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272881259 | 2409.16346 | 2024-09-24 | Scalable quantum dynamics compilation via quantum machine learning | Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared to deterministic methods such as Trotterization, variational quantum compilation (VQC) methods emplo... | [
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272832411 | 2409.16099 | 2024-09-24 | Neuromorphic Drone Detection: an Event-RGB Multimodal Approach | In recent years, drone detection has quickly become a subject of extreme interest: the potential for fast-moving objects of contained dimensions to be used for malicious intents or even terrorist attacks has posed attention to the necessity for precise and resilient systems for detecting and identifying such elements. ... | [
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272881089 | 2409.16416 | 2024-09-24 | Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity | Large Language Models (LLMs) have demonstrated impressive performance in software engineering tasks. However, improving their accuracy in generating correct and reliable code remains challenging. Numerous prompt engineering techniques (PETs) have been developed to address this, but no single approach is universally opt... | [
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272881141 | 2409.16407 | 2024-09-24 | Towards Representation Learning for Weighting Problems in Design-Based Causal Inference | Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on des... | [
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272832071 | 2409.16083 | 2024-09-24 | Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients | Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia and is associated with increased morbidity and mortality. The effectiveness of current clinical interventions for AF is often limited by an incomplete understanding of the atrial anatomical structures that sustain this arrhythmia. Late Gadolinium... | [
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272831945 | 2409.16032 | 2024-09-24 | Deep chroma compression of tone-mapped images | Acquisition of high dynamic range (HDR) images is thriving due to the increasing use of smart devices and the demand for high-quality output. Extensive research has focused on developing methods for reducing the luminance range in HDR images using conventional and deep learning-based tone mapping operators to enable ac... | [
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272832009 | 2409.15759 | 2024-09-24 | VoiceGuider: Enhancing Out-of-Domain Performance in Parameter-Efficient Speaker-Adaptive Text-to-Speech via Autoguidance | When applying parameter-efficient finetuning via LoRA onto speaker adaptive text-to-speech models, adaptation performance may decline compared to full-finetuned counterparts, especially for out-of-domain speakers. Here, we propose VoiceGuider, a parameter-efficient speaker adaptive text-to-speech system reinforced with... | [
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272831754 | 2409.15843 | 2024-09-24 | From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant | In online education, innovative tools are crucial for enhancing learning outcomes. SAM (Study with AI Mentor) is an advanced platform that integrates educational videos with a context-aware chat interface powered by large language models. SAM encourages students to ask questions and explore unclear concepts in real tim... | [
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272881215 | 2409.16636 | 2024-09-25 | Training Language Models to Win Debates with Self-Play Improves Judge Accuracy | We test the robustness of debate as a method of scalable oversight by training models to debate with data generated via self-play. In a long-context reading comprehension task, we find that language model based evaluators answer questions more accurately when judging models optimized to win debates. By contrast, we fin... | [
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272880656 | 2409.16630 | 2024-09-25 | Stochastic Subsampling With Average Pooling | Regularization of deep neural networks has been an important issue to achieve higher generalization performance without overfitting problems. Although the popular method of Dropout provides a regularization effect, it causes inconsistent properties in the output, which may degrade the performance of deep neural network... | [
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272881466 | 2409.16766 | 2024-09-25 | Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning | Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interference (other sources, noise, etc.). In this work, we address a p... | [
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272881135 | 2409.16999 | 2024-09-25 | WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks | Robotic waste sorting poses significant challenges in both perception and manipulation, given the extreme variability of objects that should be recognized on a cluttered conveyor belt. While deep learning has proven effective in solving complex tasks, the necessity for extensive data collection and labeling limits its ... | [
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272829763 | 2409.17001 | 2024-09-25 | Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation | Optical flow has made great progress in clean scenes, while suffers degradation under adverse weather due to the violation of the brightness constancy and gradient continuity assumptions of optical flow. Typically, existing methods mainly adopt domain adaptation to transfer motion knowledge from clean to degraded domai... | [
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272881220 | 2409.17066 | 2024-09-25 | VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models | Scaling model size significantly challenges the deployment and inference of Large Language Models (LLMs). Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits). It reduces memory requirements, optimizes storage costs, and decreas... | [
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272881237 | 2409.16647 | 2024-09-25 | Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data | Due to scarcity of time-series data annotated with descriptive texts, training a model to generate descriptive texts for time-series data is challenging. In this study, we propose a method to systematically generate domain-independent descriptive texts from time-series data. We identify two distinct approaches for crea... | [
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272880984 | 2409.16845 | 2024-09-25 | IRASNet: Improved Feature-Level Clutter Reduction for Domain Generalized SAR-ATR | Recently, computer-aided design models and electromagnetic simulations have been used to augment synthetic aperture radar (SAR) data for deep learning. However, an automatic target recognition (ATR) model struggles with domain shift when using synthetic data because the model learns specific clutter patterns present in... | [
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272881267 | 2409.16956 | 2024-09-25 | Informed deep hierarchical classification: a non-standard analysis inspired approach | This work proposes a novel approach to the deep hierarchical classification task, i.e., the problem of classifying data according to multiple labels organized in a rigid parent-child structure. It consists in a multi-output deep neural network equipped with specific projection operators placed before each output layer.... | [
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272881297 | 2409.17144 | 2024-09-25 | Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization | Training machine learning models based on neural networks requires large datasets, which may contain sensitive information. The models, however, should not expose private information from these datasets. Differentially private SGD [DP-SGD] requires the modification of the standard stochastic gradient descent [SGD] algo... | [
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272881431 | 2409.16619 | 2024-09-25 | CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models | The rapid spread of diverse information on online social platforms has prompted both academia and industry to realize the importance of predicting content popularity, which could benefit a wide range of applications, such as recommendation systems and strategic decision-making. Recent works mainly focused on extracting... | [
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272880728 | 2409.16639 | 2024-09-25 | Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware | Despite being the most popular privacy-enhancing network, Tor is increasingly adopted by cybercriminals to obfuscate malicious traffic, hindering the identification of malware-related communications between compromised devices and Command and Control (C&C) servers. This malicious traffic can induce congestion and reduc... | [
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272881068 | 2409.16728 | 2024-09-25 | SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentation | Semi-supervised medical image segmentation (SSMIS) has been demonstrated the potential to mitigate the issue of limited medical labeled data. However, confirmation and cognitive biases may affect the prevalent teacher-student based SSMIS methods due to erroneous pseudo-labels. To tackle this challenge, we improve the m... | [
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272881037 | 2409.16651 | 2024-09-25 | Learning Representation for Multitask learning through Self Supervised Auxiliary learning | Multi-task learning is a popular machine learning approach that enables simultaneous learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the hard parameter sharing approach, an encoder shared through multiple tasks generates data representations passed to task-specific predictors.... | [
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272881449 | 2409.16760 | 2024-09-25 | Enhancing Automatic Keyphrase Labelling with Text-to-Text Transfer Transformer (T5) Architecture: A Framework for Keyphrase Generation and Filtering | Automatic keyphrase labelling stands for the ability of models to retrieve words or short phrases that adequately describe documents' content. Previous work has put much effort into exploring extractive techniques to address this task; however, these methods cannot produce keyphrases not found in the text. Given this l... | [
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259290434 | 2409.17090 | 2024-09-25 | Locally Regularized Sparse Graph by Fast Proximal Gradient Descent | Sparse graphs built by sparse representation has been demonstrated to be effective in clustering high-dimensional data. Albeit the compelling empirical performance, the vanilla sparse graph ignores the geometric information of the data by performing sparse representation for each datum separately. In order to obtain a ... | [
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272881106 | 2409.16684 | 2024-09-25 | Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning | Graph unlearning, which aims to eliminate the influence of specific nodes, edges, or attributes from a trained Graph Neural Network (GNN), is essential in applications where privacy, bias, or data obsolescence is a concern. However, existing graph unlearning techniques often necessitate additional training on the remai... | [
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272880831 | 2409.16706 | 2024-09-25 | Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translation | This paper proposes Pix2Next, a novel image-to-image translation framework designed to address the challenge of generating high-quality Near-Infrared (NIR) images from RGB inputs. Our approach leverages a state-of-the-art Vision Foundation Model (VFM) within an encoder-decoder architecture, incorporating cross-attentio... | [
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272881349 | 2409.16850 | 2024-09-25 | Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms | We present a novel method for scene change detection that leverages the robust feature extraction capabilities of a visual foundational model, DINOv2, and integrates full-image cross-attention to address key challenges such as varying lighting, seasonal variations, and viewpoint differences. In order to effectively lea... | [
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272910800 | 2409.17315 | 2024-09-25 | KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation | The integration of privacy measures, including differential privacy techniques, ensures a provable privacy guarantee for the synthetic data. However, challenges arise for Generative Deep Learning models when tasked with generating realistic data, especially in critical domains such as Cybersecurity and Healthcare. Gene... | [
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272880680 | 2409.16922 | 2024-09-25 | Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry | In this paper, we develop a theory about the relationship between invariant and equivariant maps with regard to a group $G$. We then leverage this theory in the context of deep neural networks with group symmetries in order to obtain novel insight into their mechanisms. More precisely, we establish a one-to-one relatio... | [
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272880697 | 2409.16865 | 2024-09-25 | Linking in Style: Understanding learned features in deep learning models | Convolutional neural networks (CNNs) learn abstract features to perform object classification, but understanding these features remains challenging due to difficult-to-interpret results or high computational costs. We propose an automatic method to visualize and systematically analyze learned features in CNNs. Specific... | [
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273186965 | 2410.03706 | 2024-09-25 | Topological Foundations of Reinforcement Learning | The goal of this work is to serve as a foundation for deep studies of the topology of state, action, and policy spaces in reinforcement learning. By studying these spaces from a mathematical perspective, we expect to gain more insight into how to build better algorithms to solve decision problems. Therefore, we focus o... | [
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272911175 | 2409.17308 | 2024-09-25 | Consistent estimation of generative model representations in the data kernel perspective space | Generative models, such as large language models and text-to-image diffusion models, produce relevant information when presented a query. Different models may produce different information when presented the same query. As the landscape of generative models evolves, it is important to develop techniques to study and an... | [
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272911262 | 2409.17353 | 2024-09-25 | Internalizing ASR with Implicit Chain of Thought for Efficient Speech-to-Speech Conversational LLM | Current speech-based LLMs are predominantly trained on extensive ASR and TTS datasets, excelling in tasks related to these domains. However, their ability to handle direct speech-to-speech conversations remains notably constrained. These models often rely on an ASR-to-TTS chain-of-thought pipeline, converting speech in... | [
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272881002 | 2409.16787 | 2024-09-25 | Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution | Research in Explainable Artificial Intelligence (XAI) is increasing, aiming to make deep learning models more transparent. Most XAI methods focus on justifying the decisions made by Artificial Intelligence (AI) systems in security-relevant applications. However, relatively little attention has been given to using these... | [
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272911016 | 2409.17316 | 2024-09-25 | Bi-TTA: Bidirectional Test-Time Adapter for Remote Physiological Measurement | Remote photoplethysmography (rPPG) is gaining prominence for its non-invasive approach to monitoring physiological signals using only cameras. Despite its promise, the adaptability of rPPG models to new, unseen domains is hindered due to the environmental sensitivity of physiological signals. To address this, we pionee... | [
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272881393 | 2409.16870 | 2024-09-25 | Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability | Generalized Additive Models (GAMs) offer a balance between performance and interpretability in machine learning. The interpretability aspect of GAMs is expressed through shape plots, representing the model's decision-making process. However, the visual properties of these plots, e.g. number of kinks (number of local ma... | [
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272880827 | 2409.16576 | 2024-09-25 | FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search | Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. We present Fusi... | [
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272880811 | 2409.16914 | 2024-09-25 | Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness | The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of LLM-generated text. Zero-shot detectors, due to their training-free nature, have received considerable attention and notable success. In this paper, we identify a new feature, token cohesi... | [
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272910610 | 2409.17359 | 2024-09-25 | Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace | Predicting flight trajectories is a research area that holds significant merit. In this paper, we propose a data-driven learning framework, that leverages the predictive and feature extraction capabilities of the mixture models and seq2seq-based neural networks while addressing prevalent challenges caused by error prop... | [
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272987688 | 2409.19022 | 2024-09-25 | Application of AI-based Models for Online Fraud Detection and Analysis | Fraud is a prevalent offence that extends beyond financial loss, causing psychological and physical harm to victims. The advancements in online communication technologies alowed for online fraud to thrive in this vast network, with fraudsters increasingly using these channels for deception. With the progression of tech... | [
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272881071 | 2409.17085 | 2024-09-25 | Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation | State-of-the-art computer vision tasks, like monocular depth estimation (MDE), rely heavily on large, modern Transformer-based architectures. However, their application in safety-critical domains demands reliable predictive performance and uncertainty quantification. While Bayesian neural networks provide a conceptuall... | [
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271961994 | 2410.02819 | 2024-09-25 | Physics-Informed Graph-Mesh Networks for PDEs: A hybrid approach for complex problems | The recent rise of deep learning has led to numerous applications, including solving partial differential equations using Physics-Informed Neural Networks. This approach has proven highly effective in several academic cases. However, their lack of physical invariances, coupled with other significant weaknesses, such as... | [
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272880913 | 2409.16925 | 2024-09-25 | Game4Loc: A UAV Geo-Localization Benchmark from Game Data | The vision-based geo-localization technology for UAV, serving as a secondary source of GPS information in addition to the global navigation satellite systems (GNSS), can still operate independently in the GPS-denied environment. Recent deep learning based methods attribute this as the task of image matching and retriev... | [
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272911114 | 2409.17312 | 2024-09-25 | BabyLlama-2: Ensemble-Distilled Models Consistently Outperform Teachers With Limited Data | We present BabyLlama-2, a 345 million parameter model distillation-pretrained from two teachers on a 10 million word corpus for the BabyLM competition. On BLiMP and SuperGLUE benchmarks, BabyLlama-2 outperforms baselines trained on both 10 and 100 million word datasets with the same data mix, as well as its teacher mod... | [
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272910576 | 2409.17376 | 2024-09-25 | Optical Lens Attack on Deep Learning Based Monocular Depth Estimation | Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a single-camera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a detected obstacle or changing lanes to avoid collision. In this paper, we ... | [
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272911231 | 2409.17228 | 2024-09-25 | Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems | We introduce Disk2Planet, a machine learning-based tool to infer key parameters in disk-planet systems from observed protoplanetary disk structures. Disk2Planet takes as input the disk structures in the form of two-dimensional density and velocity maps, and outputs disk and planet properties, that is, the Shakura--Suny... | [
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272880835 | 2409.16572 | 2024-09-25 | Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration | Geological carbon sequestration (GCS) involves injecting CO$_2$ into subsurface geological formations for permanent storage. Numerical simulations could guide decisions in GCS projects by predicting CO$_2$ migration pathways and the pressure distribution in storage formation. However, these simulations are often comput... | [
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272881026 | 2409.17116 | 2024-09-25 | Hierarchical Tri-manual Planning for Vision-assisted Fruit Harvesting with Quadrupedal Robots | This paper addresses the challenge of developing a multi-arm quadrupedal robot capable of efficiently harvesting fruit in complex, natural environments. To overcome the inherent limitations of traditional bimanual manipulation, we introduce the first three-arm quadrupedal robot LocoHarv-3 and propose a novel hierarchic... | [
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272911444 | 2409.17431 | 2024-09-25 | On Extending Direct Preference Optimization to Accommodate Ties | We derive and investigate two DPO variants that explicitly model the possibility of declaring a tie in pair-wise comparisons. We replace the Bradley-Terry model in DPO with two well-known modeling extensions, by Rao and Kupper and by Davidson, that assign probability to ties as alternatives to clear preferences. Our ex... | [
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272880882 | 2409.16806 | 2024-09-25 | Topological SLAM in colonoscopies leveraging deep features and topological priors | We introduce ColonSLAM, a system that combines classical multiple-map metric SLAM with deep features and topological priors to create topological maps of the whole colon. The SLAM pipeline by itself is able to create disconnected individual metric submaps representing locations from short video subsections of the colon... | [
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272881473 | 2409.17055 | 2024-09-25 | DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data | Real-life medical data is often multimodal and incomplete, fueling the growing need for advanced deep learning models capable of integrating them efficiently. The use of diverse modalities, including histopathology slides, MRI, and genetic data, offers unprecedented opportunities to improve prognosis prediction and to ... | [
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272881341 | 2409.17045 | 2024-09-25 | GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design | We provide a dataset for enabling Deep Generative Models (DGMs) in engineering design and propose methods to automate data labeling by utilizing large-scale foundation models. GeoBiked is curated to contain 4 355 bicycle images, annotated with structural and technical features and is used to investigate two automated l... | [
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272881049 | 2409.16631 | 2024-09-25 | Enhancing Nighttime UAV Tracking with Light Distribution Suppression | Visual object tracking has boosted extensive intelligent applications for unmanned aerial vehicles (UAVs). However, the state-of-the-art (SOTA) enhancers for nighttime UAV tracking always neglect the uneven light distribution in low-light images, inevitably leading to excessive enhancement in scenarios with complex ill... | [
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272881308 | 2409.16830 | 2024-09-25 | OffRIPP: Offline RL-based Informative Path Planning | Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are ris... | [
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272880963 | 2409.16590 | 2024-09-25 | Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract) | Both Transformer and Graph Neural Networks (GNNs) have been employed in the domain of learning to rank (LTR). However, these approaches adhere to two distinct yet complementary problem formulations: ranking score regression based on query-webpage pairs, and link prediction within query-webpage bipartite graphs, respect... | [
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272911288 | 2409.17277 | 2024-09-25 | Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles | Accurate trajectory prediction is essential for the safe operation of autonomous vehicles in real-world environments. Even well-trained machine learning models may produce unreliable predictions due to discrepancies between training data and real-world conditions encountered during inference. In particular, the trainin... | [
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272880718 | 2409.16594 | 2024-09-25 | Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract) | Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages from retrieved content based on input queries. However, traditional LTR models encounter two principal obstacles that lead to suboptimal performance: (1) the lack of well-annotated query-webpage pairs with ranking scores covering... | [
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272880859 | 2409.16866 | 2024-09-25 | Risk-averse learning with delayed feedback | In real-world scenarios, risk-averse learning is valuable for mitigating potential adverse outcomes. However, the delayed feedback makes it challenging to assess and manage risk effectively. In this paper, we investigate risk-averse learning using Conditional Value at Risk (CVaR) as risk measure, while incorporating fe... | [
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272881440 | 2409.16592 | 2024-09-25 | MambaJSCC: Adaptive Deep Joint Source-Channel Coding with Generalized State Space Model | Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves state-of-the-art performance with low computational and parameter overhead. MambaJSCC utilizes the visu... | [
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272881400 | 2409.16938 | 2024-09-25 | Generative Object Insertion in Gaussian Splatting with a Multi-View Diffusion Model | Generating and inserting new objects into 3D content is a compelling approach for achieving versatile scene recreation. Existing methods, which rely on SDS optimization or single-view inpainting, often struggle to produce high-quality results. To address this, we propose a novel method for object insertion in 3D conten... | [
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272881436 | 2409.16769 | 2024-09-25 | Super Level Sets and Exponential Decay: A Synergistic Approach to Stable Neural Network Training | The objective of this paper is to enhance the optimization process for neural networks by developing a dynamic learning rate algorithm that effectively integrates exponential decay and advanced anti-overfitting strategies. Our primary contribution is the establishment of a theoretical framework where we demonstrate tha... | [
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272881152 | 2409.16832 | 2024-09-25 | Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing | In the realm of emerging real-time networked applications like cyber-physical systems (CPS), the Age of Information (AoI) has merged as a pivotal metric for evaluating the timeliness. To meet the high computational demands, such as those in intelligent manufacturing within CPS, mobile edge computing (MEC) presents a pr... | [
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272880949 | 2409.17093 | 2024-09-25 | BitQ: Tailoring Block Floating Point Precision for Improved DNN Efficiency on Resource-Constrained Devices | Deep neural networks (DNNs) are powerful for cognitive tasks such as image classification, object detection, and scene segmentation. One drawback however is the significant high computational complexity and memory consumption, which makes them unfeasible to run real-time on embedded platforms because of the limited har... | [
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274514412 | 2409.17146 | 2024-09-25 | Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models | Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed VLMs into open ones. As a result, the community has been missing foundational knowledge about how... | [
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272881450 | 2409.16720 | 2024-09-25 | Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning | Recent innovations in autonomous drones have facilitated time-optimal flight in single-drone configurations, and enhanced maneuverability in multi-drone systems by applying optimal control and learning-based methods. However, few studies have achieved time-optimal motion planning for multi-drone systems, particularly d... | [
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272880771 | 2409.16621 | 2024-09-25 | Entailment-Driven Privacy Policy Classification with LLMs | While many online services provide privacy policies for end users to read and understand what personal data are being collected, these documents are often lengthy and complicated. As a result, the vast majority of users do not read them at all, leading to data collection under uninformed consent. Several attempts have ... | [
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272880649 | 2409.16911 | 2024-09-25 | Pruning Multilingual Large Language Models for Multilingual Inference | Multilingual large language models (MLLMs), trained on multilingual balanced data, demonstrate better zero-shot learning performance in non-English languages compared to large language models trained on English-dominant data. However, the disparity in performance between English and non-English languages remains a chal... | [
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272911438 | 2409.17397 | 2024-09-25 | Building Multilingual Datasets for Predicting Mental Health Severity through LLMs: Prospects and Challenges | Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications. To address this problem, we present a novel multilingual adaptat... | [
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272880887 | 2409.16797 | 2024-09-25 | Scalable Ensemble Diversification for OOD Generalization and Detection | Training a diverse ensemble of models has several practical applications such as providing candidates for model selection with better out-of-distribution (OOD) generalization, and enabling the detection of OOD samples via Bayesian principles. An existing approach to diverse ensemble training encourages the models to di... | [
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272881405 | 2409.16767 | 2024-09-25 | Exploring Information-Theoretic Metrics Associated with Neural Collapse in Supervised Training | In this paper, we introduce matrix entropy as an analytical tool for studying supervised learning, investigating the information content of data representations and classification head vectors, as well as the dynamic interactions between them during the supervised learning process. Our experimental results reveal that ... | [
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272881061 | 2409.16944 | 2024-09-25 | Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM | We introduce Go-SLAM, a novel framework that utilizes 3D Gaussian Splatting SLAM to reconstruct dynamic environments while embedding object-level information within the scene representations. This framework employs advanced object segmentation techniques, assigning a unique identifier to each Gaussian splat that corres... | [
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