] | Although semi-supervised learning has made significant advances in the field of medical image segmentation, fully annotating a volumetric sample slice by slice remains a costly and time-consuming task. Even worse, most of the existing approaches pay much attention to image-level information and ignore semantic features... | {
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2412.15527 | Underwater Image Quality Assessment: A Perceptual Framework Guided by
Physical Imaging | [
"eess.IV",
"cs.CV"
] | In this paper, we propose a physically imaging-guided framework for underwater image quality assessment (UIQA), called PIGUIQA. First, we formulate UIQA as a comprehensive problem that considers the combined effects of direct transmission attenuation and backwards scattering on image perception. On this basis, we incor... | {
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2412.15529 | XRAG: eXamining the Core -- Benchmarking Foundational Components in
Advanced Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI"
] | Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current. We introduce XRAG, an open-source, modular codebase that facilitates exhau... | {
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2412.15532 | Improved Forecasts of Global Extreme Marine Heatwaves Through a
Physics-guided Data-driven Approach | [
"physics.ao-ph",
"cs.AI"
] | The unusually warm sea surface temperature events known as marine heatwaves (MHWs) have a profound impact on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study... | {
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2412.15533 | From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology
classification with unsupervised domain adaption | [
"astro-ph.GA",
"astro-ph.IM",
"cs.CV"
] | The DESI Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys: the Dark Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey (BASS), and the Mayall z-band Legacy Survey (MzLS). The citizen science project Galaxy Zoo DECaLS 5 (GZD-5) has provided extensive and detailed morphology labels for ... | {
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2412.15534 | SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for
Learning to Branch | [
"cs.LG"
] | Mixed Integer Linear Program (MILP) solvers are mostly built upon a Branch-and-Bound (B\&B) algorithm, where the efficiency of traditional solvers heavily depends on hand-crafted heuristics for branching. The past few years have witnessed the increasing popularity of data-driven approaches to automatically learn these ... | {
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2412.15536 | The Impact of Cut Layer Selection in Split Federated Learning | [
"cs.DC",
"cs.LG"
] | Split Federated Learning (SFL) is a distributed machine learning paradigm that combines federated learning and split learning. In SFL, a neural network is partitioned at a cut layer, with the initial layers deployed on clients and remaining layers on a training server. There are two main variants of SFL: SFL-V1 where t... | {
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2412.15537 | Enhancing Large-scale UAV Route Planing with Global and Local Features
via Reinforcement Graph Fusion | [
"cs.AI",
"cs.RO"
] | Numerous remarkable advancements have been made in accuracy, speed, and parallelism for solving the Unmanned Aerial Vehicle Route Planing (UAVRP). However, existing UAVRP solvers face challenges when attempting to scale effectively and efficiently for larger instances. In this paper, we present a generalization framewo... | {
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2412.15538 | FedRLHF: A Convergence-Guaranteed Federated Framework for
Privacy-Preserving and Personalized RLHF | [
"cs.LG",
"cs.AI",
"cs.CR"
] | In the era of increasing privacy concerns and demand for personalized experiences, traditional Reinforcement Learning with Human Feedback (RLHF) frameworks face significant challenges due to their reliance on centralized data. We introduce Federated Reinforcement Learning with Human Feedback (FedRLHF), a novel framewor... | {
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2412.15540 | MRAG: A Modular Retrieval Framework for Time-Sensitive Question
Answering | [
"cs.CL"
] | Understanding temporal relations and answering time-sensitive questions is crucial yet a challenging task for question-answering systems powered by large language models (LLMs). Existing approaches either update the parametric knowledge of LLMs with new facts, which is resource-intensive and often impractical, or integ... | {
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2412.15541 | ChangeDiff: A Multi-Temporal Change Detection Data Generator with
Flexible Text Prompts via Diffusion Model | [
"cs.CV",
"cs.AI"
] | Data-driven deep learning models have enabled tremendous progress in change detection (CD) with the support of pixel-level annotations. However, collecting diverse data and manually annotating them is costly, laborious, and knowledge-intensive. Existing generative methods for CD data synthesis show competitive potentia... | {
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2412.15544 | VLM-RL: A Unified Vision Language Models and Reinforcement Learning
Framework for Safe Autonomous Driving | [
"cs.RO",
"cs.AI",
"cs.CV"
] | In recent years, reinforcement learning (RL)-based methods for learning driving policies have gained increasing attention in the autonomous driving community and have achieved remarkable progress in various driving scenarios. However, traditional RL approaches rely on manually engineered rewards, which require extensiv... | {
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2412.15545 | Climate Policy Elites' Twitter Interactions across Nine Countries | [
"cs.SI",
"cs.CY"
] | We identified the Twitter accounts of 941 climate change policy actors across nine countries, and collected their activities from 2017--2022, totalling 48 million activities from 17,700 accounts at different organizational levels. There is considerable temporal and cross-national variation in how prominent climate-rela... | {
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2412.15546 | De-singularity Subgradient for the $q$-th-Powered $\ell_p$-Norm Weber
Location Problem | [
"math.OC",
"cs.LG"
] | The Weber location problem is widely used in several artificial intelligence scenarios. However, the gradient of the objective does not exist at a considerable set of singular points. Recently, a de-singularity subgradient method has been proposed to fix this problem, but it can only handle the $q$-th-powered $\ell_2$-... | {
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2412.15547 | NGQA: A Nutritional Graph Question Answering Benchmark for Personalized
Health-aware Nutritional Reasoning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Diet plays a critical role in human health, yet tailoring dietary reasoning to individual health conditions remains a major challenge. Nutrition Question Answering (QA) has emerged as a popular method for addressing this problem. However, current research faces two critical limitations. On one hand, the absence of data... | {
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2412.15550 | EGSRAL: An Enhanced 3D Gaussian Splatting based Renderer with Automated
Labeling for Large-Scale Driving Scene | [
"cs.CV"
] | 3D Gaussian Splatting (3D GS) has gained popularity due to its faster rendering speed and high-quality novel view synthesis. Some researchers have explored using 3D GS for reconstructing driving scenes. However, these methods often rely on various data types, such as depth maps, 3D boxes, and trajectories of moving obj... | {
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2412.15551 | New record-breaking binary linear codes constructed from group codes | [
"cs.IT",
"math.IT"
] | In this paper, we employ group rings and automorphism groups of binary linear codes to construct new record-breaking binary linear codes. We consider the semidirect product of abelian groups and cyclic groups and use these groups to construct linear codes. Finally, we obtain some linear codes which have better paramete... | {
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2412.15553 | AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed
Learning | [
"cs.LG",
"cs.DC"
] | As data volumes expand rapidly, distributed machine learning has become essential for addressing the growing computational demands of modern AI systems. However, training models in distributed environments is challenging with participants hold skew, Non-Independent-Identically distributed (Non-IID) data. Low-Rank Adapt... | {
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2412.15554 | Architecture-Aware Learning Curve Extrapolation via Graph Ordinary
Differential Equation | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Learning curve extrapolation predicts neural network performance from early training epochs and has been applied to accelerate AutoML, facilitating hyperparameter tuning and neural architecture search. However, existing methods typically model the evolution of learning curves in isolation, neglecting the impact of neur... | {
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2412.15557 | MORTAR: Metamorphic Multi-turn Testing for LLM-based Dialogue Systems | [
"cs.SE",
"cs.CL"
] | With the widespread application of LLM-based dialogue systems in daily life, quality assurance has become more important than ever. Recent research has successfully introduced methods to identify unexpected behaviour in single-turn scenarios. However, multi-turn dialogue testing remains underexplored, with the Oracle p... | {
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2412.15559 | Spatial Clustering of Citizen Science Data Improves Downstream Species
Distribution Models | [
"cs.LG"
] | Citizen science biodiversity data present great opportunities for ecology and conservation across vast spatial and temporal scales. However, the opportunistic nature of these data lacks the sampling structure required by modeling methodologies that address a pervasive challenge in ecological data collection: imperfect ... | {
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2412.15560 | Predicting Artificial Neural Network Representations to Learn
Recognition Model for Music Identification from Brain Recordings | [
"q-bio.NC",
"cs.LG",
"cs.SD",
"eess.AS",
"eess.SP"
] | Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representati... | {
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2412.15563 | In-context Continual Learning Assisted by an External Continual Learner | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Existing continual learning (CL) methods mainly rely on fine-tuning or adapting large language models (LLMs). They still suffer from catastrophic forgetting (CF). Little work has been done to exploit in-context learning (ICL) to leverage the extensive knowledge within LLMs for CL without updating any parameters. Howeve... | {
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2412.15570 | DefFiller: Mask-Conditioned Diffusion for Salient Steel Surface Defect
Generation | [
"cs.CV"
] | Current saliency-based defect detection methods show promise in industrial settings, but the unpredictability of defects in steel production environments complicates dataset creation, hampering model performance. Existing data augmentation approaches using generative models often require pixel-level annotations, which ... | {
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2412.15571 | Continual Learning Using a Kernel-Based Method Over Foundation Models | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | Continual learning (CL) learns a sequence of tasks incrementally. This paper studies the challenging CL setting of class-incremental learning (CIL). CIL has two key challenges: catastrophic forgetting (CF) and inter-task class separation (ICS). Despite numerous proposed methods, these issues remain persistent obstacles... | {
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2412.15573 | Multi Agent Reinforcement Learning for Sequential Satellite Assignment
Problems | [
"cs.MA",
"cs.LG"
] | Assignment problems are a classic combinatorial optimization problem in which a group of agents must be assigned to a group of tasks such that maximum utility is achieved while satisfying assignment constraints. Given the utility of each agent completing each task, polynomial-time algorithms exist to solve a single ass... | {
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2412.15574 | J-EDI QA: Benchmark for deep-sea organism-specific multimodal LLM | [
"cs.CV"
] | Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has made available the JAMSTEC Earth Deep-sea Image (J-EDI), a deep-sea video and image archive (https://www.godac.jamstec.go.jp/jedi/e/index.html). This archive serves as a valuable resource for researchers and scholars interested in deep-sea imagery. The ... | {
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2412.15576 | QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped
Robot Learning | [
"cs.RO",
"cs.CV"
] | This paper addresses the inherent inference latency challenges associated with deploying multimodal large language models (MLLM) in quadruped vision-language-action (QUAR-VLA) tasks. Our investigation reveals that conventional parameter reduction techniques ultimately impair the performance of the language foundation m... | {
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2412.15577 | SaliencyI2PLoc: saliency-guided image-point cloud localization using
contrastive learning | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Image to point cloud global localization is crucial for robot navigation in GNSS-denied environments and has become increasingly important for multi-robot map fusion and urban asset management. The modality gap between images and point clouds poses significant challenges for cross-modality fusion. Current cross-modalit... | {
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2412.15579 | Score-based Generative Diffusion Models for Social Recommendations | [
"cs.SI",
"cs.AI",
"cs.LG"
] | With the prevalence of social networks on online platforms, social recommendation has become a vital technique for enhancing personalized recommendations. The effectiveness of social recommendations largely relies on the social homophily assumption, which presumes that individuals with social connections often share si... | {
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2412.15582 | A Deep Probabilistic Framework for Continuous Time Dynamic Graph
Generation | [
"cs.LG"
] | Recent advancements in graph representation learning have shifted attention towards dynamic graphs, which exhibit evolving topologies and features over time. The increased use of such graphs creates a paramount need for generative models suitable for applications such as data augmentation, obfuscation, and anomaly dete... | {
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2412.15583 | Tracking the 2024 US Presidential Election Chatter on TikTok: A Public
Multimodal Dataset | [
"cs.SI"
] | This paper presents the TikTok 2024 U.S. Presidential Election Dataset, a large-scale, resource designed to advance research into political communication and social media dynamics. The dataset comprises 3.14 million videos published on TikTok between November 1, 2023, and October 16, 2024, encompassing video ids and tr... | {
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2412.15587 | Dexterous Manipulation Based on Prior Dexterous Grasp Pose Knowledge | [
"cs.RO",
"cs.LG"
] | Dexterous manipulation has received considerable attention in recent research. Predominantly, existing studies have concentrated on reinforcement learning methods to address the substantial degrees of freedom in hand movements. Nonetheless, these methods typically suffer from low efficiency and accuracy. In this work, ... | {
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} |
2412.15588 | NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional
Generalization | [
"cs.CL"
] | Compositional generalization is crucial for artificial intelligence agents to solve complex vision-language reasoning tasks. Neuro-symbolic approaches have demonstrated promise in capturing compositional structures, but they face critical challenges: (a) reliance on predefined predicates for symbolic representations th... | {
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} |
2412.15589 | Pre-training Graph Neural Networks on Molecules by Using
Subgraph-Conditioned Graph Information Bottleneck | [
"cs.LG",
"cs.AI"
] | This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in acquiring labeled molecules, the previous pre-training methods still rely on semantic subgraphs, i.e., functional gro... | {
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} |
2412.15590 | SemDP: Semantic-level Differential Privacy Protection for Face Datasets | [
"cs.CV",
"cs.CR"
] | While large-scale face datasets have advanced deep learning-based face analysis, they also raise privacy concerns due to the sensitive personal information they contain. Recent schemes have implemented differential privacy to protect face datasets. However, these schemes generally treat each image as a separate databas... | {
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} |
2412.15593 | Machine Learning Techniques for Pattern Recognition in High-Dimensional
Data Mining | [
"cs.LG",
"cs.AI"
] | This paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and sparse data environments. By converting the frequent pattern mining task into a classification problem,... | {
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} |
2412.15594 | Template-Driven LLM-Paraphrased Framework for Tabular Math Word Problem
Generation | [
"cs.CL"
] | Solving tabular math word problems (TMWPs) has become a critical role in evaluating the mathematical reasoning ability of large language models (LLMs), where large-scale TMWP samples are commonly required for LLM fine-tuning. Since the collection of high-quality TMWP datasets is costly and time-consuming, recent resear... | {
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} |
2412.15595 | Mask-RadarNet: Enhancing Transformer With Spatial-Temporal Semantic
Context for Radar Object Detection in Autonomous Driving | [
"cs.CV",
"cs.AI"
] | As a cost-effective and robust technology, automotive radar has seen steady improvement during the last years, making it an appealing complement to commonly used sensors like camera and LiDAR in autonomous driving. Radio frequency data with rich semantic information are attracting more and more attention. Most current ... | {
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} |
2412.15598 | SODor: Long-Term EEG Partitioning for Seizure Onset Detection | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Deep learning models have recently shown great success in classifying epileptic patients using EEG recordings. Unfortunately, classification-based methods lack a sound mechanism to detect the onset of seizure events. In this work, we propose a two-stage framework, \method, that explicitly models seizure onset through a... | {
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} |
2412.15601 | Gaze Label Alignment: Alleviating Domain Shift for Gaze Estimation | [
"cs.CV"
] | Gaze estimation methods encounter significant performance deterioration when being evaluated across different domains, because of the domain gap between the testing and training data. Existing methods try to solve this issue by reducing the deviation of data distribution, however, they ignore the existence of label dev... | {
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} |
2412.15602 | Music Genre Classification: Ensemble Learning with Subcomponents-level
Attention | [
"cs.SD",
"cs.IR",
"cs.LG",
"cs.MM",
"eess.AS"
] | Music Genre Classification is one of the most popular topics in the fields of Music Information Retrieval (MIR) and digital signal processing. Deep Learning has emerged as the top performer for classifying music genres among various methods. The letter introduces a novel approach by combining ensemble learning with att... | {
"Other": 1,
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} |
2412.15603 | Dynamic Label Name Refinement for Few-Shot Dialogue Intent
Classification | [
"cs.CL"
] | Dialogue intent classification aims to identify the underlying purpose or intent of a user's input in a conversation. Current intent classification systems encounter considerable challenges, primarily due to the vast number of possible intents and the significant semantic overlap among similar intent classes. In this p... | {
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} |
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