id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.10580 | Gradient-Based Stochastic Extremum-Seeking Control for Multivariable
Systems with Distinct Input Delays | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper addresses the design and analysis of a multivariable gradient-based stochastic extremum-seeking control method for multi-input systems with arbitrary input delays. The approach accommodates systems with distinct time delays across input channels and achieves local exponential stability of the closed-loop sys... | {
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2411.10581 | On the Shortcut Learning in Multilingual Neural Machine Translation | [
"cs.CL",
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] | In this study, we revisit the commonly-cited off-target issue in multilingual neural machine translation (MNMT). By carefully designing experiments on different MNMT scenarios and models, we attribute the off-target issue to the overfitting of the shortcuts of (non-centric, centric) language mappings. Specifically, the... | {
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2411.10582 | Motion Diffusion-Guided 3D Global HMR from a Dynamic Camera | [
"cs.CV"
] | Motion capture technologies have transformed numerous fields, from the film and gaming industries to sports science and healthcare, by providing a tool to capture and analyze human movement in great detail. The holy grail in the topic of monocular global human mesh and motion reconstruction (GHMR) is to achieve accurac... | {
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2411.10585 | Autonomous Sensor Exchange and Calibration for Cornstalk Nitrate
Monitoring Robot | [
"cs.RO"
] | Interactive sensors are an important component of robotic systems but often require manual replacement due to wear and tear. Automating this process can enhance system autonomy and facilitate long-term deployment. We developed an autonomous sensor exchange and calibration system for an agriculture crop monitoring robot... | {
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2411.10588 | A dataset of questions on decision-theoretic reasoning in Newcomb-like
problems | [
"cs.CL",
"cs.AI"
] | We introduce a dataset of natural-language questions in the decision theory of so-called Newcomb-like problems. Newcomb-like problems include, for instance, decision problems in which an agent interacts with a similar other agent, and thus has to reason about the fact that the other agent will likely reason in similar ... | {
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2411.10591 | Creation and Evaluation of a Food Product Image Dataset for Product
Property Extraction | [
"cs.CV",
"cs.LG"
] | The enormous progress in the field of artificial intelligence (AI) enables retail companies to automate their processes and thus to save costs. Thereby, many AI-based automation approaches are based on machine learning and computer vision. The realization of such approaches requires high-quality training data. In this ... | {
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2411.10592 | A Systematic LMI Approach to Design Multivariable Sliding Mode
Controllers | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper deals with sliding mode control for multivariable polytopic uncertain systems. We provide systematic procedures to design variable structure controllers (VSCs) and unit-vector controllers (UVCs). Based on suitable representations for the closed-loop system, we derive sufficient conditions in the form of line... | {
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2411.10595 | FedAli: Personalized Federated Learning with Aligned Prototypes through
Optimal Transport | [
"cs.LG",
"cs.CR",
"cs.CV"
] | Federated Learning (FL) enables collaborative, personalized model training across multiple devices without sharing raw data, making it ideal for pervasive computing applications that optimize user-centric performances in diverse environments. However, data heterogeneity among clients poses a significant challenge, lead... | {
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2411.10596 | A minimalistic representation model for head direction system | [
"q-bio.NC",
"cs.AI",
"cs.CV",
"stat.ML"
] | We present a minimalistic representation model for the head direction (HD) system, aiming to learn a high-dimensional representation of head direction that captures essential properties of HD cells. Our model is a representation of rotation group $U(1)$, and we study both the fully connected version and convolutional v... | {
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2411.10599 | Generating Energy-efficient code with LLMs | [
"cs.SE",
"cs.AI"
] | The increasing electricity demands of personal computers, communication networks, and data centers contribute to higher atmospheric greenhouse gas emissions, which in turn lead to global warming and climate change. Therefore the energy consumption of code must be minimized. Code can be generated by large language model... | {
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2411.10601 | Learning Quantitative Automata Modulo Theories | [
"cs.FL",
"cs.LG",
"cs.LO"
] | Quantitative automata are useful representations for numerous applications, including modeling probability distributions over sequences to Markov chains and reward machines. Actively learning such automata typically occurs using explicitly gathered input-output examples under adaptations of the L-star algorithm. Howeve... | {
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2411.10603 | A Novel MLLM-based Approach for Autonomous Driving in Different Weather
Conditions | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Autonomous driving (AD) technology promises to revolutionize daily transportation by making it safer, more efficient, and more comfortable. Their role in reducing traffic accidents and improving mobility will be vital to the future of intelligent transportation systems. Autonomous driving in harsh environmental conditi... | {
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2411.10606 | AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient
and Instant Deployment | [
"cs.LG",
"cs.AI"
] | Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronou... | {
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2411.10609 | Labeled Datasets for Research on Information Operations | [
"cs.CY",
"cs.SI"
] | Social media platforms have become a hub for political activities and discussions, democratizing participation in these endeavors. However, they have also become an incubator for manipulation campaigns, like information operations (IOs). Some social media platforms have released datasets related to such IOs originating... | {
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2411.10613 | Being Considerate as a Pathway Towards Pluralistic Alignment for Agentic
AI | [
"cs.AI",
"cs.CY",
"cs.LG"
] | Pluralistic alignment is concerned with ensuring that an AI system's objectives and behaviors are in harmony with the diversity of human values and perspectives. In this paper we study the notion of pluralistic alignment in the context of agentic AI, and in particular in the context of an agent that is trying to learn ... | {
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2411.10614 | To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling | [
"cs.CR",
"cs.LG"
] | Differentially Private Stochastic Gradient Descent (DP-SGD) is a popular method for training machine learning models with formal Differential Privacy (DP) guarantees. As DP-SGD processes the training data in batches, it uses Poisson sub-sampling to select batches at each step. However, due to computational and compatib... | {
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2411.10616 | Voxel-Aggregated Feature Synthesis: Efficient Dense Mapping for
Simulated 3D Reasoning | [
"cs.CV"
] | We address the issue of the exploding computational requirements of recent State-of-the-art (SOTA) open set multimodel 3D mapping (dense 3D mapping) algorithms and present Voxel-Aggregated Feature Synthesis (VAFS), a novel approach to dense 3D mapping in simulation. Dense 3D mapping involves segmenting and embedding se... | {
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2411.10617 | Attraction-Repulsion Swarming: A Generalized Framework of t-SNE via
Force Normalization and Tunable Interactions | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.CA",
"math.DS",
"math.NA",
"stat.ML"
] | We propose a new method for data visualization based on attraction-repulsion swarming (ARS) dynamics, which we call ARS visualization. ARS is a generalized framework that is based on viewing the t-distributed stochastic neighbor embedding (t-SNE) visualization technique as a swarm of interacting agents driven by attrac... | {
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2411.10618 | D-Flow: Multi-modality Flow Matching for D-peptide Design | [
"cs.CE"
] | Proteins play crucial roles in biological processes, with therapeutic peptides emerging as promising pharmaceutical agents. They allow new possibilities to leverage target binding sites that were previously undruggable. While deep learning (DL) has advanced peptide discovery, generating D-proteins composed of D-amino a... | {
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2411.10619 | Electrical Load Forecasting in Smart Grid: A Personalized Federated
Learning Approach | [
"cs.LG",
"eess.SP"
] | Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) are used to record household energy consumption. Traditional machine learning (ML) methods are often employed for load forecasting but require ... | {
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2411.10622 | KAT to KANs: A Review of Kolmogorov-Arnold Networks and the Neural Leap
Forward | [
"cs.LG",
"cs.NE",
"stat.ML"
] | The curse of dimensionality poses a significant challenge to modern multilayer perceptron-based architectures, often causing performance stagnation and scalability issues. Addressing this limitation typically requires vast amounts of data. In contrast, Kolmogorov-Arnold Networks have gained attention in the machine lea... | {
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2411.10624 | Weak Permission is not Well-Founded, Grounded and Stable | [
"cs.LO",
"cs.AI"
] | We consider the notion of weak permission as the failure to conclude that the opposite obligation. We investigate the issue from the point of non-monotonic reasoning, specifically logic programming and structured argumentation, and we show that it is not possible to capture weak permission in the presence of deontic co... | {
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2411.10627 | Is thermography a viable solution for detecting pressure injuries in
dark skin patients? | [
"cs.CV",
"cs.AI"
] | Pressure injury (PI) detection is challenging, especially in dark skin tones, due to the unreliability of visual inspection. Thermography has been suggested as a viable alternative as temperature differences in the skin can indicate impending tissue damage. Although deep learning models have demonstrated considerable p... | {
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2411.10629 | Leveraging large language models for efficient representation learning
for entity resolution | [
"cs.CL",
"cs.AI"
] | In this paper, the authors propose TriBERTa, a supervised entity resolution system that utilizes a pre-trained large language model and a triplet loss function to learn representations for entity matching. The system consists of two steps: first, name entity records are fed into a Sentence Bidirectional Encoder Represe... | {
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2411.10632 | Quantifying community evolution in temporal networks | [
"cs.SI"
] | When we detect communities in temporal networks it is important to ask questions about how they change in time. Normalised Mutual Information (NMI) has been used to measure the similarity of communities when the nodes on a network do not change. We propose two extensions namely Union-Normalised Mutual Information (UNMI... | {
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2411.10633 | A Geometric Perspective on the Injective Norm of Sums of Random Tensors | [
"math.PR",
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | Matrix concentration inequalities, intimately connected to the Non-Commutative Khintchine inequality, have been an important tool in both applied and pure mathematics. We study tensor versions of these inequalities, and establish non-asymptotic inequalities for the $\ell_p$ injective norm of random tensors with correla... | {
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2411.10634 | Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts
on Tabular Data | [
"cs.LG",
"stat.ML"
] | While most ML models expect independent and identically distributed data, this assumption is often violated in real-world scenarios due to distribution shifts, resulting in the degradation of machine learning model performance. Until now, no tabular method has consistently outperformed classical supervised learning, wh... | {
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2411.10636 | Gender Bias Mitigation for Bangla Classification Tasks | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In this study, we investigate gender bias in Bangla pretrained language models, a largely under explored area in low-resource languages. To assess this bias, we applied gender-name swapping techniques to existing datasets, creating four manually annotated, task-specific datasets for sentiment analysis, toxicity detecti... | {
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2411.10639 | MTA: Multimodal Task Alignment for BEV Perception and Captioning | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Bird's eye view (BEV)-based 3D perception plays a crucial role in autonomous driving applications. The rise of large language models has spurred interest in BEV-based captioning to understand object behavior in the surrounding environment. However, existing approaches treat perception and captioning as separate tasks, ... | {
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2411.10640 | BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large
Language Models on Mobile Devices | [
"cs.CV",
"cs.CL"
] | The emergence and growing popularity of multimodal large language models (MLLMs) have significant potential to enhance various aspects of daily life, from improving communication to facilitating learning and problem-solving. Mobile phones, as essential daily companions, represent the most effective and accessible deplo... | {
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2411.10645 | Patient-Specific Models of Treatment Effects Explain Heterogeneity in
Tuberculosis | [
"cs.LG",
"stat.ML"
] | Tuberculosis (TB) is a major global health challenge, and is compounded by co-morbidities such as HIV, diabetes, and anemia, which complicate treatment outcomes and contribute to heterogeneous patient responses. Traditional models of TB often overlook this heterogeneity by focusing on broad, pre-defined patient groups,... | {
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2411.10649 | Deep Loss Convexification for Learning Iterative Models | [
"cs.CV"
] | Iterative methods such as iterative closest point (ICP) for point cloud registration often suffer from bad local optimality (e.g. saddle points), due to the nature of nonconvex optimization. To address this fundamental challenge, in this paper we propose learning to form the loss landscape of a deep iterative method w.... | {
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2411.10650 | Deep Learning-Based Image Compression for Wireless Communications:
Impacts on Reliability,Throughput, and Latency | [
"eess.SP",
"cs.LG",
"cs.NI"
] | In wireless communications, efficient image transmission must balance reliability, throughput, and latency, especially under dynamic channel conditions. This paper presents an adaptive and progressive pipeline for learned image compression (LIC)-based architectures tailored to such environments. We investigate two stat... | {
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2411.10651 | Understanding Learning with Sliced-Wasserstein Requires Rethinking
Informative Slices | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.AP",
"stat.CO",
"stat.ML"
] | The practical applications of Wasserstein distances (WDs) are constrained by their sample and computational complexities. Sliced-Wasserstein distances (SWDs) provide a workaround by projecting distributions onto one-dimensional subspaces, leveraging the more efficient, closed-form WDs for one-dimensional distributions.... | {
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2411.10654 | Pluralistic Alignment Over Time | [
"cs.AI",
"cs.CY",
"cs.LG"
] | If an AI system makes decisions over time, how should we evaluate how aligned it is with a group of stakeholders (who may have conflicting values and preferences)? In this position paper, we advocate for consideration of temporal aspects including stakeholders' changing levels of satisfaction and their possibly tempora... | {
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2411.10661 | Enhancing PTSD Outcome Prediction with Ensemble Models in Disaster
Contexts | [
"cs.LG",
"cs.CV"
] | Post-traumatic stress disorder (PTSD) is a significant mental health challenge that affects individuals exposed to traumatic events. Early detection and effective intervention for PTSD are crucial, as it can lead to long-term psychological distress if untreated. Accurate detection of PTSD is essential for timely and ta... | {
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2411.10666 | SAM Decoding: Speculative Decoding via Suffix Automaton | [
"cs.CL",
"cs.AI"
] | Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration. Retrieval-based SD methods, one kind of model-free method, have yielded promising speedup, but they often rely on incomplete retrieval resources, inefficient retrieval methods, and are constrained to certa... | {
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2411.10668 | Segmentation of Ink and Parchment in Dead Sea Scroll Fragments | [
"cs.CV",
"cs.DL"
] | The discovery of the Dead Sea Scrolls over 60 years ago is widely regarded as one of the greatest archaeological breakthroughs in modern history. Recent study of the scrolls presents ongoing computational challenges, including determining the provenance of fragments, clustering fragments based on their degree of simila... | {
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2411.10669 | Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of
Experts | [
"cs.CV"
] | As the research of Multimodal Large Language Models (MLLMs) becomes popular, an advancing MLLM model is typically required to handle various textual and visual tasks (e.g., VQA, Detection, OCR, and ChartQA) simultaneously for real-world applications. However, due to the significant differences in representation and dis... | {
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2411.10670 | IntentGPT: Few-shot Intent Discovery with Large Language Models | [
"cs.CL"
] | In today's digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to virtual assistants. In these dialogues, it is important to identify user's goals automatically to resolve their needs promptly. This has necessitated the integration of models that perform In... | {
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2411.10673 | How to Defend Against Large-scale Model Poisoning Attacks in Federated
Learning: A Vertical Solution | [
"cs.LG",
"cs.CR"
] | Federated learning (FL) is vulnerable to model poisoning attacks due to its distributed nature. The current defenses start from all user gradients (model updates) in each communication round and solve for the optimal aggregation gradients (horizontal solution). This horizontal solution will completely fail when facing ... | {
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2411.10676 | Exploring Feature-based Knowledge Distillation for Recommender System: A
Frequency Perspective | [
"cs.IR",
"cs.AI",
"cs.LG"
] | In this paper, we analyze the feature-based knowledge distillation for recommendation from the frequency perspective. By defining knowledge as different frequency components of the features, we theoretically demonstrate that regular feature-based knowledge distillation is equivalent to equally minimizing losses on all ... | {
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2411.10679 | SPDFusion: An Infrared and Visible Image Fusion Network Based on a
Non-Euclidean Representation of Riemannian Manifolds | [
"cs.CV"
] | Euclidean representation learning methods have achieved commendable results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. However, data collected from a realistic scene usually have a non-Euclidean structure, where Euclidean metric might be limited in representi... | {
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2411.10681 | Structured Dialogue System for Mental Health: An LLM Chatbot Leveraging
the PM+ Guidelines | [
"cs.CL"
] | The Structured Dialogue System, referred to as SuDoSys, is an innovative Large Language Model (LLM)-based chatbot designed to provide psychological counseling. SuDoSys leverages the World Health Organization (WHO)'s Problem Management Plus (PM+) guidelines to deliver stage-aware multi-turn dialogues. Existing methods f... | {
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2411.10682 | Underwater Image Enhancement with Cascaded Contrastive Learning | [
"cs.CV"
] | Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Due to the application of deep learning, current UIE methods have made significant progress. Most of the existing deep learning-based UIE methods follow a si... | {
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2411.10684 | HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal
Automatic Diagnosis | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Chest X-ray imaging is a widely accessible and non-invasive diagnostic tool for detecting thoracic abnormalities. While numerous AI models assist radiologists in interpreting these images, most overlook patients' historical data. To bridge this gap, we introduce Temporal MIMIC dataset, which integrates five years of pa... | {
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2411.10685 | From Prototypes to General Distributions: An Efficient Curriculum for
Masked Image Modeling | [
"cs.CV"
] | Masked Image Modeling (MIM) has emerged as a powerful self-supervised learning paradigm for visual representation learning, enabling models to acquire rich visual representations by predicting masked portions of images from their visible regions. While this approach has shown promising results, we hypothesize that its ... | {
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2411.10686 | MaskMedPaint: Masked Medical Image Inpainting with Diffusion Models for
Mitigation of Spurious Correlations | [
"cs.CV",
"cs.LG"
] | Spurious features associated with class labels can lead image classifiers to rely on shortcuts that don't generalize well to new domains. This is especially problematic in medical settings, where biased models fail when applied to different hospitals or systems. In such cases, data-driven methods to reduce spurious cor... | {
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2411.10692 | DEBUG-HD: Debugging TinyML models on-device using Hyper-Dimensional
computing | [
"cs.LG",
"cs.AI",
"cs.NE"
] | TinyML models often operate in remote, dynamic environments without cloud connectivity, making them prone to failures. Ensuring reliability in such scenarios requires not only detecting model failures but also identifying their root causes. However, transient failures, privacy concerns, and the safety-critical nature o... | {
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2411.10693 | Multi-perspective Contrastive Logit Distillation | [
"cs.CV"
] | We propose a novel and efficient logit distillation method, Multi-perspective Contrastive Logit Distillation (MCLD), which leverages contrastive learning to distill logits from multiple perspectives in knowledge distillation. Recent research on logit distillation has primarily focused on maximizing the information lear... | {
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2411.10695 | Series Expansion of Probability of Correct Selection for Improved Finite
Budget Allocation in Ranking and Selection | [
"stat.ML",
"cs.LG",
"math.OC"
] | This paper addresses the challenge of improving finite sample performance in Ranking and Selection by developing a Bahadur-Rao type expansion for the Probability of Correct Selection (PCS). While traditional large deviations approximations captures PCS behavior in the asymptotic regime, they can lack precision in finit... | {
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2411.10696 | HELENE: Hessian Layer-wise Clipping and Gradient Annealing for
Accelerating Fine-tuning LLM with Zeroth-order Optimization | [
"cs.LG",
"cs.AI"
] | Fine-tuning large language models (LLMs) poses significant memory challenges, as the back-propagation process demands extensive resources, especially with growing model sizes. Recent work, MeZO, addresses this issue using a zeroth-order (ZO) optimization method, which reduces memory consumption by matching the usage to... | {
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2411.10697 | Language Model Evolutionary Algorithms for Recommender Systems:
Benchmarks and Algorithm Comparisons | [
"cs.NE"
] | In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), enabling them to tackle optimization problems involving structured language or program code. Although... | {
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2411.10699 | Hierarchical Adaptive Motion Planning with Nonlinear Model Predictive
Control for Safety-Critical Collaborative Loco-Manipulation | [
"cs.RO"
] | As legged robots take on roles in industrial and autonomous construction, collaborative loco-manipulation is crucial for handling large and heavy objects that exceed the capabilities of a single robot. However, ensuring the safety of these multi-robot tasks is essential to prevent accidents and guarantee reliable opera... | {
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2411.10701 | Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised
Out-of-Distribution Detection | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for developing a safe real-world machine learning system. Current reconstruction-based methods provide a good alternative approach by measuring the... | {
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2411.10702 | Wireless Resource Allocation with Collaborative Distributed and
Centralized DRL under Control Channel Attacks | [
"cs.IT",
"cs.LG",
"cs.SY",
"eess.SP",
"eess.SY",
"math.IT"
] | In this paper, we consider a wireless resource allocation problem in a cyber-physical system (CPS) where the control channel, carrying resource allocation commands, is subjected to denial-of-service (DoS) attacks. We propose a novel concept of collaborative distributed and centralized (CDC) resource allocation to effec... | {
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2411.10703 | Hybrid Attention Model Using Feature Decomposition and Knowledge
Distillation for Glucose Forecasting | [
"cs.LG",
"eess.SP"
] | The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated interventions to prevent devastating chronic complications that arise from poor glucose control. However... | {
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2411.10705 | Poster: Reliable 3D Reconstruction for Ad-hoc Edge Implementations | [
"cs.CV"
] | Ad-hoc edge deployments to support real-time complex video processing applications such as, multi-view 3D reconstruction often suffer from spatio-temporal system disruptions that greatly impact reconstruction quality. In this poster paper, we present a novel portfolio theory-inspired edge resource management strategy t... | {
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2411.10708 | AllRestorer: All-in-One Transformer for Image Restoration under
Composite Degradations | [
"cs.CV"
] | Image restoration models often face the simultaneous interaction of multiple degradations in real-world scenarios. Existing approaches typically handle single or composite degradations based on scene descriptors derived from text or image embeddings. However, due to the varying proportions of different degradations wit... | {
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2411.10709 | Diagnostic Text-guided Representation Learning in Hierarchical
Classification for Pathological Whole Slide Image | [
"cs.CV"
] | With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annotation, current research primarily focuses on representation learning with slide-... | {
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2411.10713 | A Regularized LSTM Method for Detecting Fake News Articles | [
"cs.LG",
"cs.CL",
"cs.CY"
] | Nowadays, the rapid diffusion of fake news poses a significant problem, as it can spread misinformation and confusion. This paper aims to develop an advanced machine learning solution for detecting fake news articles. Leveraging a comprehensive dataset of news articles, including 23,502 fake news articles and 21,417 ac... | {
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2411.10715 | EVT: Efficient View Transformation for Multi-Modal 3D Object Detection | [
"cs.CV"
] | Multi-modal sensor fusion in bird's-eye-view (BEV) representation has become the leading approach in 3D object detection. However, existing methods often rely on depth estimators or transformer encoders for view transformation, incurring substantial computational overhead. Furthermore, the lack of precise geometric cor... | {
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2411.10716 | FlowScope: Enhancing Decision Making by Time Series Forecasting based on
Prediction Optimization using HybridFlow Forecast Framework | [
"cs.LG",
"cs.CE",
"eess.SP"
] | Time series forecasting is crucial in several sectors, such as meteorology, retail, healthcare, and finance. Accurately forecasting future trends and patterns is crucial for strategic planning and making well-informed decisions. In this case, it is crucial to include many forecasting methodologies. The strengths of Aut... | {
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2411.10720 | Multi Scale Graph Neural Network for Alzheimer's Disease | [
"cs.LG",
"q-bio.NC",
"q-bio.QM"
] | Alzheimer's disease (AD) is a complex, progressive neurodegenerative disorder characterized by extracellular A\b{eta} plaques, neurofibrillary tau tangles, glial activation, and neuronal degeneration, involving multiple cell types and pathways. Current models often overlook the cellular context of these pathways. To ad... | {
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2411.10722 | DGS-SLAM: Gaussian Splatting SLAM in Dynamic Environment | [
"cs.RO"
] | We introduce Dynamic Gaussian Splatting SLAM (DGS-SLAM), the first dynamic SLAM framework built on the foundation of Gaussian Splatting. While recent advancements in dense SLAM have leveraged Gaussian Splatting to enhance scene representation, most approaches assume a static environment, making them vulnerable to photo... | {
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2411.10724 | HJ-Ky-0.1: an Evaluation Dataset for Kyrgyz Word Embeddings | [
"cs.CL"
] | One of the key tasks in modern applied computational linguistics is constructing word vector representations (word embeddings), which are widely used to address natural language processing tasks such as sentiment analysis, information extraction, and more. To choose an appropriate method for generating these word embed... | {
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2411.10727 | Self-Triggered Control in Artificial Pancreas | [
"eess.SY",
"cs.SY"
] | The management of type 1 diabetes has been revolutionized by the artificial pancreas system (APS), which automates insulin delivery based on continuous glucose monitor (CGM). While conventional closed-loop systems rely on CGM data, which leads to higher energy consumption at the sensors and increased data redundancy in... | {
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2411.10729 | On-device Anomaly Detection in Conveyor Belt Operations | [
"cs.LG",
"cs.CE",
"eess.SP"
] | Mining 4.0 leverages advancements in automation, digitalization, and interconnected technologies from Industry 4.0 to address the unique challenges of the mining sector, enhancing efficiency, safety, and sustainability. Conveyor belts are crucial in mining operations by enabling the continuous and efficient movement of... | {
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2411.10730 | Comparison of Multilingual and Bilingual Models for Satirical News
Detection of Arabic and English | [
"cs.CL",
"cs.CR"
] | Satirical news is real news combined with a humorous comment or exaggerated content, and it often mimics the format and style of real news. However, satirical news is often misunderstood as misinformation, especially by individuals from different cultural and social backgrounds. This research addresses the challenge of... | {
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2411.10739 | A Wearable Gait Monitoring System for 17 Gait Parameters Based on
Computer Vision | [
"eess.SY",
"cs.CV",
"cs.SY",
"eess.SP"
] | We developed a shoe-mounted gait monitoring system capable of tracking up to 17 gait parameters, including gait length, step time, stride velocity, and others. The system employs a stereo camera mounted on one shoe to track a marker placed on the opposite shoe, enabling the estimation of spatial gait parameters. Additi... | {
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2411.10741 | MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map | [
"cs.LG",
"cs.AI"
] | Various linear complexity models, such as Linear Transformer (LinFormer), State Space Model (SSM), and Linear RNN (LinRNN), have been proposed to replace the conventional softmax attention in Transformer structures. However, the optimal design of these linear models is still an open question. In this work, we attempt t... | {
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2411.10742 | It Takes Two: Accurate Gait Recognition in the Wild via
Cross-granularity Alignment | [
"cs.CV"
] | Existing studies for gait recognition primarily utilized sequences of either binary silhouette or human parsing to encode the shapes and dynamics of persons during walking. Silhouettes exhibit accurate segmentation quality and robustness to environmental variations, but their low information entropy may result in sub-o... | {
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2411.10743 | Never eat a Pigeon with a Pumpkin: a model for the emergence and
fixation of unsupported beliefs | [
"physics.soc-ph",
"cs.SI"
] | A popular poster from Myanmar lists food pairings that should be avoided, sometimes at all costs. Coconut and honey taken together, for example, are believed to cause nausea, while pork and curdled milk will induce diarrhea. Worst of all, according to the poster, many seemingly innocuous combinations that include jelly... | {
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2411.10744 | Digital-Analog Quantum Machine Learning | [
"quant-ph",
"cs.AI"
] | Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this ... | {
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2411.10745 | TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action
Recognition | [
"cs.CV"
] | We firstly present a diffusion-based action recognition with zero-shot learning for skeleton inputs. In zero-shot skeleton-based action recognition, aligning skeleton features with the text features of action labels is essential for accurately predicting unseen actions. Previous methods focus on direct alignment betwee... | {
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2411.10746 | LTCXNet: Advancing Chest X-Ray Analysis with Solutions for Long-Tailed
Multi-Label Classification and Fairness Challenges | [
"cs.CV",
"cs.AI"
] | Chest X-rays (CXRs) often display various diseases with disparate class frequencies, leading to a long-tailed, multi-label data distribution. In response to this challenge, we explore the Pruned MIMIC-CXR-LT dataset, a curated collection derived from the MIMIC-CXR dataset, specifically designed to represent a long-tail... | {
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2411.10752 | Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis
in Breast Cancer Sections | [
"eess.IV",
"cs.CV"
] | Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images (WSIs). This development enables comprehensive digital reviews by pathologists and accelerates AI-driven diagnostic support for WSI analysi... | {
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2411.10753 | Chain-of-Programming (CoP) : Empowering Large Language Models for
Geospatial Code Generation | [
"cs.SE",
"cs.AI",
"cs.CL"
] | With the rapid growth of interdisciplinary demands for geospatial modeling and the rise of large language models (LLMs), geospatial code generation technology has seen significant advancements. However, existing LLMs often face challenges in the geospatial code generation process due to incomplete or unclear user requi... | {
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2411.10754 | Integrated Machine Learning and Survival Analysis Modeling for Enhanced
Chronic Kidney Disease Risk Stratification | [
"cs.LG",
"cs.AI",
"stat.CO",
"stat.ML"
] | Chronic kidney disease (CKD) is a significant public health challenge, often progressing to end-stage renal disease (ESRD) if not detected and managed early. Early intervention, warranted by silent disease progression, can significantly reduce associated morbidity, mortality, and financial burden. In this study, we pro... | {
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2411.10755 | Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower
Back Pain Patients | [
"eess.IV",
"cs.CV",
"cs.LG"
] | This study introduces a diffusion-based framework for robust and accurate segmenton of vertebrae, intervertebral discs (IVDs), and spinal canal from Magnetic Resonance Imaging~(MRI) scans of patients with low back pain (LBP), regardless of whether the scans are T1w or T2-weighted. The results showed that SpineSegDiff a... | {
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2411.10760 | Experimental study of fish-like bodies with passive tail and tunable
stiffness | [
"physics.flu-dyn",
"cs.RO"
] | Scombrid fishes and tuna are efficient swimmers capable of maximizing performance to escape predators and save energy during long journeys. A key aspect in achieving these goals is the flexibility of the tail, which the fish optimizes during swimming. Though, the robotic counterparts, although highly efficient, have pa... | {
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2411.10761 | Can Generic LLMs Help Analyze Child-adult Interactions Involving
Children with Autism in Clinical Observation? | [
"cs.CL"
] | Large Language Models (LLMs) have shown significant potential in understanding human communication and interaction. However, their performance in the domain of child-inclusive interactions, including in clinical settings, remains less explored. In this work, we evaluate generic LLMs' ability to analyze child-adult dyad... | {
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2411.10764 | ML$^2$Tuner: Efficient Code Tuning via Multi-Level Machine Learning
Models | [
"cs.LG"
] | The increasing complexity of deep learning models necessitates specialized hardware and software optimizations, particularly for deep learning accelerators. Existing autotuning methods often suffer from prolonged tuning times due to profiling invalid configurations, which can cause runtime errors. We introduce ML$^2$Tu... | {
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2411.10765 | Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using
Enhanced Long Short-Term Memory Variational Autoencoder | [
"cs.LG",
"eess.SP"
] | As core thermal power generation equipment, steam turbines incur significant expenses and adverse effects on operation when facing interruptions like downtime, maintenance, and damage. Accurate anomaly detection is the prerequisite for ensuring the safe and stable operation of steam turbines. However, challenges in ste... | {
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2411.10768 | Building Interpretable Climate Emulators for Economics | [
"econ.EM",
"cs.CE",
"cs.LG"
] | This paper presents a framework for developing efficient and interpretable carbon-cycle emulators (CCEs) as part of climate emulators in Integrated Assessment Models, enabling economists to custom-build CCEs accurately calibrated to advanced climate science. We propose a generalized multi-reservoir linear box-model CCE... | {
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2411.10769 | Demonstrating Remote Synchronization: An Experimental Approach with
Nonlinear Oscillators | [
"eess.SY",
"cs.SY",
"nlin.CD"
] | This study investigates remote synchronization in arbitrary network clusters of coupled nonlinear oscillators, a phenomenon inspired by neural synchronization in the brain. Employing a multi-faceted approach encompassing analytical, numerical, and experimental methodologies, we leverage the Master Stability Function (M... | {
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2411.10772 | MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption
of Independent Pixels | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG",
"stat.ML"
] | We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential to robustly and repeatably measure biologically-relevant tissue maps that strongly relate to underlying microstructure. Quantitative maps ar... | {
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2411.10773 | An End-to-End Real-World Camera Imaging Pipeline | [
"eess.IV",
"cs.CV"
] | Recent advances in neural camera imaging pipelines have demonstrated notable progress. Nevertheless, the real-world imaging pipeline still faces challenges including the lack of joint optimization in system components, computational redundancies, and optical distortions such as lens shading.In light of this, we propose... | {
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2411.10775 | Beyond Feature Mapping GAP: Integrating Real HDRTV Priors for Superior
SDRTV-to-HDRTV Conversion | [
"eess.IV",
"cs.CV",
"cs.MM"
] | The rise of HDR-WCG display devices has highlighted the need to convert SDRTV to HDRTV, as most video sources are still in SDR. Existing methods primarily focus on designing neural networks to learn a single-style mapping from SDRTV to HDRTV. However, the limited information in SDRTV and the diversity of styles in real... | {
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2411.10781 | Bag of Design Choices for Inference of High-Resolution Masked Generative
Transformer | [
"cs.CV",
"cs.LG"
] | Text-to-image diffusion models (DMs) develop at an unprecedented pace, supported by thorough theoretical exploration and empirical analysis. Unfortunately, the discrepancy between DMs and autoregressive models (ARMs) complicates the path toward achieving the goal of unified vision and language generation. Recently, the... | {
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} |
2411.10784 | On Reductions and Representations of Learning Problems in Euclidean
Spaces | [
"cs.LG",
"stat.ML"
] | Many practical prediction algorithms represent inputs in Euclidean space and replace the discrete 0/1 classification loss with a real-valued surrogate loss, effectively reducing classification tasks to stochastic optimization. In this paper, we investigate the expressivity of such reductions in terms of key resources, ... | {
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} |
2411.10788 | C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation
with Confidence-Guided Reliable Object Generation | [
"cs.CV",
"eess.IV"
] | Synthetic Aperture Radar (SAR) imagery provides robust environmental and temporal coverage (e.g., during clouds, seasons, day-night cycles), yet its noise and unique structural patterns pose interpretation challenges, especially for non-experts. SAR-to-EO (Electro-Optical) image translation (SET) has emerged to make SA... | {
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} |
2411.10789 | Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional
Prompts | [
"cs.CV"
] | Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy is challenging, as radiological images often feature subtle lesions and intricate structures. Existing systems often fall short, largely due to their relia... | {
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} |
2411.10794 | Going Beyond Conventional OOD Detection | [
"cs.CV",
"cs.LG"
] | Out-of-distribution (OOD) detection is critical to ensure the safe deployment of deep learning models in critical applications. Deep learning models can often misidentify OOD samples as in-distribution (ID) samples. This vulnerability worsens in the presence of spurious correlation in the training set. Likewise, in fin... | {
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} |
2411.10798 | Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World
Super-Resolution | [
"eess.IV",
"cs.CV"
] | Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus on generating details from the LR RGB domain, often leading to a lack of richness or fidelity in fine details. In this paper... | {
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} |
2411.10800 | Test-time Conditional Text-to-Image Synthesis Using Diffusion Models | [
"cs.CV"
] | We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introduce new trainable parameters, leading to deployment inflexibility due to the need for training. To address this gap in the current literatur... | {
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} |
2411.10803 | Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large
Language Model | [
"cs.CV"
] | The vision tokens in multimodal large language models usually exhibit significant spatial and temporal redundancy and take up most of the input tokens, which harms their inference efficiency. To solve this problem, some recent works were introduced to drop the unimportant tokens during inference where the importance of... | {
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} |
2411.10805 | Existence of $\epsilon$-Nash Equilibria in Nonzero-Sum Borel Stochastic
Games and Equilibria of Quantized Models | [
"eess.SY",
"cs.SY"
] | Establishing the existence of exact or near Markov or stationary perfect Nash equilibria in nonzero-sum Markov games over Borel spaces remains a challenging problem, with few positive results to date. In this paper, we establish the existence of approximate Markov and stationary Nash equilibria for nonzero-sum stochast... | {
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} |
2411.10809 | Stable Continual Reinforcement Learning via Diffusion-based Trajectory
Replay | [
"cs.LG"
] | Given the inherent non-stationarity prevalent in real-world applications, continual Reinforcement Learning (RL) aims to equip the agent with the capability to address a series of sequentially presented decision-making tasks. Within this problem setting, a pivotal challenge revolves around \textit{catastrophic forgettin... | {
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} |
2411.10813 | Information Anxiety in Large Language Models | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated strong performance as knowledge repositories, enabling models to understand user queries and generate accurate and context-aware responses. Extensive evaluation setups have corroborated the positive correlation between the retrieval capability of LLMs and the frequency of ... | {
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} |
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