id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.20105 | ST$^3$: Accelerating Multimodal Large Language Model by Spatial-Temporal
Visual Token Trimming | [
"cs.CV"
] | Multimodal large language models (MLLMs) enhance their perceptual capabilities by integrating visual and textual information. However, processing the massive number of visual tokens incurs a significant computational cost. Existing analysis of the MLLM attention mechanisms remains shallow, leading to coarse-grain token... | {
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2412.20110 | Cross-Modal Mapping: Eliminating the Modality Gap for Few-Shot Image
Classification | [
"cs.CV"
] | In few-shot image classification tasks, methods based on pretrained vision-language models (such as CLIP) have achieved significant progress. Many existing approaches directly utilize visual or textual features as class prototypes, however, these features fail to adequately represent their respective classes. We identi... | {
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2412.20112 | Towards influence centrality: where to not add an edge in the network? | [
"eess.SY",
"cs.SY"
] | In this work, we consider a strongly connected group of individuals involved in decision-making. The opinions of the individuals evolve using the Friedkin-Johnsen (FJ) model. We consider that there are two competing `influencers' (stubborn agents) vying for control over the final opinion of the group. We investigate th... | {
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2412.20115 | Gradient Descent Methods for Regularized Optimization | [
"math.OC",
"cs.LG"
] | Regularization is a widely recognized technique in mathematical optimization. It can be used to smooth out objective functions, refine the feasible solution set, or prevent overfitting in machine learning models. Due to its simplicity and robustness, the gradient descent (GD) method is one of the primary methods used f... | {
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2412.20117 | Neuromorphic circuit for temporal odor encoding in turbulent
environments | [
"cs.NE"
] | Natural odor environments present turbulent and dynamic conditions, causing chemical signals to fluctuate in space, time, and intensity. While many species have evolved highly adaptive behavioral responses to such variability, the emerging field of neuromorphic olfaction continues to grapple with the challenge of effic... | {
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2412.20127 | M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine
Translation Evaluation | [
"cs.CL",
"cs.AI"
] | Recent advancements in large language models (LLMs) have given rise to the LLM-as-a-judge paradigm, showcasing their potential to deliver human-like judgments. However, in the field of machine translation (MT) evaluation, current LLM-as-a-judge methods fall short of learned automatic metrics. In this paper, we propose ... | {
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2412.20131 | A finite strain model for fiber angle plasticity of textile fabrics
based on isogeometric shell finite elements | [
"cs.CE"
] | This work presents a shear elastoplasticity model for textile fabrics within the theoretical framework of anisotropic Kirchhoff-Love shells with bending of embedded fibers proposed by Duong et al. (2023). The plasticity model aims at capturing the rotational inter-ply frictional sliding between fiber families in textil... | {
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2412.20138 | TradingAgents: Multi-Agents LLM Financial Trading Framework | [
"q-fin.TR",
"cs.AI",
"cs.CE",
"cs.LG"
] | Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have largely focused on single-agent systems handling specific tasks or multi-agent frameworks independently gathering data. However, multi-agent systems' potential to r... | {
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2412.20145 | Efficient Multi-Agent Collaboration with Tool Use for Online Planning in
Complex Table Question Answering | [
"cs.CL"
] | Complex table question answering (TQA) aims to answer questions that require complex reasoning, such as multi-step or multi-category reasoning, over data represented in tabular form. Previous approaches demonstrated notable performance by leveraging either closed-source large language models (LLMs) or fine-tuned open-w... | {
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2412.20148 | DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving
Talking Face Synthesis | [
"cs.CV",
"cs.HC"
] | Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis me... | {
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2412.20155 | Stable-TTS: Stable Speaker-Adaptive Text-to-Speech Synthesis via Prosody
Prompting | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Speaker-adaptive Text-to-Speech (TTS) synthesis has attracted considerable attention due to its broad range of applications, such as personalized voice assistant services. While several approaches have been proposed, they often exhibit high sensitivity to either the quantity or the quality of target speech samples. To ... | {
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2412.20156 | Distilled Transformers with Locally Enhanced Global Representations for
Face Forgery Detection | [
"cs.CV"
] | Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in mo... | {
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2412.20157 | UniRestorer: Universal Image Restoration via Adaptively Estimating Image
Degradation at Proper Granularity | [
"cs.CV"
] | Recently, considerable progress has been made in allin-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are limited in leveraging degradation-specific restoration, and the latter suffer from the inevitable error in degradation estimation. Consequen... | {
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2412.20158 | Stronger together? The homophily trap in networks | [
"cs.SI",
"cs.CY",
"cs.MA",
"physics.soc-ph",
"stat.AP"
] | While homophily -- the tendency to link with similar others -- may nurture a sense of belonging and shared values, it can also hinder diversity and widen inequalities. Here, we unravel this trade-off analytically, revealing homophily traps for minority groups: scenarios where increased homophilic interaction among mino... | {
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2412.20162 | Multi-Modality Driven LoRA for Adverse Condition Depth Estimation | [
"cs.CV"
] | The autonomous driving community is increasingly focused on addressing corner case problems, particularly those related to ensuring driving safety under adverse conditions (e.g., nighttime, fog, rain). To this end, the task of Adverse Condition Depth Estimation (ACDE) has gained significant attention. Previous approach... | {
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2412.20163 | Topic-Aware Knowledge Graph with Large Language Models for
Interoperability in Recommender Systems | [
"cs.IR",
"cs.AI"
] | The use of knowledge graphs in recommender systems has become one of the common approaches to addressing data sparsity and cold start problems. Recent advances in large language models (LLMs) offer new possibilities for processing side and context information within knowledge graphs. However, consistent integration acr... | {
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2412.20164 | StyleAutoEncoder for manipulating image attributes using pre-trained
StyleGAN | [
"cs.CV",
"cs.AI"
] | Deep conditional generative models are excellent tools for creating high-quality images and editing their attributes. However, training modern generative models from scratch is very expensive and requires large computational resources. In this paper, we introduce StyleAutoEncoder (StyleAE), a lightweight AutoEncoder mo... | {
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2412.20166 | LoL-PIM: Long-Context LLM Decoding with Scalable DRAM-PIM System | [
"cs.AR",
"cs.AI"
] | The expansion of large language models (LLMs) with hundreds of billions of parameters presents significant challenges to computational resources, particularly data movement and memory bandwidth. Long-context LLMs, which process sequences of tens of thousands of tokens, further increase the demand on the memory system a... | {
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2412.20167 | Conformal Risk Control for Pulmonary Nodule Detection | [
"cs.CV"
] | Quantitative tools are increasingly appealing for decision support in healthcare, driven by the growing capabilities of advanced AI systems. However, understanding the predictive uncertainties surrounding a tool's output is crucial for decision-makers to ensure reliable and transparent decisions. In this paper, we pres... | {
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2412.20170 | Real-time Calibration Model for Low-cost Sensor in Fine-grained Time
series | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Precise measurements from sensors are crucial, but data is usually collected from low-cost, low-tech systems, which are often inaccurate. Thus, they require further calibrations. To that end, we first identify three requirements for effective calibration under practical low-tech sensor conditions. Based on the requirem... | {
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2412.20171 | Geo-ConvGRU: Geographically Masked Convolutional Gated Recurrent Unit
for Bird-Eye View Segmentation | [
"cs.CV"
] | Convolutional Neural Networks (CNNs) have significantly impacted various computer vision tasks, however, they inherently struggle to model long-range dependencies explicitly due to the localized nature of convolution operations. Although Transformers have addressed limitations in long-range dependencies for the spatial... | {
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2412.20172 | On dataset transferability in medical image classification | [
"cs.CV"
] | Current transferability estimation methods designed for natural image datasets are often suboptimal in medical image classification. These methods primarily focus on estimating the suitability of pre-trained source model features for a target dataset, which can lead to unrealistic predictions, such as suggesting that t... | {
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2412.20173 | Debiased Nonparametric Regression for Statistical Inference and
Distributionally Robustness | [
"stat.ME",
"cs.LG",
"econ.EM",
"math.ST",
"stat.ML",
"stat.TH"
] | This study proposes a debiasing method for smooth nonparametric estimators. While machine learning techniques such as random forests and neural networks have demonstrated strong predictive performance, their theoretical properties remain relatively underexplored. Specifically, many modern algorithms lack assurances of ... | {
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2412.20177 | Mining Platoon Patterns from Traffic Videos | [
"cs.CV",
"cs.DB"
] | Discovering co-movement patterns from urban-scale video data sources has emerged as an attractive topic. This task aims to identify groups of objects that travel together along a common route, which offers effective support for government agencies in enhancing smart city management. However, the previous work has made ... | {
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2412.20185 | Pushing the Envelope of Low-Bit LLM via Dynamic Error Compensation | [
"cs.LG"
] | Quantization of Large Language Models (LLMs) has recently gained popularity, particularly for on-device settings with limited hardware resources. While efficient, quantization inevitably degrades model quality, especially in aggressive low-bit settings such as 3-bit and 4-bit precision. In this paper, we propose QDEC, ... | {
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2412.20189 | Accurate Coresets for Latent Variable Models and Regularized Regression | [
"cs.LG",
"stat.ML"
] | Accurate coresets are a weighted subset of the original dataset, ensuring a model trained on the accurate coreset maintains the same level of accuracy as a model trained on the full dataset. Primarily, these coresets have been studied for a limited range of machine learning models. In this paper, we introduce a unified... | {
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2412.20192 | Learning physical unknowns from hydrodynamic shock and material
interface features in ICF capsule implosions | [
"physics.comp-ph",
"cs.LG",
"hep-ph"
] | In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, h... | {
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2412.20193 | Imitation Learning from Suboptimal Demonstrations via Meta-Learning An
Action Ranker | [
"cs.LG",
"cs.AI"
] | A major bottleneck in imitation learning is the requirement of a large number of expert demonstrations, which can be expensive or inaccessible. Learning from supplementary demonstrations without strict quality requirements has emerged as a powerful paradigm to address this challenge. However, previous methods often fai... | {
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2412.20195 | Lower bounds on transformers with infinite precision | [
"cs.LG",
"cs.AI",
"stat.ML"
] | In this note, we use the VC dimension technique to prove the first lower bound against one-layer softmax transformers with infinite precision. We do so for two tasks: function composition, considered by Peng, Narayanan, and Papadimitriou, and the SUM$_2$ task, considered by Sanford, Hsu, and Telgarsky. | {
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2412.20200 | Federated Unlearning with Gradient Descent and Conflict Mitigation | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.DC"
] | Federated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly remember clients' local data. Therefore, it's necessary to effectively remove the target client's data from the FL global model to ease the ri... | {
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2412.20201 | Injecting Explainability and Lightweight Design into Weakly Supervised
Video Anomaly Detection Systems | [
"cs.CV",
"cs.AI"
] | Weakly Supervised Monitoring Anomaly Detection (WSMAD) utilizes weak supervision learning to identify anomalies, a critical task for smart city monitoring. However, existing multimodal approaches often fail to meet the real-time and interpretability requirements of edge devices due to their complexity. This paper prese... | {
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2412.20203 | No-regret learning in harmonic games: Extrapolation in the face of
conflicting interests | [
"cs.GT",
"cs.LG",
"cs.MA",
"math.OC"
] | The long-run behavior of multi-agent learning - and, in particular, no-regret learning - is relatively well-understood in potential games, where players have aligned interests. By contrast, in harmonic games - the strategic counterpart of potential games, where players have conflicting interests - very little is known ... | {
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2412.20206 | Towards Visual Grounding: A Survey | [
"cs.CV"
] | Visual Grounding is also known as Referring Expression Comprehension and Phrase Grounding. It involves localizing a natural number of specific regions within an image based on a given textual description. The objective of this task is to emulate the prevalent referential relationships in social conversations, equipping... | {
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2412.20210 | Towards Real-Time 2D Mapping: Harnessing Drones, AI, and Computer Vision
for Advanced Insights | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This paper presents an advanced mapping system that combines drone imagery with machine learning and computer vision to overcome challenges in speed, accuracy, and adaptability across diverse terrains. By automating processes like feature detection, image matching, and stitching, the system produces seamless, high-reso... | {
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2412.20211 | Sequence Generation Modeling for Continuous Value Prediction | [
"cs.LG",
"cs.IR"
] | Continuous value prediction (CVP) plays a crucial role in short video recommendation, capturing user preferences through precise numerical estimations. However, traditional regression-based methods often struggle with challenges like wide value ranges and imbalanced data, leading to prediction bias. While ordinal class... | {
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2412.20212 | Building a Rich Dataset to Empower the Persian Question Answering
Systems | [
"cs.CL",
"cs.AI"
] | Question answering systems provide short, precise, and specific answers to questions. So far, many robust question answering systems have been developed for English, while some languages with fewer resources, like Persian, have few numbers of standard dataset. In this study, a comprehensive open-domain dataset is prese... | {
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2412.20213 | Decoding Emotion: Speech Perception Patterns in Individuals with
Self-reported Depression | [
"cs.CL",
"cs.AI"
] | The current study examines the relationship between self-reported depression and the perception of affective speech within the Indian population. PANAS and PHQ-9 were used to assess current mood and depression, respectively. Participants' emotional reactivity was recorded on a valence and arousal scale against the affe... | {
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2412.20215 | IMSSA: Deploying modern state-space models on memristive in-memory
compute hardware | [
"cs.LG",
"cs.AR"
] | Processing long temporal sequences is a key challenge in deep learning. In recent years, Transformers have become state-of-the-art for this task, but suffer from excessive memory requirements due to the need to explicitly store the sequences. To address this issue, structured state-space sequential (S4) models recently... | {
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2412.20218 | YAD: Leveraging T5 for Improved Automatic Diacritization of Yor\`ub\'a
Text | [
"cs.CL"
] | In this work, we present Yor\`ub\'a automatic diacritization (YAD) benchmark dataset for evaluating Yor\`ub\'a diacritization systems. In addition, we pre-train text-to-text transformer, T5 model for Yor\`ub\'a and showed that this model outperform several multilingually trained T5 models. Lastly, we showed that more d... | {
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2412.20223 | AfriHG: News headline generation for African Languages | [
"cs.CL"
] | This paper introduces AfriHG -- a news headline generation dataset created by combining from XLSum and MasakhaNEWS datasets focusing on 16 languages widely spoken by Africa. We experimented with two seq2eq models (mT5-base and AfriTeVa V2), and Aya-101 LLM. Our results show that Africa-centric seq2seq models such as Af... | {
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2412.20225 | Machine and Deep Learning for Credit Scoring: A compliant approach | [
"q-fin.RM",
"cs.LG"
] | Credit Scoring is one of the problems banks and financial institutions have to solve on a daily basis. If the state-of-the-art research in Machine and Deep Learning for finance has reached interesting results about Credit Scoring models, usage of such models in a heavily regulated context such as the one in banks has n... | {
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2412.20226 | Embodiment-Agnostic Navigation Policy Trained with Visual Demonstrations | [
"cs.RO"
] | Learning to navigate in unstructured environments is a challenging task for robots. While reinforcement learning can be effective, it often requires extensive data collection and can pose risk. Learning from expert demonstrations, on the other hand, offers a more efficient approach. However, many existing methods rely ... | {
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2412.20227 | LLM Reasoning Engine: Specialized Training for Enhanced Mathematical
Reasoning | [
"cs.CL"
] | Large Language Models (LLMs) have shown remarkable performance in various natural language processing tasks but face challenges in mathematical reasoning, where complex problem-solving requires both linguistic understanding and mathematical reasoning skills. Existing approaches to address this challenge often rely on e... | {
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2412.20230 | Leveraging Large Language Models for Enhancing Autonomous Vehicle
Perception | [
"cs.RO",
"cs.AI"
] | Autonomous vehicles (AVs) rely on sophisticated perception systems to interpret their surroundings, a cornerstone for safe navigation and decision-making. The integration of Large Language Models (LLMs) into AV perception frameworks offers an innovative approach to address challenges in dynamic environments, sensor fus... | {
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2412.20231 | How To Think About End-To-End Encryption and AI: Training, Processing,
Disclosure, and Consent | [
"cs.CR",
"cs.AI"
] | End-to-end encryption (E2EE) has become the gold standard for securing communications, bringing strong confidentiality and privacy guarantees to billions of users worldwide. However, the current push towards widespread integration of artificial intelligence (AI) models, including in E2EE systems, raises some serious se... | {
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2412.20232 | Plastic Waste Classification Using Deep Learning: Insights from the
WaDaBa Dataset | [
"cs.CV"
] | With the increasing use of plastic, the challenges associated with managing plastic waste have become more challenging, emphasizing the need of effective solutions for classification and recycling. This study explores the potential of deep learning, focusing on convolutional neural networks (CNNs) and object detection ... | {
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2412.20233 | Decentralized Unlabeled Multi-Agent Navigation in Continuous Space | [
"cs.MA"
] | In this work, we study the problem where a group of mobile agents needs to reach a set of goal locations, but it does not matter which agent reaches a specific goal. Unlike most of the existing works on this topic that typically assume the existence of the centralized planner (or controller) and limit the agents' moves... | {
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2412.20235 | Enhancing Transfer Learning for Medical Image Classification with SMOTE:
A Comparative Study | [
"eess.IV",
"cs.CV"
] | This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL-using pre-trained models on the ImageNet dataset-varies due to domain-specific challenges. We... | {
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2412.20241 | A Hybrid Quantum-Classical Autoencoder Framework for End-to-End
Communication Systems | [
"cs.IT",
"eess.SP",
"math.IT",
"quant-ph"
] | This paper investigates the application of quantum machine learning to End-to-End (E2E) communication systems in wireless fading scenarios. We introduce a novel hybrid quantum-classical autoencoder architecture that combines parameterized quantum circuits with classical deep neural networks (DNNs). Specifically, we pro... | {
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2412.20250 | Recommender Engine Driven Client Selection in Federated Brain Tumor
Segmentation | [
"cs.LG",
"cs.CV"
] | This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2024). In the evolving landscape of FL, the judicious selection of collaborators emerges as a critical determinant for the success and effici... | {
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2412.20251 | ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge
Frequency Control and Uncertainty | [
"cs.CL"
] | The rapid development of LLMs has sparked extensive research into their factual knowledge. Current works claim that LLMs fall short on questions requiring less frequent knowledge. However, their proof is incomplete since they only study the influence of entity frequency, which can not fully represent knowledge frequenc... | {
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2412.20253 | Election of Collaborators via Reinforcement Learning for Federated Brain
Tumor Segmentation | [
"cs.LG",
"cs.CV"
] | Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challenging. We present RL-HSimAgg, a novel reinforcement learning (RL) and similarity-weighted aggregatio... | {
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2412.20255 | An Anomaly Detection System Based on Generative Classifiers for
Controller Area Network | [
"cs.CR",
"cs.LG"
] | As electronic systems become increasingly complex and prevalent in modern vehicles, securing onboard networks is crucial, particularly as many of these systems are safety-critical. Researchers have demonstrated that modern vehicles are susceptible to various types of attacks, enabling attackers to gain control and comp... | {
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2412.20256 | Towards Ideal Temporal Graph Neural Networks: Evaluations and
Conclusions after 10,000 GPU Hours | [
"cs.LG",
"cs.SI"
] | Temporal Graph Neural Networks (TGNNs) have emerged as powerful tools for modeling dynamic interactions across various domains. The design space of TGNNs is notably complex, given the unique challenges in runtime efficiency and scalability raised by the evolving nature of temporal graphs. We contend that many of the ex... | {
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2412.20264 | Scoring with Large Language Models: A Study on Measuring Empathy of
Responses in Dialogues | [
"cs.CL"
] | In recent years, Large Language Models (LLMs) have become increasingly more powerful in their ability to complete complex tasks. One such task in which LLMs are often employed is scoring, i.e., assigning a numerical value from a certain scale to a subject. In this paper, we strive to understand how LLMs score, specific... | {
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2412.20269 | TeLU Activation Function for Fast and Stable Deep Learning | [
"cs.LG"
] | We propose the Hyperbolic Tangent Exponential Linear Unit (TeLU), a neural network hidden activation function defined as TeLU(x)=xtanh(exp(x)). TeLU's design is grounded in the core principles of key activation functions, achieving strong convergence by closely approximating the identity function in its active region w... | {
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2412.20271 | High-fidelity social learning via shared episodic memories enhances
collaborative foraging through mnemonic convergence | [
"cs.AI",
"cs.MA"
] | Social learning, a cornerstone of cultural evolution, enables individuals to acquire knowledge by observing and imitating others. At the heart of its efficacy lies episodic memory, which encodes specific behavioral sequences to facilitate learning and decision-making. This study explores the interrelation between episo... | {
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2412.20275 | Few-shot Algorithm Assurance | [
"cs.CV"
] | In image classification tasks, deep learning models are vulnerable to image distortion. For successful deployment, it is important to identify distortion levels under which the model is usable i.e. its accuracy stays above a stipulated threshold. We refer to this problem as Model Assurance under Image Distortion, and f... | {
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2412.20277 | High-Performance Model Predictive Control for Quadcopters with Formal
Stability Guarantees | [
"eess.SY",
"cs.SY",
"math.OC"
] | In this paper, we present a novel cascade control structure with formal guarantees of uniform almost global asymptotic stability for the state tracking error dynamics of a quadcopter. The proposed approach features a model predictive control strategy for the outer loop, explicitly accounting for the non-zero total thru... | {
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2412.20279 | Deep Generalized Schr\"odinger Bridges: From Image Generation to Solving
Mean-Field Games | [
"stat.ML",
"cs.LG",
"math.OC"
] | Generalized Schr\"odinger Bridges (GSBs) are a fundamental mathematical framework used to analyze the most likely particle evolution based on the principle of least action including kinetic and potential energy. In parallel to their well-established presence in the theoretical realms of quantum mechanics and optimal tr... | {
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2412.20283 | Convex Data-Driven Contraction With Riemannian Metrics | [
"math.OC",
"cs.SY",
"eess.SY"
] | The growing complexity of dynamical systems and advances in data collection necessitates robust data-driven control strategies without explicit system identification and robust synthesis. Data-driven stability has been explored in linear and nonlinear systems, often by turning the problem into a linear or positive semi... | {
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2412.20289 | Causal Discovery on Dependent Binary Data | [
"cs.LG",
"stat.ME",
"stat.ML"
] | The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-world data, posing challenges to accurate structure learning. We propose a decorrelation-based approa... | {
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2412.20290 | Transformer-Based Contrastive Meta-Learning For Low-Resource
Generalizable Activity Recognition | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Deep learning has been widely adopted for human activity recognition (HAR) while generalizing a trained model across diverse users and scenarios remains challenging due to distribution shifts. The inherent low-resource challenge in HAR, i.e., collecting and labeling adequate human-involved data can be prohibitively cos... | {
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2412.20292 | An analytic theory of creativity in convolutional diffusion models | [
"cs.LG",
"cond-mat.dis-nn",
"cs.AI",
"q-bio.NC",
"stat.ML"
] | We obtain the first analytic, interpretable and predictive theory of creativity in convolutional diffusion models. Indeed, score-based diffusion models can generate highly creative images that lie far from their training data. But optimal score-matching theory suggests that these models should only be able to produce m... | {
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2412.20295 | Predicting Customer Lifetime Value Using Recurrent Neural Net | [
"stat.AP",
"cs.LG",
"stat.ML"
] | This paper introduces a recurrent neural network approach for predicting user lifetime value in Software as a Service (SaaS) applications. The approach accounts for three connected time dimensions. These dimensions are the user cohort (the date the user joined), user age-in-system (the time since the user joined the se... | {
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2412.20298 | An experimental study on fairness-aware machine learning for credit
scoring problem | [
"cs.LG",
"cs.CY",
"stat.ML"
] | Digitalization of credit scoring is an essential requirement for financial organizations and commercial banks, especially in the context of digital transformation. Machine learning techniques are commonly used to evaluate customers' creditworthiness. However, the predicted outcomes of machine learning models can be bia... | {
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2412.20299 | No Preference Left Behind: Group Distributional Preference Optimization | [
"cs.CL"
] | Preferences within a group of people are not uniform but follow a distribution. While existing alignment methods like Direct Preference Optimization (DPO) attempt to steer models to reflect human preferences, they struggle to capture the distributional pluralistic preferences within a group. These methods often skew to... | {
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2412.20301 | Distributed Hybrid Sketching for $\ell_2$-Embeddings | [
"math.NA",
"cs.DC",
"cs.IT",
"cs.NA",
"eess.SP",
"math.IT"
] | Linear algebraic operations are ubiquitous in engineering applications, and arise often in a variety of fields including statistical signal processing and machine learning. With contemporary large datasets, to perform linear algebraic methods and regression tasks, it is necessary to resort to both distributed computati... | {
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2412.20302 | EXAdam: The Power of Adaptive Cross-Moments | [
"cs.LG",
"cs.AI",
"math.OC"
] | This paper introduces EXAdam ($\textbf{EX}$tended $\textbf{Adam}$), a novel optimization algorithm that builds upon the widely-used Adam optimizer. EXAdam incorporates three key enhancements: (1) new debiasing terms for improved moment estimation, (2) a gradient-based acceleration mechanism for increased responsiveness... | {
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2412.20309 | Understanding the Impact of Confidence in Retrieval Augmented
Generation: A Case Study in the Medical Domain | [
"cs.CL"
] | Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries. This approach is widely applied in several fields by taking its advantage of injecting the most up-to-date information, and researchers are focusing... | {
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2412.20320 | Hybrid Feedback Control for Global Navigation with Locally Optimal
Obstacle Avoidance in n-Dimensional Spaces | [
"cs.RO",
"cs.SY",
"eess.SY"
] | We present a hybrid feedback control framework for autonomous robot navigation in n-dimensional Euclidean spaces cluttered with spherical obstacles. The proposed approach ensures safe navigation and global asymptotic stability (GAS) of the target location by dynamically switching between two operational modes: motion-t... | {
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2412.20321 | Hypergraph-Based Dynamic Graph Node Classification | [
"cs.SI",
"cs.AI"
] | Node classification on static graphs has achieved significant success, but achieving accurate node classification on dynamic graphs where node topology, attributes, and labels change over time has not been well addressed. Existing methods based on RNNs and self-attention only aggregate features of the same node across ... | {
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2412.20327 | Motion Transfer-Driven intra-class data augmentation for Finger Vein
Recognition | [
"cs.CV"
] | Finger vein recognition (FVR) has emerged as a secure biometric technique because of the confidentiality of vascular bio-information. Recently, deep learning-based FVR has gained increased popularity and achieved promising performance. However, the limited size of public vein datasets has caused overfitting issues and ... | {
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2412.20328 | Dual-Level Precision Edges Guided Multi-View Stereo with Accurate
Planarization | [
"cs.CV"
] | The reconstruction of low-textured areas is a prominent research focus in multi-view stereo (MVS). In recent years, traditional MVS methods have performed exceptionally well in reconstructing low-textured areas by constructing plane models. However, these methods often encounter issues such as crossing object boundarie... | {
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2412.20329 | Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement
Learning | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures. For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achieving optimal conformations with 25% fewer training episodes... | {
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2412.20330 | Zeroth-Order Methods for Nonconvex Stochastic Problems with
Decision-Dependent Distributions | [
"math.OC",
"cs.LG"
] | In this study, we consider an optimization problem with uncertainty dependent on decision variables, which has recently attracted attention due to its importance in machine learning and pricing applications. In this problem, the gradient of the objective function cannot be obtained explicitly because the decision-depen... | {
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2412.20331 | Mind the Data Gap: Bridging LLMs to Enterprise Data Integration | [
"cs.DB",
"cs.AI",
"cs.LG"
] | Leading large language models (LLMs) are trained on public data. However, most of the world's data is dark data that is not publicly accessible, mainly in the form of private organizational or enterprise data. We show that the performance of methods based on LLMs seriously degrades when tested on real-world enterprise ... | {
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2412.20337 | Contrastive Conditional Alignment based on Label Shift Calibration for
Imbalanced Domain Adaptation | [
"cs.CV"
] | Many existing unsupervised domain adaptation (UDA) methods primarily focus on covariate shift, limiting their effectiveness in imbalanced domain adaptation (IDA) where both covariate shift and label shift coexist. Recent IDA methods have achieved promising results based on self-training using target pseudo labels. Howe... | {
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2412.20338 | Exploiting Hybrid Policy in Reinforcement Learning for Interpretable
Temporal Logic Manipulation | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Reinforcement Learning (RL) based methods have been increasingly explored for robot learning. However, RL based methods often suffer from low sampling efficiency in the exploration phase, especially for long-horizon manipulation tasks, and generally neglect the semantic information from the task level, resulted in a de... | {
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2412.20340 | Distilling Desired Comments for Enhanced Code Review with Large Language
Models | [
"cs.SE",
"cs.AI"
] | There has been a growing interest in using Large Language Models (LLMs) for code review thanks to their proven proficiency in code comprehension. The primary objective of most review scenarios is to generate desired review comments (DRCs) that explicitly identify issues to trigger code fixes. However, existing LLM-base... | {
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2412.20341 | Asynchronous Federated Clustering with Unknown Number of Clusters | [
"cs.LG",
"cs.DC"
] | Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data provided by multiple clients while preserving their privacy. Most existing attempts learn cluster distributions at local clients, and then securely pass the desensitized information to the serv... | {
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2412.20345 | Deep Learning in Image Classification: Evaluating VGG19's Performance on
Complex Visual Data | [
"cs.CV",
"cs.LG"
] | This study aims to explore the automatic classification method of pneumonia X-ray images based on VGG19 deep convolutional neural network, and evaluate its application effect in pneumonia diagnosis by comparing with classic models such as SVM, XGBoost, MLP, and ResNet50. The experimental results show that VGG19 perform... | {
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2412.20350 | Safe Bayesian Optimization for the Control of High-Dimensional Embodied
Systems | [
"cs.LG",
"cs.RO"
] | Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such as the control of human or humanoid control, the high-dimensional parameter space adds complexity to the safe optimization effort. Current ... | {
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2412.20354 | Distributed Convex Optimization with State-Dependent (Social)
Interactions over Random Networks | [
"eess.SY",
"cs.SY"
] | This paper aims at distributed multi-agent convex optimization where the communications network among the agents are presented by a random sequence of possibly state-dependent weighted graphs. This is the first work to consider both random arbitrary communication networks and state-dependent interactions among agen... | {
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2412.20355 | Confidence Interval Construction and Conditional Variance Estimation
with Dense ReLU Networks | [
"stat.ML",
"cs.LG"
] | This paper addresses the problems of conditional variance estimation and confidence interval construction in nonparametric regression using dense networks with the Rectified Linear Unit (ReLU) activation function. We present a residual-based framework for conditional variance estimation, deriving nonasymptotic bounds f... | {
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2412.20357 | HindiLLM: Large Language Model for Hindi | [
"cs.CL",
"cs.AI"
] | The advancements in the Large Language Model (LLM) have helped in solving several problems related to language processing. Most of the researches have focused on the English language only, because of its popularity and abundance on the internet. However, a high-performance language model for Hindi and other Indic langu... | {
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2412.20359 | EmoReg: Directional Latent Vector Modeling for Emotional Intensity
Regularization in Diffusion-based Voice Conversion | [
"eess.AS",
"cs.AI",
"cs.MM",
"cs.SD"
] | The Emotional Voice Conversion (EVC) aims to convert the discrete emotional state from the source emotion to the target for a given speech utterance while preserving linguistic content. In this paper, we propose regularizing emotion intensity in the diffusion-based EVC framework to generate precise speech of the target... | {
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2412.20360 | Left-handed representation in top 100 male professional tennis players:
Multi-disciplinary perspectives | [
"cs.CY",
"cs.IR"
] | A commonly held opinion is that left-handed tennis players are overrepresented compared to the percentage of left-handers within the general population. This study provides the domain insights supported by data analysis that could help inform the decision of parents and coaches considering whether a child should start ... | {
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2412.20361 | Safe Multiagent Coordination via Entropic Exploration | [
"cs.MA",
"cs.AI",
"cs.RO"
] | Many real-world multiagent learning problems involve safety concerns. In these setups, typical safe reinforcement learning algorithms constrain agents' behavior, limiting exploration -- a crucial component for discovering effective cooperative multiagent behaviors. Moreover, the multiagent literature typically models i... | {
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2412.20362 | Slow and fast dynamics in measure functional differential equations with
state-dependent delays through averaging principles and applications to
extremum seeking | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper investigates a new class of equations called measure functional differential equations with state-dependent delays. We establish the existence and uniqueness of solutions and present a discussion concerning the appropriate phase space to define these equations. Also, we prove a version of periodic averaging ... | {
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2412.20363 | Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in
Crowded Video Scenes | [
"cs.CV",
"stat.AP"
] | Detecting anomalies in crowded video scenes is critical for public safety, enabling timely identification of potential threats. This study explores video anomaly detection within a Functional Data Analysis framework, focusing on the application of the Magnitude-Shape (MS) Plot. Autoencoders are used to learn and recons... | {
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} |
2412.20365 | Accelerated regularized learning in finite N-person games | [
"cs.GT",
"cs.LG",
"math.OC"
] | Motivated by the success of Nesterov's accelerated gradient algorithm for convex minimization problems, we examine whether it is possible to achieve similar performance gains in the context of online learning in games. To that end, we introduce a family of accelerated learning methods, which we call "follow the acceler... | {
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} |
2412.20366 | Introducing Semantic Capability in LinkedIn's Content Search Engine | [
"cs.IR"
] | In the past, most search queries issued to a search engine were short and simple. A keyword based search engine was able to answer such queries quite well. However, members are now developing the habit of issuing long and complex natural language queries. Answering such queries requires evolution of a search engine to ... | {
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} |
2412.20367 | Enhancing Code LLMs with Reinforcement Learning in Code Generation: A
Survey | [
"cs.SE",
"cs.CL"
] | With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of RL in code optimization and generation, highlighting its role in enhancing compil... | {
"Other": 1,
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} |
2412.20368 | Subconscious Robotic Imitation Learning | [
"cs.RO"
] | Although robotic imitation learning (RIL) is promising for embodied intelligent robots, existing RIL approaches rely on computationally intensive multi-model trajectory predictions, resulting in slow execution and limited real-time responsiveness. Instead, human beings subconscious can constantly process and store vast... | {
"Other": 0,
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} |
2412.20370 | Differential Evolution Integrated Hybrid Deep Learning Model for Object
Detection in Pre-made Dishes | [
"cs.CV"
] | With the continuous improvement of people's living standards and fast-paced working conditions, pre-made dishes are becoming increasingly popular among families and restaurants due to their advantages of time-saving, convenience, variety, cost-effectiveness, standard quality, etc. Object detection is a key technology f... | {
"Other": 0,
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} |
2412.20372 | LLM2: Let Large Language Models Harness System 2 Reasoning | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We posit that these limitations are rooted in the foundational autoregressive architecture of LLMs, which inherently lacks mechanisms for differentiating between desirable and u... | {
"Other": 0,
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} |
2412.20373 | A Deep Subgrouping Framework for Precision Drug Repurposing via
Emulating Clinical Trials on Real-world Patient Data | [
"cs.LG",
"cs.AI",
"stat.AP"
] | Drug repurposing identifies new therapeutic uses for existing drugs, reducing the time and costs compared to traditional de novo drug discovery. Most existing drug repurposing studies using real-world patient data often treat the entire population as homogeneous, ignoring the heterogeneity of treatment responses across... | {
"Other": 0,
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} |
2412.20374 | FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair
Bayesian Perturbation | [
"cs.CV",
"cs.LG"
] | Recent progress in generative AI, especially diffusion models, has demonstrated significant utility in text-to-image synthesis. Particularly in healthcare, these models offer immense potential in generating synthetic datasets and training medical students. However, despite these strong performances, it remains uncertai... | {
"Other": 0,
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} |
2412.20375 | Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes | [
"cs.LG",
"stat.ML"
] | Bayesian optimization is an effective technique for black-box optimization, but its applicability is typically limited to low-dimensional and small-budget problems due to the cubic complexity of computing the Gaussian process (GP) surrogate. While various approximate GP models have been employed to scale Bayesian optim... | {
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"cs.SY": 0
} |
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