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272881094 | 2409.16653 | 2024-09-25 | The Credibility Transformer | Inspired by the large success of Transformers in Large Language Models, these architectures are increasingly applied to tabular data. This is achieved by embedding tabular data into low-dimensional Euclidean spaces resulting in similar structures as time-series data. We introduce a novel credibility mechanism to this T... | [
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272881025 | 2409.16685 | 2024-09-25 | Skyeyes: Ground Roaming using Aerial View Images | Integrating aerial imagery-based scene generation into applications like autonomous driving and gaming enhances realism in 3D environments, but challenges remain in creating detailed content for occluded areas and ensuring real-time, consistent rendering. In this paper, we introduce Skyeyes, a novel framework that can ... | [
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272881051 | 2409.16623 | 2024-09-25 | On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction | Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information popularity lies in subtly modeling the underlying temporal information diffusion process behind observed events of an information cascade, s... | [
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272881287 | 2409.16668 | 2024-09-25 | Topic-aware Causal Intervention for Counterfactual Detection | Counterfactual statements, which describe events that did not or cannot take place, are beneficial to numerous NLP applications. Hence, we consider the problem of counterfactual detection (CFD) and seek to enhance the CFD models. Previous models are reliant on clue phrases to predict counterfactuality, so they suffer f... | [
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272910617 | 2409.17430 | 2024-09-25 | A Hierarchical Gradient Tracking Algorithm for Mitigating Subnet-Drift in Fog Learning Networks | Federated learning (FL) encounters scalability challenges when implemented over fog networks that do not follow FL's conventional star topology architecture. Semi-decentralized FL (SD-FL) has proposed a solution for device-to-device (D2D) enabled networks that divides model cooperation into two stages: at the lower sta... | [
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272881018 | 2409.16779 | 2024-09-25 | LLaMa-SciQ: An Educational Chatbot for Answering Science MCQ | Large Language Models (LLMs) often struggle with tasks requiring mathematical reasoning, particularly multiple-choice questions (MCQs). To address this issue, we developed LLaMa-SciQ, an educational chatbot designed to assist college students in solving and understanding MCQs in STEM fields. We begin by fine-tuning and... | [
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272881403 | 2409.17087 | 2024-09-25 | SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking | Climate change and increasing droughts pose significant challenges to water resource management around the world. These problems lead to severe water shortages that threaten ecosystems, agriculture, and human communities. To advance the fight against these challenges, we present a new dataset, SEN12-WATER, along with a... | [
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272881277 | 2409.16788 | 2024-09-25 | Mitigating the Bias of Large Language Model Evaluation | Recently, there has been a trend of evaluating the Large Language Model (LLM) quality in the flavor of LLM-as-a-Judge, namely leveraging another LLM to evaluate the current output quality. However, existing judges are proven to be biased, namely they would favor answers which present better superficial quality (such as... | [
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272910821 | 2409.17356 | 2024-09-25 | A vision-based framework for human behavior understanding in industrial assembly lines | This paper introduces a vision-based framework for capturing and understanding human behavior in industrial assembly lines, focusing on car door manufacturing. The framework leverages advanced computer vision techniques to estimate workers' locations and 3D poses and analyze work postures, actions, and task progress. A... | [
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272881245 | 2409.16675 | 2024-09-25 | CryptoTrain: Fast Secure Training on Encrypted Dataset | Secure training, while protecting the confidentiality of both data and model weights, typically incurs significant training overhead. Traditional Fully Homomorphic Encryption (FHE)-based non-inter-active training models are heavily burdened by computationally demanding bootstrapping. To develop an efficient secure trai... | [
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272987409 | 2409.19020 | 2024-09-25 | DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications | The scarcity of domain-specific dialogue datasets limits the development of dialogue systems across applications. Existing research is constrained by general or niche datasets that lack sufficient scale for training dialogue systems. To address this gap, we introduce DiaSynth - a synthetic dialogue generation framework... | [
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272880696 | 2409.16997 | 2024-09-25 | INT-FlashAttention: Enabling Flash Attention for INT8 Quantization | As the foundation of large language models (LLMs), self-attention module faces the challenge of quadratic time and memory complexity with respect to sequence length. FlashAttention accelerates attention computation and reduces its memory usage by leveraging the GPU memory hierarchy. A promising research direction is to... | [
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272880668 | 2409.16950 | 2024-09-25 | Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion | By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models are effective in predicting long-horizon state trajectories in deterministic environments, they face challenges in dynamic settings with movi... | [
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272910911 | 2409.17200 | 2024-09-25 | A random measure approach to reinforcement learning in continuous time | We present a random measure approach for modeling exploration, i.e., the execution of measure-valued controls, in continuous-time reinforcement learning (RL) with controlled diffusion and jumps. First, we consider the case when sampling the randomized control in continuous time takes place on a discrete-time grid and r... | [
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272910770 | 2409.17328 | 2024-09-25 | The poison of dimensionality | This paper advances the understanding of how the size of a machine learning model affects its vulnerability to poisoning, despite state-of-the-art defenses. Given isotropic random honest feature vectors and the geometric median (or clipped mean) as the robust gradient aggregator rule, we essentially prove that, perhaps... | [
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272910588 | 2409.17331 | 2024-09-25 | ChatCam: Empowering Camera Control through Conversational AI | Cinematographers adeptly capture the essence of the world, crafting compelling visual narratives through intricate camera movements. Witnessing the strides made by large language models in perceiving and interacting with the 3D world, this study explores their capability to control cameras with human language guidance.... | [
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272910946 | 2409.17335 | 2024-09-25 | Non-asymptotic Convergence of Training Transformers for Next-token Prediction | Transformers have achieved extraordinary success in modern machine learning due to their excellent ability to handle sequential data, especially in next-token prediction (NTP) tasks. However, the theoretical understanding of their performance in NTP is limited, with existing studies focusing mainly on asymptotic perfor... | [
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272881300 | 2409.17134 | 2024-09-25 | Streaming Neural Images | Implicit Neural Representations (INRs) are a novel paradigm for signal representation that have attracted considerable interest for image compression. INRs offer unprecedented advantages in signal resolution and memory efficiency, enabling new possibilities for compression techniques. However, the existing limitations ... | [
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274639361 | 2504.15822 | 2024-09-25 | Quantifying Source Speaker Leakage in One-to-One Voice Conversion | Using a multi-accented corpus of parallel utterances for use with commercial speech devices, we present a case study to show that it is possible to quantify a degree of confidence about a source speaker's identity in the case of one-to-one voice conversion. Following voice conversion using a HiFi-GAN vocoder, we compar... | [
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272911065 | 2409.17385 | 2024-09-25 | SSTP: Efficient Sample Selection for Trajectory Prediction | Trajectory prediction is a core task in autonomous driving. However, training advanced trajectory prediction models on existing large-scale datasets is both time-consuming and computationally expensive. More critically, these datasets are highly imbalanced in scenario density, with normal driving scenes (low-moderate t... | [
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272880867 | 2409.16838 | 2024-09-25 | Explicitly Modeling Pre-Cortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness | While convolutional neural networks (CNNs) excel at clean image classification, they struggle to classify images corrupted with different common corruptions, limiting their real-world applicability. Recent work has shown that incorporating a CNN front-end block that simulates some features of the primate primary visual... | [
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272911295 | 2409.17264 | 2024-09-25 | No Request Left Behind: Tackling Heterogeneity in Long-Context LLM Inference with Medha | Deploying million-token Large Language Models (LLMs) is challenging because production workloads are highly heterogeneous, mixing short queries and long documents. This heterogeneity, combined with the quadratic complexity of attention, creates severe convoy effects where long-running requests stall short, interactive ... | [
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272881392 | 2409.16658 | 2024-09-25 | Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts | In this work, we show the pre-trained language models return distinguishable generation probability and uncertainty distribution to unfaithfully hallucinated texts, regardless of their size and structure. By examining 24 models on 6 data sets, we find out that 88-98% of cases return statistically significantly distingu... | [
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272880722 | 2409.16646 | 2024-09-25 | Cross-Lingual and Cross-Cultural Variation in Image Descriptions | Do speakers of different languages talk differently about what they see? Behavioural and cognitive studies report cultural effects on perception; however, these are mostly limited in scope and hard to replicate. In this work, we conduct the first large-scale empirical study of cross-lingual variation in image descripti... | [
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272910598 | 2409.17363 | 2024-09-25 | Improving satellite imagery segmentation using multiple Sentinel-2 revisits | In recent years, analysis of remote sensing data has benefited immensely from borrowing techniques from the broader field of computer vision, such as the use of shared models pre-trained on large and diverse datasets. However, satellite imagery has unique features that are not accounted for in traditional computer visi... | [
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272880855 | 2409.16644 | 2024-09-25 | Enabling Auditory Large Language Models for Automatic Speech Quality Evaluation | Speech quality assessment typically requires evaluating audio from multiple aspects, such as mean opinion score (MOS) and speaker similarity (SIM) \etc., which can be challenging to cover using one small model designed for a single task. In this paper, we propose leveraging recently introduced auditory large language m... | [
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272881084 | 2409.16539 | 2024-09-25 | Context-aware and Style-related Incremental Decoding framework for Discourse-Level Literary Translation | This report outlines our approach for the WMT24 Discourse-Level Literary Translation Task, focusing on the Chinese-English language pair in the Constrained Track. Translating literary texts poses significant challenges due to the nuanced meanings, idiomatic expressions, and intricate narrative structures inherent in su... | [
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272910659 | 2409.17386 | 2024-09-25 | Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning | Unsupervised Multiplex Graph Learning (UMGL) aims to learn node representations on various edge types without manual labeling. However, existing research overlooks a key factor: the reliability of the graph structure. Real-world data often exhibit a complex nature and contain abundant task-irrelevant noise, severely co... | [
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272881097 | 2409.16973 | 2024-09-25 | Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization | Large language models (LLMs) have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge, particularly in on-device applications. Traditional methods often depend heavily on labeled datasets and can be resource-intensive. To address these... | [
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272881271 | 2409.16861 | 2024-09-25 | Limitations of (Procrustes) Alignment in Assessing Multi-Person Human Pose and Shape Estimation | We delve into the challenges of accurately estimating 3D human pose and shape in video surveillance scenarios. Beginning with the advocacy for metrics like W-MPJPE and W-PVE, which omit the (Procrustes) realignment step, to improve model evaluation, we then introduce RotAvat. This technique aims to enhance these metric... | [
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272911048 | 2409.17256 | 2024-09-25 | AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content | Video super-resolution (VSR) is a critical task for enhancing low-bitrate and low-resolution videos, particularly in streaming applications. While numerous solutions have been developed, they often suffer from high computational demands, resulting in low frame rates (FPS) and poor power efficiency, especially on mobile... | [
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272880931 | 2409.16984 | 2024-09-25 | AXCEL: Automated eXplainable Consistency Evaluation using LLMs | Large Language Models (LLMs) are widely used in both industry and academia for various tasks, yet evaluating the consistency of generated text responses continues to be a challenge. Traditional metrics like ROUGE and BLEU show a weak correlation with human judgment. More sophisticated metrics using Natural Language Inf... | [
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272910842 | 2409.17313 | 2024-09-25 | Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation | This study presents a novel evaluation framework for the Vision-Language Navigation (VLN) task. It aims to diagnose current models for various instruction categories at a finer-grained level. The framework is structured around the context-free grammar (CFG) of the task. The CFG serves as the basis for the problem decom... | [
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272911038 | 2409.17201 | 2024-09-25 | Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning | Federated learning (FL) has emerged as a method to preserve privacy in collaborative distributed learning. In FL, clients train AI models directly on their devices rather than sharing data with a centralized server, which can pose privacy risks. However, it has been shown that despite FL's partial protection of local d... | [
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272881293 | 2409.16783 | 2024-09-25 | Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction | Automated red teaming is an effective method for identifying misaligned behaviors in large language models (LLMs). Existing approaches, however, often focus primarily on improving attack success rates while overlooking the need for comprehensive test case coverage. Additionally, most of these methods are limited to sin... | [
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272881020 | 2409.16652 | 2024-09-25 | Progressive Representation Learning for Real-Time UAV Tracking | Visual object tracking has significantly promoted autonomous applications for unmanned aerial vehicles (UAVs). However, learning robust object representations for UAV tracking is especially challenging in complex dynamic environments, when confronted with aspect ratio change and occlusion. These challenges severely alt... | [
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272880989 | 2409.16570 | 2024-09-25 | Disentangling Questions from Query Generation for Task-Adaptive Retrieval | This paper studies the problem of information retrieval, to adapt to unseen tasks. Existing work generates synthetic queries from domain-specific documents to jointly train the retriever. However, the conventional query generator assumes the query as a question, thus failing to accommodate general search intents. A mor... | [
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272881342 | 2409.16907 | 2024-09-25 | An Adaptive Screen-Space Meshing Approach for Normal Integration | Reconstructing surfaces from normals is a key component of photometric stereo. This work introduces an adaptive surface triangulation in the image domain and afterwards performs the normal integration on a triangle mesh. Our key insight is that surface curvature can be computed from normals. Based on the curvature, we ... | [
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272880820 | 2409.17140 | 2024-09-25 | AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents | Multimodal large language models (MLLMs) have enabled LLM-based agents to directly interact with application user interfaces (UIs), enhancing agents' performance in complex tasks. However, these agents often suffer from high latency and low reliability due to the extensive sequential UI interactions. To address this is... | [
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272969291 | 2409.18153 | 2024-09-25 | Most Influential Subset Selection: Challenges, Promises, and Beyond | How can we attribute the behaviors of machine learning models to their training data? While the classic influence function sheds light on the impact of individual samples, it often fails to capture the more complex and pronounced collective influence of a set of samples. To tackle this challenge, we study the Most Infl... | [
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272880736 | 2409.16810 | 2024-09-25 | Inline Photometrically Calibrated Hybrid Visual SLAM | This paper presents an integrated approach to Visual SLAM, merging online sequential photometric calibration within a Hybrid direct-indirect visual SLAM (H-SLAM). Photometric calibration helps normalize pixel intensity values under different lighting conditions, and thereby improves the direct component of our H-SLAM. ... | [
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272880926 | 2409.16560 | 2024-09-25 | Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference | Large language models (LLMs) have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slow and costly. Speculative decoding has emerged as a promising solution, leveraging a smaller auxiliary model to draft future tokens, which a... | [
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