id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.12925 | Loss-to-Loss Prediction: Scaling Laws for All Datasets | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] | While scaling laws provide a reliable methodology for predicting train loss across compute scales for a single data distribution, less is known about how these predictions should change as we change the distribution. In this paper, we derive a strategy for predicting one loss from another and apply it to predict across... | {
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2411.12930 | LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog
Circuits | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Traditional approaches for designing analog circuits are time-consuming and require significant human expertise. Existing automation efforts using methods like Bayesian Optimization (BO) and Reinforcement Learning (RL) are sub-optimal and costly to generalize across different topologies and technology nodes. In our wor... | {
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2411.12935 | Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse
Identification of Nonlinear Dynamics | [
"eess.SY",
"cs.LG",
"cs.NE",
"cs.SY"
] | Accurate modeling of lithium ion (li-ion) batteries is essential for enhancing the safety, and efficiency of electric vehicles and renewable energy systems. This paper presents a data-inspired approach for improving the fidelity of reduced-order li-ion battery models. The proposed method combines a Genetic Algorithm wi... | {
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2411.12939 | Stabilization of Switched Affine Systems With Dwell-Time Constraint | [
"eess.SY",
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] | This paper addresses the problem of stabilization of switched affine systems under dwell-time constraint, giving guarantees on the bound of the quadratic cost associated with the proposed state switching control law. Specifically, two switching rules are presented relying on the solution of differential Lyapunov inequa... | {
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2411.12940 | On the relationship between Koopman operator approximations and neural
ordinary differential equations for data-driven time-evolution predictions | [
"nlin.CD",
"cs.LG"
] | This work explores the relationship between state space methods and Koopman operator-based methods for predicting the time-evolution of nonlinear dynamical systems. We demonstrate that extended dynamic mode decomposition with dictionary learning (EDMD-DL), when combined with a state space projection, is equivalent to a... | {
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2411.12943 | Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal
Identity and Motion Similarity | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Multiple Object Tracking (MOT) in thermal imaging presents unique challenges due to the lack of visual features and the complexity of motion patterns. This paper introduces an innovative approach to improve MOT in the thermal domain by developing a novel box association method that utilizes both thermal object identity... | {
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2411.12946 | A Flexible Large Language Models Guardrail Development Methodology
Applied to Off-Topic Prompt Detection | [
"cs.CL",
"cs.LG"
] | Large Language Models are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely on curated examples or custom classifiers, suffer from high false-positive rates, limited adaptability, and the impracticality of requiring real-wor... | {
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2411.12948 | Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse
Tsunameter Networks | [
"cs.LG",
"physics.flu-dyn"
] | We investigate the potential of an attention-based neural network architecture known as the Senseiver to perform sparse sensing tasks in the context of tsunami forecasting. In particular, we focus on the Tsunami Data Assimilation Method, where forecasts are derived from tsunameter networks. We used our model to generat... | {
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2411.12949 | Epidemiology-informed Network for Robust Rumor Detection | [
"cs.SI",
"cs.IR"
] | The rapid spread of rumors on social media has posed significant challenges to maintaining public trust and information integrity. Since an information cascade process is essentially a propagation tree, recent rumor detection models leverage graph neural networks to additionally capture information propagation patterns... | {
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2411.12950 | KAAE: Numerical Reasoning for Knowledge Graphs via Knowledge-aware
Attributes Learning | [
"cs.AI"
] | Numerical reasoning is pivotal in various artificial intelligence applications, such as natural language processing and recommender systems, where it involves using entities, relations, and attribute values (e.g., weight, length) to infer new factual relations (e.g., the Nile is longer than the Amazon). However, existi... | {
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2411.12951 | On the Consistency of Video Large Language Models in Temporal
Comprehension | [
"cs.CV"
] | Video large language models (Video-LLMs) can temporally ground language queries and retrieve video moments. Yet, such temporal comprehension capabilities are neither well-studied nor understood. So we conduct a study on prediction consistency -- a key indicator for robustness and trustworthiness of temporal grounding. ... | {
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2411.12955 | Matrix-Scheduling of QSR-Dissipative Systems | [
"eess.SY",
"cs.SY"
] | This paper considers gain-scheduling of QSR-dissipative subsystems using scheduling matrices. The corresponding QSR-dissipative properties of the overall matrix-gain-scheduled system, which depends on the QSR properties of the subsystems scheduled, are explicitly derived. The use of scheduling matrices is a generalizat... | {
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2411.12960 | I Can Tell What I am Doing: Toward Real-World Natural Language Grounding
of Robot Experiences | [
"cs.RO"
] | Understanding robot behaviors and experiences through natural language is crucial for developing intelligent and transparent robotic systems. Recent advancement in large language models (LLMs) makes it possible to translate complex, multi-modal robotic experiences into coherent, human-readable narratives. However, grou... | {
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2411.12962 | Bring the Heat: Rapid Trajectory Optimization with Pseudospectral
Techniques and the Affine Geometric Heat Flow Equation | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Generating optimal trajectories for high-dimensional robotic systems in a time-efficient manner while adhering to constraints is a challenging task. This paper introduces PHLAME, which applies pseudospectral collocation and spatial vector algebra to efficiently solve the Affine Geometric Heat Flow (AGHF) Partial Differ... | {
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2411.12963 | Probabilistic Dynamic Line Rating Forecasting with Line Graph
Convolutional LSTM | [
"eess.SY",
"cs.SY"
] | Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling stage is thus necessary for system operators to proactively optimize power flows, manage congestion, and reduce the cost... | {
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2411.12964 | Real-Time Energy-Optimal Path Planning for Electric Vehicles | [
"cs.AI"
] | The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only... | {
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2411.12965 | On adaptivity and minimax optimality of two-sided nearest neighbors | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.ME",
"stat.TH"
] | Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making systems. Prior theoretical analysis has established favorable guarantees for NN when the underlying data is sufficiently smooth and the missingness probabilities are lower bounded.... | {
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2411.12967 | Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue | [
"cs.RO",
"cs.AI"
] | Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban environments. We present a comprehensive approach to optimize UAV-based search and rescue operations in neighborhood areas, utilizing both a... | {
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2411.12968 | Quadratic Programming Optimization for Bio-Inspired Thruster-Assisted
Bipedal Locomotion on Inclined Slopes | [
"cs.RO"
] | Our work aims to make significant strides in understanding unexplored locomotion control paradigms based on the integration of posture manipulation and thrust vectoring. These techniques are commonly seen in nature, such as Chukar birds using their wings to run on a nearly vertical wall. In this work, we show quadratic... | {
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2411.12970 | Validation of Tumbling Robot Dynamics with Posture Manipulation for
Closed-Loop Heading Angle Control | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Navigating rugged terrain and steep slopes is a challenge for mobile robots. Conventional legged and wheeled systems struggle with these environments due to limited traction and stability. Northeastern University's COBRA (Crater Observing Bio-inspired Rolling Articulator), a novel multi-modal snake-like robot, addresse... | {
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2411.12972 | A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction | [
"cs.LG"
] | Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Traditional approaches have relied on separate models tailored to either grid-based data, representing cities as uniform cells, or graph-based da... | {
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2411.12973 | Adaptive Process-Guided Learning: An Application in Predicting Lake DO
Concentrations | [
"cs.LG"
] | This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sustaining water quality and ecosystem health. Unlike traditional RNNs, which m... | {
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2411.12977 | MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong
Collaborative Learning | [
"cs.AI",
"cs.CL"
] | Contemporary embodied agents powered by large language models (LLMs), such as Voyager, have shown promising capabilities in individual learning within open-ended environments like Minecraft. However, when powered by open LLMs, they struggle with basic tasks even after domain-specific fine-tuning. We present MindForge, ... | {
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2411.12980 | LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with
Token Selection, Recovery and Enhancement | [
"cs.CV",
"cs.AI"
] | Recent advancements in Visual Language Models (VLMs) have made them crucial for visual question answering (VQA) in autonomous driving, enabling natural human-vehicle interactions. However, existing methods often struggle in dynamic driving environments, as they usually focus on static images or videos and rely on downs... | {
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2411.12981 | GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting | [
"cs.CV"
] | Gaze estimation encounters generalization challenges when dealing with out-of-distribution data. To address this problem, recent methods use neural radiance fields (NeRF) to generate augmented data. However, existing methods based on NeRF are computationally expensive and lack facial details. 3D Gaussian Splatting (3DG... | {
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2411.12982 | Hierarchical Diffusion Policy: manipulation trajectory generation via
contact guidance | [
"cs.RO"
] | Decision-making in robotics using denoising diffusion processes has increasingly become a hot research topic, but end-to-end policies perform poorly in tasks with rich contact and have limited controllability. This paper proposes Hierarchical Diffusion Policy (HDP), a new imitation learning method of using objective co... | {
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2411.12986 | Training Bilingual LMs with Data Constraints in the Targeted Language | [
"cs.CL",
"cs.LG"
] | Large language models are trained on massive scrapes of the web, as required by current scaling laws. Most progress is made for English, given its abundance of high-quality pretraining data. For most other languages, however, such high quality pretraining data is unavailable. In this work, we study how to boost pretrai... | {
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2411.12989 | Data Watermarking for Sequential Recommender Systems | [
"cs.IR"
] | In the era of large foundation models, data has become a crucial component for building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyright protection is attracting increasing attention. In this work, we explore the problem of data watermarking for sequenti... | {
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2411.12990 | BetterBench: Assessing AI Benchmarks, Uncovering Issues, and
Establishing Best Practices | [
"cs.AI",
"cs.LG"
] | AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking progress, and identifying weaknesses in foundation and non-foundation models. They can ... | {
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2411.12992 | MemoryFormer: Minimize Transformer Computation by Removing
Fully-Connected Layers | [
"cs.CL"
] | In order to reduce the computational complexity of large language models, great efforts have been made to to improve the efficiency of transformer models such as linear attention and flash-attention. However, the model size and corresponding computational complexity are constantly scaled up in pursuit of higher perform... | {
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2411.12995 | Eliminating Ratio Bias for Gradient-based Simulated Parameter Estimation | [
"stat.ML",
"cs.LG",
"math.OC"
] | This article addresses the challenge of parameter calibration in stochastic models where the likelihood function is not analytically available. We propose a gradient-based simulated parameter estimation framework, leveraging a multi-time scale algorithm that tackles the issue of ratio bias in both maximum likelihood es... | {
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2411.12999 | From Signal Space To STP-CS | [
"eess.SY",
"cs.SY"
] | Under the assumption that a finite signal with different sampling lengths or different sampling frequencies is considered as equivalent, the signal space is considered as the quotient space of $\mathbb{R}^{\infty}$ over equivalence. The topological structure and the properties of signal space are investigated. Using th... | {
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2411.13000 | NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent
Detection | [
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT"
] | Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without relying on expensive channel estimation and feedback. This paper proposes NCAirFL, a CSI-free AirFL scheme based on unbiase... | {
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2411.13001 | Collaborative Feature-Logits Contrastive Learning for Open-Set
Semi-Supervised Object Detection | [
"cs.CV"
] | Current Semi-Supervised Object Detection (SSOD) methods enhance detector performance by leveraging large amounts of unlabeled data, assuming that both labeled and unlabeled data share the same label space. However, in open-set scenarios, the unlabeled dataset contains both in-distribution (ID) classes and out-of-distri... | {
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2411.13004 | MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable
Encrypted Traffic Classification | [
"cs.LG",
"cs.CR"
] | We present MERLOT, a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification. By applying model distillation techniques in a teacher-student paradigm, compact models derived from GPT-2-base retain high classification accuracy while minimizing co... | {
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2411.13005 | DT-LSD: Deformable Transformer-based Line Segment Detection | [
"cs.CV"
] | Line segment detection is a fundamental low-level task in computer vision, and improvements in this task can impact more advanced methods that depend on it. Most new methods developed for line segment detection are based on Convolutional Neural Networks (CNNs). Our paper seeks to address challenges that prevent the wid... | {
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2411.13006 | Automating Sonologists USG Commands with AI and Voice Interface | [
"eess.IV",
"cs.AI",
"cs.CV"
] | This research presents an advanced AI-powered ultrasound imaging system that incorporates real-time image processing, organ tracking, and voice commands to enhance the efficiency and accuracy of diagnoses in clinical practice. Traditional ultrasound diagnostics often require significant time and introduce a degree of s... | {
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2411.13008 | Evaluating LLMs Capabilities Towards Understanding Social Dynamics | [
"cs.LG",
"cs.AI"
] | Social media discourse involves people from different backgrounds, beliefs, and motives. Thus, often such discourse can devolve into toxic interactions. Generative Models, such as Llama and ChatGPT, have recently exploded in popularity due to their capabilities in zero-shot question-answering. Because these models are ... | {
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2411.13009 | LLMSteer: Improving Long-Context LLM Inference by Steering Attention on
Reused Contexts | [
"cs.LG",
"cs.CL"
] | As large language models (LLMs) show impressive performance on complex tasks, they still struggle with longer contextual understanding and high computational costs. To balance efficiency and quality, we introduce LLMSteer, a fine-tuning-free framework that enhances LLMs through query-independent attention steering. Tes... | {
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2411.13010 | Deriving Activation Functions Using Integration | [
"cs.LG",
"cs.NE"
] | Our work proposes a novel approach to designing activation functions by focusing on their gradients and deriving the corresponding activation functions using integration. We introduce the Expanded Integral of the Exponential Linear Unit (xIELU), a trainable piecewise activation function derived by integrating trainable... | {
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2411.13014 | Scalable Deep Metric Learning on Attributed Graphs | [
"cs.LG"
] | We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep metric and unbiased contrastive learning techniques to 1) work with attributed graphs, 2) enabling a mini-batch based app... | {
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2411.13015 | Strong XOR Lemma for Information Complexity | [
"cs.CC",
"cs.IT",
"math.IT"
] | For any $\{0,1\}$-valued function $f$, its \emph{$n$-folded XOR} is the function $f^{\oplus n}$ where $f^{\oplus n}(X_1, \ldots, X_n) = f(X_1) \oplus \cdots \oplus f(X_n)$. Given a procedure for computing the function $f$, one can apply a ``naive" approach to compute $f^{\oplus n}$ by computing each $f(X_i)$ independen... | {
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2411.13017 | Breaking the Cycle of Recurring Failures: Applying Generative AI to Root
Cause Analysis in Legacy Banking Systems | [
"cs.SE",
"cs.CL"
] | Traditional banks face significant challenges in digital transformation, primarily due to legacy system constraints and fragmented ownership. Recent incidents show that such fragmentation often results in superficial incident resolutions, leaving root causes unaddressed and causing recurring failures. We introduce a no... | {
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2411.13019 | Open-World Amodal Appearance Completion | [
"cs.CV"
] | Understanding and reconstructing occluded objects is a challenging problem, especially in open-world scenarios where categories and contexts are diverse and unpredictable. Traditional methods, however, are typically restricted to closed sets of object categories, limiting their use in complex, open-world scenes. We int... | {
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2411.13020 | AsymDex: Leveraging Asymmetry and Relative Motion in Learning Bimanual
Dexterity | [
"cs.RO"
] | We present Asymmetric Dexterity (AsymDex), a novel reinforcement learning (RL) framework that can efficiently learn asymmetric bimanual skills for multi-fingered hands without relying on demonstrations, which can be cumbersome to collect. Two crucial ingredients enable AsymDex to reduce the observation and action space... | {
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2411.13021 | Chanel-Orderer: A Channel-Ordering Predictor for Tri-Channel Natural
Images | [
"cs.CV"
] | This paper shows a proof-of-concept that, given a typical 3-channel images but in a randomly permuted channel order, a model (termed as Chanel-Orderer) with ad-hoc inductive biases in terms of both architecture and loss functions can accurately predict the channel ordering and knows how to make it right. Specifically, ... | {
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2411.13022 | Training Physics-Driven Deep Learning Reconstruction without Raw Data
Access for Equitable Fast MRI | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Physics-driven deep learning (PD-DL) approaches have become popular for improved reconstruction of fast magnetic resonance imaging (MRI) scans. Even though PD-DL offers higher acceleration rates compared to existing clinical fast MRI techniques, their use has been limited outside specialized MRI centers. One impediment... | {
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2411.13024 | Prior-based Objective Inference Mining Potential Uncertainty for Facial
Expression Recognition | [
"cs.CV"
] | Annotation ambiguity caused by the inherent subjectivity of visual judgment has always been a major challenge for Facial Expression Recognition (FER) tasks, particularly for largescale datasets from in-the-wild scenarios. A potential solution is the evaluation of relatively objective emotional distributions to help mit... | {
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2411.13025 | ORID: Organ-Regional Information Driven Framework for Radiology Report
Generation | [
"cs.CV"
] | The objective of Radiology Report Generation (RRG) is to automatically generate coherent textual analyses of diseases based on radiological images, thereby alleviating the workload of radiologists. Current AI-based methods for RRG primarily focus on modifications to the encoder-decoder model architecture. To advance th... | {
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2411.13026 | X as Supervision: Contending with Depth Ambiguity in Unsupervised
Monocular 3D Pose Estimation | [
"cs.CV"
] | Recent unsupervised methods for monocular 3D pose estimation have endeavored to reduce dependence on limited annotated 3D data, but most are solely formulated in 2D space, overlooking the inherent depth ambiguity issue. Due to the information loss in 3D-to-2D projection, multiple potential depths may exist, yet only so... | {
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2411.13028 | A Theory for Compressibility of Graph Transformers for Transductive
Learning | [
"cs.LG",
"stat.ML"
] | Transductive tasks on graphs differ fundamentally from typical supervised machine learning tasks, as the independent and identically distributed (i.i.d.) assumption does not hold among samples. Instead, all train/test/validation samples are present during training, making them more akin to a semi-supervised task. These... | {
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2411.13029 | Probably Approximately Precision and Recall Learning | [
"cs.LG",
"stat.ML"
] | Precision and Recall are foundational metrics in machine learning where both accurate predictions and comprehensive coverage are essential, such as in recommender systems and multi-label learning. In these tasks, balancing precision (the proportion of relevant items among those predicted) and recall (the proportion of ... | {
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2411.13032 | "It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large
Language Models | [
"cs.HC",
"cs.AI",
"cs.CY"
] | Given the rising proliferation and diversity of AI writing assistance tools, especially those powered by large language models (LLMs), both writers and readers may have concerns about the impact of these tools on the authenticity of writing work. We examine whether and how writers want to preserve their authentic voice... | {
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2411.13033 | LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned
Image Compression | [
"eess.IV",
"cs.CV"
] | Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achieve high compression rates and superior perceptual quality by using image captions as sub-information. This paper demonstrates that using a ... | {
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2411.13036 | Unsupervised Homography Estimation on Multimodal Image Pair via
Alternating Optimization | [
"cs.CV",
"cs.AI"
] | Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-truth data. In response, unsupervised learning approaches have emerged. Mo... | {
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2411.13040 | RobustFormer: Noise-Robust Pre-training for images and videos | [
"cs.CV"
] | While deep learning models are powerful tools that revolutionized many areas, they are also vulnerable to noise as they rely heavily on learning patterns and features from the exact details of the clean data. Transformers, which have become the backbone of modern vision models, are no exception. Current Discrete Wavele... | {
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2411.13042 | Attentive Contextual Attention for Cloud Removal | [
"cs.CV",
"eess.IV"
] | Cloud cover can significantly hinder the use of remote sensing images for Earth observation, prompting urgent advancements in cloud removal technology. Recently, deep learning strategies have shown strong potential in restoring cloud-obscured areas. These methods utilize convolution to extract intricate local features ... | {
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2411.13045 | Explainable LLM-driven Multi-dimensional Distillation for E-Commerce
Relevance Learning | [
"cs.IR",
"cs.AI",
"cs.CL"
] | Effective query-item relevance modeling is pivotal for enhancing user experience and safeguarding user satisfaction in e-commerce search systems. Recently, benefiting from the vast inherent knowledge, Large Language Model (LLM) approach demonstrates strong performance and long-tail generalization ability compared with ... | {
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2411.13047 | Bounding-box Watermarking: Defense against Model Extraction Attacks on
Object Detectors | [
"cs.CR",
"cs.CV"
] | Deep neural networks (DNNs) deployed in a cloud often allow users to query models via the APIs. However, these APIs expose the models to model extraction attacks (MEAs). In this attack, the attacker attempts to duplicate the target model by abusing the responses from the API. Backdoor-based DNN watermarking is known as... | {
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2411.13052 | On-device Content-based Recommendation with Single-shot Embedding
Pruning: A Cooperative Game Perspective | [
"cs.IR",
"cs.LG"
] | Content-based Recommender Systems (CRSs) play a crucial role in shaping user experiences in e-commerce, online advertising, and personalized recommendations. However, due to the vast amount of categorical features, the embedding tables used in CRS models pose a significant storage bottleneck for real-world deployment, ... | {
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2411.13053 | MEGL: Multimodal Explanation-Guided Learning | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for addressing their "black box" nature, particularly in tasks like image classification. Traditional eXplainable AI (XAI) methods typically rely on unimodal explanations, either visual or textual, each with inherent limitations.... | {
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2411.13055 | Hardware Scaling Trends and Diminishing Returns in Large-Scale
Distributed Training | [
"cs.LG",
"cs.DC"
] | Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources. To develop the exceedingly large networks required in modern applications, such as large language models (LLMs), model training is distributed acros... | {
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2411.13056 | Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale
Benchmark | [
"cs.CV"
] | The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of foreground objects. This often leads to severe under- and over-prediction problems and has been less studied in existing works. To tackle this issue in video object counting, we propose... | {
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2411.13057 | Branches, Assemble! Multi-Branch Cooperation Network for Large-Scale
Click-Through Rate Prediction at Taobao | [
"cs.IR",
"cs.AI"
] | Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strength, and solely using one type could constrain the model's capability to capture the complex feature relationships, especially for industrial ... | {
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2411.13059 | Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and
Anticipation | [
"cs.CV"
] | Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modelling objects and their evolving relationships over time. However, real-world visual relationships often exhibit a long-tailed distribution, causing existing methods for tasks like Video Scene Graph Generation ... | {
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2411.13069 | Automatic marker-free registration based on similar tetrahedras for
single-tree point clouds | [
"cs.CV"
] | In recent years, terrestrial laser scanning technology has been widely used to collect tree point cloud data, aiding in measurements of diameter at breast height, biomass, and other forestry survey data. Since a single scan from terrestrial laser systems captures data from only one angle, multiple scans must be registe... | {
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2411.13072 | AMaze: An intuitive benchmark generator for fast prototyping of
generalizable agents | [
"cs.RO",
"cs.AI"
] | Traditional approaches to training agents have generally involved a single, deterministic environment of minimal complexity to solve various tasks such as robot locomotion or computer vision. However, agents trained in static environments lack generalization capabilities, limiting their potential in broader scenarios. ... | {
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2411.13073 | Improving OOD Generalization of Pre-trained Encoders via Aligned
Embedding-Space Ensembles | [
"cs.LG",
"cs.CV"
] | The quality of self-supervised pre-trained embeddings on out-of-distribution (OOD) data is poor without fine-tuning. A straightforward and simple approach to improving the generalization of pre-trained representation to OOD data is the use of deep ensembles. However, obtaining an effective ensemble in the embedding spa... | {
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2411.13076 | Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in
Autonomous Driving | [
"cs.CV"
] | In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to represent driving-specific scenarios accurately, particularly in complex interactions and long-tail cases. To address this, we propose the Hints of Prompt (HoP) f... | {
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2411.13079 | Neural Internal Model Control: Learning a Robust Control Policy via
Predictive Error Feedback | [
"cs.RO",
"cs.AI"
] | Accurate motion control in the face of disturbances within complex environments remains a major challenge in robotics. Classical model-based approaches often struggle with nonlinearities and unstructured disturbances, while RL-based methods can be fragile when encountering unseen scenarios. In this paper, we propose a ... | {
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2411.13081 | Practical Compact Deep Compressed Sensing | [
"cs.CV",
"eess.IV"
] | Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical and compact network dubbed PCNet for general image CS. Specifically, in PCNet, a ... | {
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2411.13082 | Patience Is The Key to Large Language Model Reasoning | [
"cs.CL"
] | Recent advancements in the field of large language models, particularly through the Chain of Thought (CoT) approach, have demonstrated significant improvements in solving complex problems. However, existing models either tend to sacrifice detailed reasoning for brevity due to user preferences, or require extensive and ... | {
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2411.13083 | Omnipredicting Single-Index Models with Multi-Index Models | [
"cs.LG",
"cs.DS",
"math.OC",
"stat.ML"
] | Recent work on supervised learning [GKR+22] defined the notion of omnipredictors, i.e., predictor functions $p$ over features that are simultaneously competitive for minimizing a family of loss functions $\mathcal{L}$ against a comparator class $\mathcal{C}$. Omniprediction requires approximating the Bayes-optimal pred... | {
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2411.13089 | ESARM: 3D Emotional Speech-to-Animation via Reward Model from
Automatically-Ranked Demonstrations | [
"cs.CV",
"cs.SD",
"eess.AS"
] | This paper proposes a novel 3D speech-to-animation (STA) generation framework designed to address the shortcomings of existing models in producing diverse and emotionally resonant animations. Current STA models often generate animations that lack emotional depth and variety, failing to align with human expectations. To... | {
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2411.13093 | Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension | [
"cs.CV",
"cs.AI"
] | Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context. To address this problem, fine-tuning long-context LVLMs and employing GPT-based agents have emerged as promising solutions. However, fine-tuning LVLMs would require extensive high-quality data and substanti... | {
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2411.13097 | Incremental Label Distribution Learning with Scalable Graph
Convolutional Networks | [
"cs.LG",
"cs.IT",
"math.IT"
] | Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes a given sample. Most existing LDL methods consider the number of labels to be static. However, in various LDL-specific contexts (e.g., disea... | {
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2411.13100 | Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level
Granularity Syllable Count Control | [
"cs.CL",
"cs.AI"
] | Lyrics generation presents unique challenges, particularly in achieving precise syllable control while adhering to song form structures such as verses and choruses. Conventional line-by-line approaches often lead to unnatural phrasing, underscoring the need for more granular syllable management. We propose a framework ... | {
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2411.13104 | DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled
IoV | [
"cs.LG",
"cs.NI"
] | To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communi... | {
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2411.13105 | Superpixel Cost Volume Excitation for Stereo Matching | [
"cs.CV"
] | In this work, we concentrate on exciting the intrinsic local consistency of stereo matching through the incorporation of superpixel soft constraints, with the objective of mitigating inaccuracies at the boundaries of predicted disparity maps. Our approach capitalizes on the observation that neighboring pixels are predi... | {
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2411.13108 | Demonstrating the Suitability of Neuromorphic, Event-Based, Dynamic
Vision Sensors for In Process Monitoring of Metallic Additive Manufacturing
and Welding | [
"eess.IV",
"cs.CV"
] | We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manuf... | {
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2411.13109 | Special Unitary Parameterized Estimators of Rotation | [
"cs.RO"
] | This paper explores rotation estimation from the perspective of special unitary matrices. First, multiple solutions to Wahba's problem are derived through special unitary matrices, providing linear constraints on quaternion rotation parameters. Next, from these constraints, closed-form solutions to the problem are pres... | {
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2411.13112 | DriveMLLM: A Benchmark for Spatial Understanding with Multimodal Large
Language Models in Autonomous Driving | [
"cs.CV"
] | Autonomous driving requires a comprehensive understanding of 3D environments to facilitate high-level tasks such as motion prediction, planning, and mapping. In this paper, we introduce DriveMLLM, a benchmark specifically designed to evaluate the spatial understanding capabilities of multimodal large language models (M... | {
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2411.13116 | Provably Efficient Action-Manipulation Attack Against Continuous
Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Manipulating the interaction trajectories between the intelligent agent and the environment can control the agent's training and behavior, exposing the potential vulnerabilities of reinforcement learning (RL). For example, in Cyber-Physical Systems (CPS) controlled by RL, the attacker can manipulate the actions of the ... | {
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2411.13117 | Compute Optimal Inference and Provable Amortisation Gap in Sparse
Autoencoders | [
"cs.LG"
] | A recent line of work has shown promise in using sparse autoencoders (SAEs) to uncover interpretable features in neural network representations. However, the simple linear-nonlinear encoding mechanism in SAEs limits their ability to perform accurate sparse inference. Using compressed sensing theory, we prove that an SA... | {
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2411.13120 | Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry | [
"cs.CV",
"cs.LG",
"physics.med-ph",
"physics.optics"
] | Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial distributions of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms ... | {
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2411.13127 | Adapting Vision Foundation Models for Robust Cloud Segmentation in
Remote Sensing Images | [
"cs.CV"
] | Cloud segmentation is a critical challenge in remote sensing image interpretation, as its accuracy directly impacts the effectiveness of subsequent data processing and analysis. Recently, vision foundation models (VFM) have demonstrated powerful generalization capabilities across various visual tasks. In this paper, we... | {
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2411.13134 | Approximating Spatial Distance Through Confront Networks: Application to
the Segmentation of Medieval Avignon | [
"cs.SI"
] | In historical studies, the older the sources, the more common it is to have access to data that are only partial, and/or unreliable or imprecise. This can make it difficult, or even impossible, to perform certain tasks of interest, such as the segmentation of some urban space based on the location of its constituting e... | {
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2411.13136 | TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in
Vision-Language Models | [
"cs.CV"
] | Large pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated excellent zero-shot generalizability across various downstream tasks. However, recent studies have shown that the inference performance of CLIP can be greatly degraded by small adversarial perturbations, especially its visual modality, posin... | {
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2411.13137 | Domain Adaptive Unfolded Graph Neural Networks | [
"cs.LG",
"eess.SP"
] | Over the last decade, graph neural networks (GNNs) have made significant progress in numerous graph machine learning tasks. In real-world applications, where domain shifts occur and labels are often unavailable for a new target domain, graph domain adaptation (GDA) approaches have been proposed to facilitate knowledge ... | {
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2411.13140 | Robust Convergency Indicator using High-dimension PID Controller in the
presence of disturbance | [
"eess.SY",
"cs.SY"
] | The PID controller currently occupies a prominent position as the most prevalent control architecture, which has achieved groundbreaking success across extensive implications. However, its parameters online regulation remains a formidable challenge. The majority of existing theories hinge on the linear constant system ... | {
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2411.13144 | CopyrightMeter: Revisiting Copyright Protection in Text-to-image Models | [
"cs.CR",
"cs.AI",
"cs.CV"
] | Text-to-image diffusion models have emerged as powerful tools for generating high-quality images from textual descriptions. However, their increasing popularity has raised significant copyright concerns, as these models can be misused to reproduce copyrighted content without authorization. In response, recent studies h... | {
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2411.13145 | Globally Correlation-Aware Hard Negative Generation | [
"cs.CV"
] | Hard negative generation aims to generate informative negative samples that help to determine the decision boundaries and thus facilitate advancing deep metric learning. Current works select pair/triplet samples, learn their correlations, and fuse them to generate hard negatives. However, these works merely consider th... | {
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2411.13147 | GraphCL: Graph-based Clustering for Semi-Supervised Medical Image
Segmentation | [
"cs.CV",
"cs.AI"
] | Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importa... | {
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2411.13148 | Learning Time-Optimal and Speed-Adjustable Tactile In-Hand Manipulation | [
"cs.RO"
] | In-hand manipulation with multi-fingered hands is a challenging problem that recently became feasible with the advent of deep reinforcement learning methods. While most contributions to the task brought improvements in robustness and generalization, this paper addresses the critical performance measure of the speed at ... | {
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2411.13149 | YCB-LUMA: YCB Object Dataset with Luminance Keying for Object
Localization | [
"cs.CV",
"cs.AI"
] | Localizing target objects in images is an important task in computer vision. Often it is the first step towards solving a variety of applications in autonomous driving, maintenance, quality insurance, robotics, and augmented reality. Best in class solutions for this task rely on deep neural networks, which require a se... | {
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2411.13150 | RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image
Generation | [
"cs.CV"
] | Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for precise recognition, particularly in challenging conditions like low-light environments. The resultant demand for comprehensive RAW image dat... | {
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2411.13152 | AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation | [
"cs.CV",
"cs.AI"
] | In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its generalization capability for the target domain. A key advantage of SSDA is its ability to significantly reduce reliance on labeled data, th... | {
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2411.13153 | Long-term Detection System for Six Kinds of Abnormal Behavior of the
Elderly Living Alone | [
"cs.LG"
] | The proportion of elderly people is increasing worldwide, particularly those living alone in Japan. As elderly people get older, their risks of physical disabilities and health issues increase. To automatically discover these issues at a low cost in daily life, sensor-based detection in a smart home is promising. As pa... | {
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2411.13154 | DMQR-RAG: Diverse Multi-Query Rewriting for RAG | [
"cs.IR",
"cs.AI"
] | Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these issues by incorporating external information. However, user queries frequently contain noise and intent deviations, necessitating query rewri... | {
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2411.13156 | MecQaBot: A Modular Robot Sensing and Wireless Mechatronics Framework
for Education and Research | [
"cs.RO"
] | We introduce MecQaBot, an open-source, affordable, and modular autonomous mobile robotics framework developed for education and research at Macquarie University, School of Engineering, since 2019. This platform aims to provide students and researchers with an accessible means for exploring autonomous robotics and foste... | {
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