id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.14290 | Soft Manipulation Surface With Reduced Actuator Density For
Heterogeneous Object Manipulation | [
"cs.RO"
] | Object manipulation in robotics faces challenges due to diverse object shapes, sizes, and fragility. Gripper-based methods offer precision and low degrees of freedom (DOF) but the gripper limits the kind of objects to grasp. On the other hand, surface-based approaches provide flexibility for handling fragile and hetero... | {
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2411.14292 | Hypothesis testing of symmetry in quantum dynamics | [
"quant-ph",
"cs.IT",
"math.IT"
] | Symmetry plays a crucial role in quantum physics, dictating the behavior and dynamics of physical systems. In this paper, We develop a hypothesis-testing framework for quantum dynamics symmetry using a limited number of queries to the unknown unitary operation and establish the quantum max-relative entropy lower bound ... | {
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2411.14295 | StereoCrafter-Zero: Zero-Shot Stereo Video Generation with Noisy Restart | [
"cs.CV"
] | Generating high-quality stereo videos that mimic human binocular vision requires maintaining consistent depth perception and temporal coherence across frames. While diffusion models have advanced image and video synthesis, generating high-quality stereo videos remains challenging due to the difficulty of maintaining co... | {
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2411.14296 | Improving Routability Prediction via NAS Using a Smooth One-shot
Augmented Predictor | [
"cs.LG"
] | Routability optimization in modern EDA tools has benefited greatly from using machine learning (ML) models. Constructing and optimizing the performance of ML models continues to be a challenge. Neural Architecture Search (NAS) serves as a tool to aid in the construction and improvement of these models. Traditional NAS ... | {
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2411.14303 | BugSpotter: Automated Generation of Code Debugging Exercises | [
"cs.SE",
"cs.AI"
] | Debugging is an essential skill when learning to program, yet its instruction and emphasis often vary widely across introductory courses. In the era of code-generating large language models (LLMs), the ability for students to reason about code and identify errors is increasingly important. However, students frequently ... | {
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2411.14305 | Outlier-robust Mean Estimation near the Breakdown Point via
Sum-of-Squares | [
"cs.DS",
"cs.LG",
"stat.ML"
] | We revisit the problem of estimating the mean of a high-dimensional distribution in the presence of an $\varepsilon$-fraction of adversarial outliers. When $\varepsilon$ is at most some sufficiently small constant, previous works can achieve optimal error rate efficiently \cite{diakonikolas2018robustly, kothari2018ro... | {
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2411.14317 | Model-free learning of probability flows: Elucidating the nonequilibrium
dynamics of flocking | [
"cond-mat.stat-mech",
"cs.LG",
"math.PR"
] | Active systems comprise a class of nonequilibrium dynamics in which individual components autonomously dissipate energy. Efforts towards understanding the role played by activity have centered on computation of the entropy production rate (EPR), which quantifies the breakdown of time reversal symmetry. A fundamental di... | {
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2411.14318 | Velocitune: A Velocity-based Dynamic Domain Reweighting Method for
Continual Pre-training | [
"cs.CL"
] | It is well-known that a diverse corpus is critical for training large language models, which are typically constructed from a mixture of various domains. In general, previous efforts resort to sampling training data from different domains with static proportions, as well as adjusting data proportions during training. H... | {
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2411.14319 | Iteration-Free Cooperative Distributed MPC through Multiparametric
Programming | [
"eess.SY",
"cs.SY"
] | Cooperative Distributed Model Predictive Control (DiMPC) architecture employs local MPC controllers to control different subsystems, exchanging information with each other through an iterative procedure to enhance overall control performance compared to the decentralized architecture. However, this method can result in... | {
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2411.14321 | Continual Learning and Lifting of Koopman Dynamics for Linear Control of
Legged Robots | [
"cs.RO"
] | The control of legged robots, particularly humanoid and quadruped robots, presents significant challenges due to their high-dimensional and nonlinear dynamics. While linear systems can be effectively controlled using methods like Model Predictive Control (MPC), the control of nonlinear systems remains complex. One prom... | {
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2411.14322 | SplatR : Experience Goal Visual Rearrangement with 3D Gaussian Splatting
and Dense Feature Matching | [
"cs.RO",
"cs.CV"
] | Experience Goal Visual Rearrangement task stands as a foundational challenge within Embodied AI, requiring an agent to construct a robust world model that accurately captures the goal state. The agent uses this world model to restore a shuffled scene to its original configuration, making an accurate representation of t... | {
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2411.14330 | Datalog with First-Class Facts | [
"cs.DB",
"cs.PL"
] | Datalog is a popular logic programming language for deductive reasoning tasks in a wide array of applications, including business analytics, program analysis, and ontological reasoning. However, Datalog's restriction to flat facts over atomic constants leads to challenges in working with tree-structured data, such as d... | {
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2411.14331 | Data Formats in Analytical DBMSs: Performance Trade-offs and Future
Directions | [
"cs.DB"
] | This paper evaluates the suitability of Apache Arrow, Parquet, and ORC as formats for subsumption in an analytical DBMS. We systematically identify and explore the high-level features that are important to support efficient querying in modern OLAP DBMSs and evaluate the ability of each format to support these features.... | {
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2411.14336 | Finding the root in random nearest neighbor trees | [
"math.PR",
"cs.DS",
"cs.SI"
] | We study the inference of network archaeology in growing random geometric graphs. We consider the root finding problem for a random nearest neighbor tree in dimension $d \in \mathbb{N}$, generated by sequentially embedding vertices uniformly at random in the $d$-dimensional torus and connecting each new vertex to the n... | {
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2411.14341 | Logarithmic Neyman Regret for Adaptive Estimation of the Average
Treatment Effect | [
"stat.ML",
"cs.LG"
] | Estimation of the Average Treatment Effect (ATE) is a core problem in causal inference with strong connections to Off-Policy Evaluation in Reinforcement Learning. This paper considers the problem of adaptively selecting the treatment allocation probability in order to improve estimation of the ATE. The majority of prio... | {
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2411.14343 | UnifiedCrawl: Aggregated Common Crawl for Affordable Adaptation of LLMs
on Low-Resource Languages | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) under-perform on low-resource languages due to limited training data. We present a method to efficiently collect text data for low-resource languages from the entire Common Crawl corpus. Our approach, UnifiedCrawl, filters and extracts common crawl using minimal compute resources, yielding ... | {
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2411.14344 | Overcomplete Tensor Decomposition via Koszul-Young Flattenings | [
"cs.DS",
"cs.LG"
] | Motivated by connections between algebraic complexity lower bounds and tensor decompositions, we investigate Koszul-Young flattenings, which are the main ingredient in recent lower bounds for matrix multiplication. Based on this tool we give a new algorithm for decomposing an $n_1 \times n_2 \times n_3$ tensor as the s... | {
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2411.14345 | Layer Pruning with Consensus: A Triple-Win Solution | [
"cs.LG",
"cs.CV"
] | Layer pruning offers a promising alternative to standard structured pruning, effectively reducing computational costs, latency, and memory footprint. While notable layer-pruning approaches aim to detect unimportant layers for removal, they often rely on single criteria that may not fully capture the complex, underlying... | {
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2411.14346 | Lower Dimensional Spherical Representation of Medium Voltage Load
Profiles for Visualization, Outlier Detection, and Generative Modelling | [
"eess.SY",
"cs.SY"
] | This paper presents the spherical lower dimensional representation for daily medium voltage load profiles, based on principal component analysis. The objective is to unify and simplify the tasks for (i) clustering visualisation, (ii) outlier detection and (iii) generative profile modelling under one concept. The lower ... | {
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2411.14347 | DINO-X: A Unified Vision Model for Open-World Object Detection and
Understanding | [
"cs.CV"
] | In this paper, we introduce DINO-X, which is a unified object-centric vision model developed by IDEA Research with the best open-world object detection performance to date. DINO-X employs the same Transformer-based encoder-decoder architecture as Grounding DINO 1.5 to pursue an object-level representation for open-worl... | {
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2411.14349 | Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals | [
"cs.LG",
"cs.DS",
"stat.ML"
] | We consider the problem of learning an arbitrarily-biased ReLU activation (or neuron) over Gaussian marginals with the squared loss objective. Despite the ReLU neuron being the basic building block of modern neural networks, we still do not understand the basic algorithmic question of whether one arbitrary ReLU neuron ... | {
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2411.14351 | Indiscriminate Disruption of Conditional Inference on Multivariate
Gaussians | [
"stat.ML",
"cs.CR",
"cs.LG",
"stat.AP"
] | The multivariate Gaussian distribution underpins myriad operations-research, decision-analytic, and machine-learning models (e.g., Bayesian optimization, Gaussian influence diagrams, and variational autoencoders). However, despite recent advances in adversarial machine learning (AML), inference for Gaussian models in t... | {
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2411.14353 | Enhancing Medical Image Segmentation with Deep Learning and Diffusion
Models | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Medical image segmentation is crucial for accurate clinical diagnoses, yet it faces challenges such as low contrast between lesions and normal tissues, unclear boundaries, and high variability across patients. Deep learning has improved segmentation accuracy and efficiency, but it still relies heavily on expert annotat... | {
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2411.14354 | Contrasting local and global modeling with machine learning and
satellite data: A case study estimating tree canopy height in African
savannas | [
"cs.LG",
"cs.AI",
"cs.CV"
] | While advances in machine learning with satellite imagery (SatML) are facilitating environmental monitoring at a global scale, developing SatML models that are accurate and useful for local regions remains critical to understanding and acting on an ever-changing planet. As increasing attention and resources are being d... | {
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2411.14356 | Convex Approximation of Probabilistic Reachable Sets from Small Samples
Using Self-supervised Neural Networks | [
"cs.RO"
] | Probabilistic Reachable Set (PRS) plays a crucial role in many fields of autonomous systems, yet efficiently generating PRS remains a significant challenge. This paper presents a learning approach to generating 2-dimensional PRS for states in a dynamic system. Traditional methods such as Hamilton-Jacobi reachability an... | {
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2411.14358 | InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating SLAM in
Indoor Pedestrian-Rich Spaces for Human Navigation | [
"cs.RO",
"cs.CV"
] | Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual-inertial dataset specifically designed for human n... | {
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2411.14365 | Formal Simulation and Visualisation of Hybrid Programs | [
"eess.SY",
"cs.PL",
"cs.SY"
] | The design and analysis of systems that combine computational behaviour with physical processes' continuous dynamics - such as movement, velocity, and voltage - is a famous, challenging task. Several theoretical results from programming theory emerged in the last decades to tackle the issue; some of which are the basis... | {
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2411.14367 | ROSMonitoring 2.0: Extending ROS Runtime Verification to Services and
Ordered Topics | [
"cs.SE",
"cs.AI",
"cs.RO"
] | Formal verification of robotic applications presents challenges due to their hybrid nature and distributed architecture. This paper introduces ROSMonitoring 2.0, an extension of ROSMonitoring designed to facilitate the monitoring of both topics and services while considering the order in which messages are published an... | {
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2411.14368 | RV4Chatbot: Are Chatbots Allowed to Dream of Electric Sheep? | [
"cs.AI",
"cs.HC",
"cs.SE"
] | Chatbots have become integral to various application domains, including those with safety-critical considerations. As a result, there is a pressing need for methods that ensure chatbots consistently adhere to expected, safe behaviours. In this paper, we introduce RV4Chatbot, a Runtime Verification framework designed to... | {
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2411.14369 | Model Checking and Verification of Synchronisation Properties of Cobot
Welding | [
"cs.RO",
"cs.MA",
"cs.SE"
] | This paper describes use of model checking to verify synchronisation properties of an industrial welding system consisting of a cobot arm and an external turntable. The robots must move synchronously, but sometimes get out of synchronisation, giving rise to unsatisfactory weld qualities in problem areas, such as around... | {
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2411.14371 | Synthesising Robust Controllers for Robot Collectives with Recurrent
Tasks: A Case Study | [
"cs.MA",
"cs.AI",
"cs.RO"
] | When designing correct-by-construction controllers for autonomous collectives, three key challenges are the task specification, the modelling, and its use at practical scale. In this paper, we focus on a simple yet useful abstraction for high-level controller synthesis for robot collectives with optimisation goals (e.g... | {
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2411.14373 | Cross--layer Formal Verification of Robotic Systems | [
"cs.RO"
] | Robotic systems are widely used to interact with humans or to perform critical tasks. As a result, it is imperative to provide guarantees about their behavior. Due to the modularity and complexity of robotic systems, their design and verification are often divided into several layers. However, some system properties ca... | {
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2411.14374 | Using Formal Models, Safety Shields and Certified Control to Validate
AI-Based Train Systems | [
"cs.LO",
"cs.AI",
"cs.CV"
] | The certification of autonomous systems is an important concern in science and industry. The KI-LOK project explores new methods for certifying and safely integrating AI components into autonomous trains. We pursued a two-layered approach: (1) ensuring the safety of the steering system by formal analysis using the B me... | {
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2411.14375 | Model Checking for Reinforcement Learning in Autonomous Driving: One Can
Do More Than You Think! | [
"cs.LG",
"cs.LO"
] | Most reinforcement learning (RL) platforms use high-level programming languages, such as OpenAI Gymnasium using Python. These frameworks provide various API and benchmarks for testing RL algorithms in different domains, such as autonomous driving (AD) and robotics. These platforms often emphasise the design of RL algor... | {
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2411.14378 | CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent
Diffusion Models with Neural Fields | [
"physics.flu-dyn",
"cs.LG"
] | Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multi-scale structures of turbulence. Traditional recycling-based methods rely on computationally expensive precursor simulations, while existing synthetic inflow generators often fail to reproduce realisti... | {
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2411.14381 | ETA-IK: Execution-Time-Aware Inverse Kinematics for Dual-Arm Systems | [
"cs.RO"
] | This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redundancy of both arms, specifically in tasks where only the relative pose of the robots is constrained, such as dual-arm sca... | {
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2411.14384 | Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable
Single-stage Image-to-3D Generation | [
"cs.CV",
"cs.GR"
] | Existing feed-forward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction and mainly handle object-centric prompt images. In this paper, we propose a novel single-stage 3D diffusion model, Diffusion... | {
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2411.14385 | Enhancing Diagnostic Precision in Gastric Bleeding through Automated
Lesion Segmentation: A Deep DuS-KFCM Approach | [
"eess.IV",
"cs.CV"
] | Timely and precise classification and segmentation of gastric bleeding in endoscopic imagery are pivotal for the rapid diagnosis and intervention of gastric complications, which is critical in life-saving medical procedures. Traditional methods grapple with the challenge posed by the indistinguishable intensity values ... | {
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2411.14386 | Learning Humanoid Locomotion with Perceptive Internal Model | [
"cs.RO"
] | In contrast to quadruped robots that can navigate diverse terrains using a "blind" policy, humanoid robots require accurate perception for stable locomotion due to their high degrees of freedom and inherently unstable morphology. However, incorporating perceptual signals often introduces additional disturbances to the ... | {
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2411.14390 | Persistent Homology for Structural Characterization in Disordered
Systems | [
"cond-mat.dis-nn",
"cond-mat.mtrl-sci",
"cs.LG",
"math-ph",
"math.MP"
] | We propose a unified framework based on persistent homology (PH) to characterize both local and global structures in disordered systems. It can simultaneously generate local and global descriptors using the same algorithm and data structure, and has shown to be highly effective and interpretable in predicting particle ... | {
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2411.14393 | POS-tagging to highlight the skeletal structure of sentences | [
"cs.CL"
] | This study presents the development of a part-of-speech (POS) tagging model to extract the skeletal structure of sentences using transfer learning with the BERT architecture for token classification. The model, fine-tuned on Russian text, demonstrating its effectiveness. The approach offers potential applications in en... | {
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2411.14398 | Lightweight Safety Guardrails Using Fine-tuned BERT Embeddings | [
"cs.CL"
] | With the recent proliferation of large language models (LLMs), enterprises have been able to rapidly develop proof-of-concepts and prototypes. As a result, there is a growing need to implement robust guardrails that monitor, quantize and control an LLM's behavior, ensuring that the use is reliable, safe, accurate and a... | {
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2411.14400 | 23 DoF Grasping Policies from a Raw Point Cloud | [
"cs.RO"
] | Coordinating the motion of robots with high degrees of freedom (DoF) to grasp objects gives rise to many challenges. In this paper, we propose a novel imitation learning approach to learn a policy that directly predicts 23 DoF grasp trajectories from a partial point cloud provided by a single, fixed camera. At the core... | {
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2411.14401 | Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding | [
"cs.CV",
"cs.LG"
] | Recent advancements in multimodal large language models (MLLMs) have opened new avenues for video understanding. However, achieving high fidelity in zero-shot video tasks remains challenging. Traditional video processing methods rely heavily on fine-tuning to capture nuanced spatial-temporal details, which incurs signi... | {
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2411.14402 | Multimodal Autoregressive Pre-training of Large Vision Encoders | [
"cs.CV",
"cs.LG"
] | We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we present AIMV2, a family of generalist vision encoders characterized by ... | {
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2411.14403 | Landing Trajectory Prediction for UAS Based on Generative Adversarial
Network | [
"cs.RO",
"cs.AI"
] | Models for trajectory prediction are an essential component of many advanced air mobility studies. These models help aircraft detect conflict and plan avoidance maneuvers, which is especially important in Unmanned Aircraft systems (UAS) landing management due to the congested airspace near vertiports. In this paper, we... | {
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2411.14404 | Resolving Multiple-Dynamic Model Uncertainty in Hypothesis-Driven
Belief-MDPs | [
"cs.AI",
"cs.RO"
] | When human operators of cyber-physical systems encounter surprising behavior, they often consider multiple hypotheses that might explain it. In some cases, taking information-gathering actions such as additional measurements or control inputs given to the system can help resolve uncertainty and determine the most accur... | {
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2411.14405 | Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions | [
"cs.CL"
] | Currently OpenAI o1 sparks a surge of interest in the study of large reasoning models (LRM). Building on this momentum, Marco-o1 not only focuses on disciplines with standard answers, such as mathematics, physics, and coding -- which are well-suited for reinforcement learning (RL) -- but also places greater emphasis on... | {
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2411.14411 | Multi-Agent Environments for Vehicle Routing Problems | [
"cs.LG",
"cs.MA"
] | Research on Reinforcement Learning (RL) approaches for discrete optimization problems has increased considerably, extending RL to an area classically dominated by Operations Research (OR). Vehicle routing problems are a good example of discrete optimization problems with high practical relevance where RL techniques hav... | {
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2411.14412 | Adversarial Poisoning Attack on Quantum Machine Learning Models | [
"quant-ph",
"cs.CR",
"cs.CV"
] | With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks associated with QML has become an urgent priority. One key concern in the QML domain is the threat of data poisoning attacks in the current qu... | {
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2411.14418 | Multimodal 3D Brain Tumor Segmentation with Adversarial Training and
Conditional Random Field | [
"eess.IV",
"cs.CV"
] | Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas. Leveraging the pre-eminent detail resilience of CRF and spatial feature extraction capacity of V-net, we propose a multimodal 3D Volume Generative Adversarial Network (3D-vGAN) for prec... | {
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2411.14421 | From RNNs to Foundation Models: An Empirical Study on Commercial
Building Energy Consumption | [
"cs.LG"
] | Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing datas... | {
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2411.14423 | Unleashing the Potential of Multi-modal Foundation Models and Video
Diffusion for 4D Dynamic Physical Scene Simulation | [
"cs.CV"
] | Realistic simulation of dynamic scenes requires accurately capturing diverse material properties and modeling complex object interactions grounded in physical principles. However, existing methods are constrained to basic material types with limited predictable parameters, making them insufficient to represent the comp... | {
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2411.14424 | Learning Fair Robustness via Domain Mixup | [
"cs.LG",
"cs.CR",
"cs.CY"
] | Adversarial training is one of the predominant techniques for training classifiers that are robust to adversarial attacks. Recent work, however has found that adversarial training, which makes the overall classifier robust, it does not necessarily provide equal amount of robustness for all classes. In this paper, we pr... | {
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2411.14425 | Whack-a-Chip: The Futility of Hardware-Centric Export Controls | [
"cs.CY",
"cs.AI"
] | U.S. export controls on semiconductors are widely known to be permeable, with the People's Republic of China (PRC) steadily creating state-of-the-art artificial intelligence (AI) models with exfiltrated chips. This paper presents the first concrete, public evidence of how leading PRC AI labs evade and circumvent U.S. e... | {
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2411.14427 | Transformer-based Heuristic for Advanced Air Mobility Planning | [
"cs.RO"
] | Safety is extremely important for urban flights of autonomous Unmanned Aerial Vehicles (UAVs). Risk-aware path planning is one of the most effective methods to guarantee the safety of UAVs. This type of planning can be represented as a Constrained Shortest Path (CSP) problem, which seeks to find the shortest route that... | {
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2411.14429 | Revisiting the Integration of Convolution and Attention for Vision
Backbone | [
"cs.CV",
"cs.AI"
] | Convolutions (Convs) and multi-head self-attentions (MHSAs) are typically considered alternatives to each other for building vision backbones. Although some works try to integrate both, they apply the two operators simultaneously at the finest pixel granularity. With Convs responsible for per-pixel feature extraction a... | {
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2411.14430 | Stable Flow: Vital Layers for Training-Free Image Editing | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT), and employed flow-matching for improved training and sampling. However, they exhibit limited generation diversity. In this work, we leverag... | {
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2411.14432 | Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large
Language Models | [
"cs.CV"
] | Large Language Models (LLMs) demonstrate enhanced capabilities and reliability by reasoning more, evolving from Chain-of-Thought prompting to product-level solutions like OpenAI o1. Despite various efforts to improve LLM reasoning, high-quality long-chain reasoning data and optimized training pipelines still remain ina... | {
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2411.14433 | Transforming Engineering Education Using Generative AI and Digital Twin
Technologies | [
"cs.CY",
"cs.AI",
"cs.CR",
"cs.HC"
] | Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom's taxonomy... | {
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2411.14438 | Agent-Based Modeling for Multimodal Transportation of $CO_2$ for Carbon
Capture, Utilization, and Storage: CCUS-Agent | [
"cs.MA"
] | To understand the system-level interactions between the entities in Carbon Capture, Utilization, and Storage (CCUS), an agent-based foundational modeling tool, CCUS-Agent, is developed for a large-scale study of transportation flows and infrastructure in the United States. Key features of the tool include (i) modular d... | {
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2411.14441 | GeMID: Generalizable Models for IoT Device Identification | [
"cs.CR",
"cs.AI",
"cs.NI"
] | With the proliferation of Internet of Things (IoT) devices, ensuring their security has become paramount. Device identification (DI), which distinguishes IoT devices based on their traffic patterns, plays a crucial role in both differentiating devices and identifying vulnerable ones, closing a serious security gap. How... | {
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2411.14442 | AI Ethics by Design: Implementing Customizable Guardrails for
Responsible AI Development | [
"cs.CY",
"cs.CL"
] | This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ens... | {
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2411.14443 | Industrial Machines Health Prognosis using a Transformer-based Framework | [
"eess.SP",
"cs.LG"
] | This article introduces Transformer Quantile Regression Neural Networks (TQRNNs), a novel data-driven solution for real-time machine failure prediction in manufacturing contexts. Our objective is to develop an advanced predictive maintenance model capable of accurately identifying machine system breakdowns. To do so, T... | {
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2411.14446 | Rising Rested Bandits: Lower Bounds and Efficient Algorithms | [
"stat.ML",
"cs.LG"
] | This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e. those sequential selection techniques able to learn online using only the feedback given by the chosen option (a.k.a. $arm$). We study a particular case of the rested bandits in which the arms' expected reward is monotonically non-decreasing and ... | {
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2411.14449 | Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on
Deep Learning Models | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Deep learning models are vulnerable to backdoor attacks, where adversaries inject malicious functionality during training that activates on trigger inputs at inference time. Extensive research has focused on developing stealthy backdoor attacks to evade detection and defense mechanisms. However, these approaches still ... | {
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2411.14452 | Past, Present, and Future of Sensor-Based Human Activity Recognition
Using Wearables: A Surveying Tutorial on a Still Challenging Task | [
"eess.SP",
"cs.LG"
] | In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to recognize activities. Substantial gains have been made since the days of hand-crafting heuristics as features, yet, progress has seemingly st... | {
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2411.14453 | Direct Speech-to-Speech Neural Machine Translation: A Survey | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Speech-to-Speech Translation (S2ST) models transform speech from one language to another target language with the same linguistic information. S2ST is important for bridging the communication gap among communities and has diverse applications. In recent years, researchers have introduced direct S2ST models, which have ... | {
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2411.14456 | Can Artificial Intelligence Generate Quality Research Topics Reflecting
Patient Concerns? | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Patient-centered research is increasingly important in narrowing the gap between research and patient care, yet incorporating patient perspectives into health research has been inconsistent. We propose an automated framework leveraging innovative natural language processing (NLP) and artificial intelligence (AI) with p... | {
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2411.14457 | Guiding Reinforcement Learning Using Uncertainty-Aware Large Language
Models | [
"cs.LG"
] | Human guidance in reinforcement learning (RL) is often impractical for large-scale applications due to high costs and time constraints. Large Language Models (LLMs) offer a promising alternative to mitigate RL sample inefficiency and potentially replace human trainers. However, applying LLMs as RL trainers is challengi... | {
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2411.14458 | Improving training time and GPU utilization in geo-distributed language
model training | [
"cs.DC",
"cs.AI",
"cs.LG"
] | The widespread adoption of language models (LMs) across multiple industries has caused huge surge in demand for GPUs. Training LMs requires tens of thousands of GPUs and housing them in the same datacenter (DCs) is becoming challenging. We focus on training such models across multiple DCs connected via Wide-Area-Networ... | {
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2411.14459 | Unveiling User Preferences: A Knowledge Graph and LLM-Driven Approach
for Conversational Recommendation | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Conversational Recommender Systems (CRSs) aim to provide personalized recommendations through dynamically capturing user preferences in interactive conversations. Conventional CRSs often extract user preferences as hidden representations, which are criticized for their lack of interpretability. This diminishes the tran... | {
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2411.14460 | LLaSA: Large Language and Structured Data Assistant | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Structured data, such as tables, graphs, and databases, play a critical role in plentiful NLP tasks such as question answering and dialogue system. Recently, inspired by Vision-Language Models, Graph Neutral Networks (GNNs) have been introduced as an additional modality into the input of Large Language Models (LLMs) to... | {
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2411.14461 | Towards Next-Generation Medical Agent: How o1 is Reshaping
Decision-Making in Medical Scenarios | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making. Traditional model-based approaches, including those leveraging in-context demonstrations and those with specialized medical fine-tuning, have demonstrated st... | {
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2411.14462 | Activation Functions for "A Feedforward Unitary Equivariant Neural
Network" | [
"cs.LG",
"cs.NE"
] | In our previous work [Ma and Chan (2023)], we presented a feedforward unitary equivariant neural network. We proposed three distinct activation functions tailored for this network: a softsign function with a small residue, an identity function, and a Leaky ReLU function. While these functions demonstrated the desired e... | {
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2411.14463 | Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap
Analysis | [
"cs.CL",
"cs.AI",
"econ.GN",
"q-fin.EC"
] | This paper explores the growing impact of AI and NLP in bank marketing, highlighting their evolving roles in enhancing marketing strategies, improving customer engagement, and creating value within this sector. While AI and NLP have been widely studied in general marketing, there is a notable gap in understanding their... | {
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2411.14464 | JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules
for the Annotation of Untargeted Metabolomics Data | [
"q-bio.QM",
"cs.AI",
"cs.LG",
"q-bio.BM"
] | Motivation: A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra and in spectra-to-molecular fingerprint prediction (FP), annotation rates remain low. Results: We introduce in this paper a novel paradigm (... | {
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2411.14465 | Testing Uncertainty of Large Language Models for Physics Knowledge and
Reasoning | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have gained significant popularity in recent years for their ability to answer questions in various fields. However, these models have a tendency to "hallucinate" their responses, making it challenging to evaluate their performance. A major challenge is determining how to assess the certain... | {
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2411.14466 | Learning to Ask: Conversational Product Search via Representation
Learning | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Online shopping platforms, such as Amazon and AliExpress, are increasingly prevalent in society, helping customers purchase products conveniently. With recent progress in natural language processing, researchers and practitioners shift their focus from traditional product search to conversational product search. Conver... | {
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2411.14467 | Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device
Triggers for Insect Camera Traps | [
"q-bio.QM",
"cs.CV",
"cs.LG",
"eess.IV"
] | Camera traps, combined with AI, have emerged as a way to achieve automated, scalable biodiversity monitoring. However, the passive infrared (PIR) sensors that trigger camera traps are poorly suited for detecting small, fast-moving ectotherms such as insects. Insects comprise over half of all animal species and are key ... | {
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2411.14468 | A Neural Network Training Method Based on Distributed PID Control | [
"cs.LG",
"cs.AI",
"cs.NE"
] | In the previous article, we introduced a neural network framework based on symmetric differential equations. This novel framework exhibits complete symmetry, endowing it with perfect mathematical properties. While we have examined some of the system's mathematical characteristics, a detailed discussion of the network t... | {
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2411.14469 | Popular LLMs Amplify Race and Gender Disparities in Human Mobility | [
"cs.CL",
"cs.AI"
] | As large language models (LLMs) are increasingly applied in areas influencing societal outcomes, it is critical to understand their tendency to perpetuate and amplify biases. This study investigates whether LLMs exhibit biases in predicting human mobility -- a fundamental human behavior -- based on race and gender. Usi... | {
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2411.14471 | Leveraging Gene Expression Data and Explainable Machine Learning for
Enhanced Early Detection of Type 2 Diabetes | [
"q-bio.GN",
"cs.AI"
] | Diabetes, particularly Type 2 diabetes (T2D), poses a substantial global health burden, compounded by its associated complications such as cardiovascular diseases, kidney failure, and vision impairment. Early detection of T2D is critical for improving healthcare outcomes and optimizing resource allocation. In this stud... | {
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2411.14472 | Exploring the Potential Role of Generative AI in the TRAPD Procedure for
Survey Translation | [
"cs.CL",
"stat.AP",
"stat.ME"
] | This paper explores and assesses in what ways generative AI can assist in translating survey instruments. Writing effective survey questions is a challenging and complex task, made even more difficult for surveys that will be translated and deployed in multiple linguistic and cultural settings. Translation errors can b... | {
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2411.14473 | Large Language Model for Qualitative Research -- A Systematic Mapping
Study | [
"cs.CL",
"cs.AI"
] | The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformati... | {
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2411.14474 | Attention-guided Spectrogram Sequence Modeling with CNNs for Music Genre
Classification | [
"cs.SD",
"cs.CV",
"cs.LG",
"eess.AS"
] | Music genre classification is a critical component of music recommendation systems, generation algorithms, and cultural analytics. In this work, we present an innovative model for classifying music genres using attention-based temporal signature modeling. By processing spectrogram sequences through Convolutional Neural... | {
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2411.14476 | StreetviewLLM: Extracting Geographic Information Using a
Chain-of-Thought Multimodal Large Language Model | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Geospatial predictions are crucial for diverse fields such as disaster management, urban planning, and public health. Traditional machine learning methods often face limitations when handling unstructured or multi-modal data like street view imagery. To address these challenges, we propose StreetViewLLM, a novel framew... | {
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2411.14478 | Why you don't overfit, and don't need Bayes if you only train for one
epoch | [
"cs.LG"
] | Here, we show that in the data-rich setting where you only train on each datapoint once (or equivalently, you only train for one epoch), standard "maximum likelihood" training optimizes the true data generating process (DGP) loss, which is equivalent to the test loss. Further, we show that the Bayesian model average op... | {
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2411.14479 | GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via
Reinforcement Learning | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing (NLP) tasks due to their extensive general knowledge of the world. Recent works discovered that the performance of LLMs is heavily dependent on the input prompt. However, prompt engineering is usually done m... | {
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2411.14480 | Associative Knowledge Graphs for Efficient Sequence Storage and
Retrieval | [
"cs.AI",
"cs.DB"
] | This paper presents a novel approach for constructing associative knowledge graphs that are highly effective for storing and recognizing sequences. The graph is created by representing overlapping sequences of objects, as tightly connected clusters within the larger graph. Individual objects (represented as nodes) can ... | {
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2411.14483 | Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat | [
"cs.CL",
"cs.AI"
] | Deciding which large language model (LLM) to use is a complex challenge. Pairwise ranking has emerged as a new method for evaluating human preferences for LLMs. This approach entails humans evaluating pairs of model outputs based on a predefined criterion. By collecting these comparisons, a ranking can be constructed u... | {
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2411.14484 | Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach | [
"cs.CL",
"cs.AI"
] | Previous work has attempted to boost Large Language Model (LLM) performance on planning and scheduling tasks through a variety of prompt engineering techniques. While these methods can work within the distributions tested, they are neither robust nor predictable. This limitation can be addressed through compound LLM ar... | {
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2411.14485 | Mediating Modes of Thought: LLM's for design scripting | [
"cs.CL",
"cs.AI",
"cs.HC"
] | Architects adopt visual scripting and parametric design tools to explore more expansive design spaces (Coates, 2010), refine their thinking about the geometric logic of their design (Woodbury, 2010), and overcome conventional software limitations (Burry, 2011). Despite two decades of effort to make design scripting mor... | {
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} |
2411.14486 | The Impossible Test: A 2024 Unsolvable Dataset and A Chance for an AGI
Quiz | [
"cs.CL",
"cs.AI"
] | This research introduces a novel evaluation framework designed to assess large language models' (LLMs) ability to acknowledge uncertainty on 675 fundamentally unsolvable problems. Using a curated dataset of graduate-level grand challenge questions with intentionally unknowable answers, we evaluated twelve state-of-the-... | {
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} |
2411.14487 | Ensuring Safety and Trust: Analyzing the Risks of Large Language Models
in Medicine | [
"cs.CL",
"cs.AI",
"cs.CY"
] | The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with using LLMs in medical applications have not been systematically characterized. We propose using five key principles for safe and trustworth... | {
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2411.14489 | GhostRNN: Reducing State Redundancy in RNN with Cheap Operations | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] | Recurrent neural network (RNNs) that are capable of modeling long-distance dependencies are widely used in various speech tasks, eg., keyword spotting (KWS) and speech enhancement (SE). Due to the limitation of power and memory in low-resource devices, efficient RNN models are urgently required for real-world applicati... | {
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} |
2411.14491 | A Survey on Human-Centric LLMs | [
"cs.CL",
"cs.AI"
] | The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally performed by humans, namely those involving cognition, decision-making, an... | {
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} |
2411.14493 | From Statistical Methods to Pre-Trained Models; A Survey on Automatic
Speech Recognition for Resource Scarce Urdu Language | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Automatic Speech Recognition (ASR) technology has witnessed significant advancements in recent years, revolutionizing human-computer interactions. While major languages have benefited from these developments, lesser-resourced languages like Urdu face unique challenges. This paper provides an extensive exploration of th... | {
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2411.14494 | dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph | [
"cs.CV"
] | A facial morph is an image created by combining two face images pertaining to two distinct identities. Face demorphing inverts the process and tries to recover the original images constituting a facial morph. While morph attack detection (MAD) techniques can be used to flag morph images, they do not divulge any visual ... | {
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} |
2411.14495 | Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models | [
"cs.CV"
] | Test-time adaptation (TTA) of 3D point clouds is crucial for mitigating discrepancies between training and testing samples in real-world scenarios, particularly when handling corrupted point clouds. LiDAR data, for instance, can be affected by sensor failures or environmental factors, causing domain gaps. Adapting mode... | {
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} |
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