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272689759 | 2409.10478 | 2024-09-16 | Local SGD for Near-Quadratic Problems: Improving Convergence under Unconstrained Noise Conditions | Distributed optimization plays an important role in modern large-scale machine learning and data processing systems by optimizing the utilization of computational resources. One of the classical and popular approaches is Local Stochastic Gradient Descent (Local SGD), characterized by multiple local updates before avera... | [
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272689393 | 2409.10323 | 2024-09-16 | On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimization | We study the oracle complexity of nonsmooth nonconvex optimization, with the algorithm assumed to have access only to local function information. It has been shown by Davis, Drusvyatskiy, and Jiang (2023) that for nonsmooth Lipschitz functions satisfying certain regularity and strictness conditions, perturbed gradient ... | [
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272689780 | 2409.10033 | 2024-09-16 | Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs | LLMs have long demonstrated remarkable effectiveness in automatic program repair (APR), with OpenAI's ChatGPT being one of the most widely used models in this domain. Through continuous iterations and upgrades of GPT-family models, their performance in fixing bugs has already reached state-of-the-art levels. However, t... | [
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272689940 | 2409.10202 | 2024-09-16 | SteeredMarigold: Steering Diffusion Towards Depth Completion of Largely Incomplete Depth Maps | Even if the depth maps captured by RGB-D sensors deployed in real environments are often characterized by large areas missing valid depth measurements, the vast majority of depth completion methods still assumes depth values covering all areas of the scene. To address this limitation, we introduce SteeredMarigold, a tr... | [
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272689801 | 2409.10411 | 2024-09-16 | Assessing Privacy Compliance of Android Third-Party SDKs | Third-party Software Development Kits (SDKs) are widely adopted in Android app development, to effortlessly accelerate development pipelines and enhance app functionality. However, this convenience raises substantial concerns about unauthorized access to users' privacy-sensitive information, which could be further abus... | [
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272689514 | 2409.10327 | 2024-09-16 | Baking Relightable NeRF for Real-time Direct/Indirect Illumination Rendering | Relighting, which synthesizes a novel view under a given lighting condition (unseen in training time), is a must feature for immersive photo-realistic experience. However, real-time relighting is challenging due to high computation cost of the rendering equation which requires shape and material decomposition and visib... | [
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272689615 | 2409.10104 | 2024-09-16 | A Comparative Study of Open Source Computer Vision Models for Application on Small Data: The Case of CFRP Tape Laying | In the realm of industrial manufacturing, Artificial Intelligence (AI) is playing an increasing role, from automating existing processes to aiding in the development of new materials and techniques. However, a significant challenge arises in smaller, experimental processes characterized by limited training data availab... | [
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272689464 | 2409.10095 | 2024-09-16 | Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference | Autonomous driving systems require a comprehensive understanding of the environment, achieved by extracting visual features essential for perception, planning, and control. However, models trained solely on single-task objectives or generic datasets often lack the contextual information needed for robust performance in... | [
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272689697 | 2409.10259 | 2024-09-16 | Self-Updating Vehicle Monitoring Framework Employing Distributed Acoustic Sensing towards Real-World Settings | The recent emergence of Distributed Acoustic Sensing (DAS) technology has facilitated the effective capture of traffic-induced seismic data. The traffic-induced seismic wave is a prominent contributor to urban vibrations and contain crucial information to advance urban exploration and governance. However, identifying v... | [
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272690112 | 2409.09951 | 2024-09-16 | Optimal ablation for interpretability | Interpretability studies often involve tracing the flow of information through machine learning models to identify specific model components that perform relevant computations for tasks of interest. Prior work quantifies the importance of a model component on a particular task by measuring the impact of performing abla... | [
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272689100 | 2409.10048 | 2024-09-16 | Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic Environments | Although deep reinforcement learning (DRL) approaches in audio signal processing have seen substantial progress in recent years, audio-driven DRL for tasks such as navigation, gaze control and head-orientation control in the context of human-robot interaction have received little attention. Here, we propose an audio-dr... | [
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272693976 | 2409.10641 | 2024-09-16 | HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions | Video annotation is a critical and time-consuming task in computer vision research and applications. This paper presents a novel annotation pipeline that uses pre-extracted features and dimensionality reduction to accelerate the temporal video annotation process. Our approach uses Hierarchical Stochastic Neighbor Embed... | [
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272690041 | 2409.10173 | 2024-09-16 | jina-embeddings-v3: Multilingual Embeddings With Task LoRA | We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens. The model includes a set of task-specific Low-Rank Adaptation (LoRA) adapters to genera... | [
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272689170 | 2409.09916 | 2024-09-16 | SFR-RAG: Towards Contextually Faithful LLMs | Retrieval Augmented Generation (RAG), a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance, has emerged as a pivotal area in generative AI. The LLMs used in RAG applications are required to faithfully and completely comprehend the provide... | [
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272689778 | 2409.10206 | 2024-09-16 | Garment Attribute Manipulation with Multi-level Attention | In the rapidly evolving field of online fashion shopping, the need for more personalized and interactive image retrieval systems has become paramount. Existing methods often struggle with precisely manipulating specific garment attributes without inadvertently affecting others. To address this challenge, we propose GAM... | [
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272689290 | 2409.10445 | 2024-09-16 | Deep-Wide Learning Assistance for Insect Pest Classification | Accurate insect pest recognition plays a critical role in agriculture. It is a challenging problem due to the intricate characteristics of insects. In this paper, we present DeWi, novel learning assistance for insect pest classification. With a one-stage and alternating training strategy, DeWi simultaneously improves s... | [
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272694549 | 2409.10643 | 2024-09-16 | CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble | Machine Learning as a Service (MLaaS) is often provided as a pay-per-query, black-box system to clients. Such a black-box approach not only hinders open replication, validation, and interpretation of model results, but also makes it harder for white-hat researchers to identify vulnerabilities in the MLaaS systems. Mode... | [
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272689561 | 2409.10102 | 2024-09-16 | Trustworthiness in Retrieval-Augmented Generation Systems: A Survey | Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). While much of the current research in this field focuses on performance optimization, particularly in terms of accuracy and efficiency, the trustworthiness of RAG systems remains an area st... | [
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272689363 | 2409.09984 | 2024-09-16 | Convergence of Sharpness-Aware Minimization Algorithms using Increasing Batch Size and Decaying Learning Rate | The sharpness-aware minimization (SAM) algorithm and its variants, including gap guided SAM (GSAM), have been successful at improving the generalization capability of deep neural network models by finding flat local minima of the empirical loss in training. Meanwhile, it has been shown theoretically and practically tha... | [
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272689075 | 2409.10376 | 2024-09-16 | Leveraging Joint Spectral and Spatial Learning with MAMBA for Multichannel Speech Enhancement | In multichannel speech enhancement, effectively capturing spatial and spectral information across different microphones is crucial for noise reduction. Traditional methods, such as CNN or LSTM, attempt to model the temporal dynamics of full-band and sub-band spectral and spatial features. However, these approaches face... | [
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272690282 | 2409.10228 | 2024-09-16 | Robust Bird's Eye View Segmentation by Adapting DINOv2 | Extracting a Bird's Eye View (BEV) representation from multiple camera images offers a cost-effective, scalable alternative to LIDAR-based solutions in autonomous driving. However, the performance of the existing BEV methods drops significantly under various corruptions such as brightness and weather changes or camera ... | [
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272694406 | 2409.10583 | 2024-09-16 | Reinforcement Learning with Quasi-Hyperbolic Discounting | Reinforcement learning has traditionally been studied with exponential discounting or the average reward setup, mainly due to their mathematical tractability. However, such frameworks fall short of accurately capturing human behavior, which has a bias towards immediate gratification. Quasi-Hyperbolic (QH) discounting i... | [
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272689139 | 2409.10394 | 2024-09-16 | MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learning | Deep learning-based Magnetic Resonance (MR) reconstruction methods have focused on generating high-quality images but often overlook the impact on downstream tasks (e.g., segmentation) that utilize the reconstructed images. Cascading separately trained reconstruction network and downstream task network has been shown t... | [
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272552652 | 2409.10649 | 2024-09-16 | Visualizing Temporal Topic Embeddings with a Compass | Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly a... | [
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276939221 | 2409.16316 | 2024-09-16 | Surface solar radiation: AI satellite retrieval can outperform Heliosat and generalizes well to other climate zones | Accurate estimates of surface solar irradiance (SSI) are essential for solar resource assessments and solar energy forecasts in grid integration and building control applications. SSI estimates for spatially extended regions can be retrieved from geostationary satellites such as Meteosat. Traditional SSI satellite retr... | [
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272694652 | 2409.10582 | 2024-09-16 | WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency | Recent advancements in single image super-resolution have been predominantly driven by token mixers and transformer architectures. WaveMixSR utilized the WaveMix architecture, employing a two-dimensional discrete wavelet transform for spatial token mixing, achieving superior performance in super-resolution tasks with r... | [
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272694654 | 2409.11184 | 2024-09-16 | LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling | Learning compact and meaningful latent space representations has been shown to be very useful in generative modeling tasks for visual data. One particular example is applying Vector Quantization (VQ) in variational autoencoders (VQ-VAEs, VQ-GANs, etc.), which has demonstrated state-of-the-art performance in many modern... | [
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272690378 | 2409.10335 | 2024-09-16 | Phys3DGS: Physically-based 3D Gaussian Splatting for Inverse Rendering | We propose two novel ideas (adoption of deferred rendering and mesh-based representation) to improve the quality of 3D Gaussian splatting (3DGS) based inverse rendering. We first report a problem incurred by hidden Gaussians, where Gaussians beneath the surface adversely affect the pixel color in the volume rendering a... | [
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272690044 | 2409.09914 | 2024-09-16 | A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models | This work investigates two strategies for zero-shot non-intrusive speech assessment leveraging large language models. First, we explore the audio analysis capabilities of GPT-4o. Second, we propose GPT-Whisper, which uses Whisper as an audio-to-text module and evaluates the naturalness of text via targeted prompt engin... | [
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272694064 | 2409.10692 | 2024-09-16 | Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs | Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially with the number of robots and task complexity, making them challenging for online solution. To accelerate MR-TP over a system's lifetime, this ... | [
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272689519 | 2409.10280 | 2024-09-16 | ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code | In recent years, the application of large language models (LLMs) to code-related tasks has gained significant attention. However, existing evaluation benchmarks often focus on limited scenarios, such as code generation or completion, which do not reflect the diverse challenges developers face in real-world contexts. To... | [
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272689838 | 2409.10094 | 2024-09-16 | Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models | Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.e., In-Distribution (InD), or from OoD. Diffusion Models (DMs) are recently utilized in OoD detection by using the perceptual distances between the given image and its DM ge... | [
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272689251 | 2409.10080 | 2024-09-16 | DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion | In extreme scenarios such as nighttime or low-visibility environments, achieving reliable perception is critical for applications like autonomous driving, robotics, and surveillance. Multi-modality image fusion, particularly integrating infrared imaging, offers a robust solution by combining complementary information f... | [
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272693854 | 2409.10644 | 2024-09-16 | Improving Multi-candidate Speculative Decoding | Speculative Decoding (SD) is a technique to accelerate the inference of Large Language Models (LLMs) by using a lower complexity draft model to propose candidate tokens verified by a larger target model. To further improve efficiency, Multi-Candidate Speculative Decoding (MCSD) improves upon this by sampling multiple c... | [
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272690298 | 2409.10385 | 2024-09-16 | Mamba-ST: State Space Model for Efficient Style Transfer | The goal of style transfer is, given a content image and a style source, generating a new image preserving the content but with the artistic representation of the style source. Most of the state-of-the-art architectures use transformers or diffusion-based models to perform this task, despite the heavy computational bur... | [
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272689594 | 2409.09931 | 2024-09-16 | Generalizability of Graph Neural Network Force Fields for Predicting Solid-State Properties | Machine-learned force fields (MLFFs) promise to offer a computationally efficient alternative to ab initio simulations for complex molecular systems. However, ensuring their generalizability beyond training data is crucial for their wide application in studying solid materials. This work investigates the ability of a g... | [
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272690344 | 2409.10053 | 2024-09-16 | Householder Pseudo-Rotation: A Novel Approach to Activation Editing in LLMs with Direction-Magnitude Perspective | Activation Editing, which involves directly editting the internal representations of large language models (LLMs) to alter their behaviors and achieve desired properties, has emerged as a promising area of research. Existing works primarily treat LLMs' activations as points in space and modify them by adding steering v... | [
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272538333 | 2409.10218 | 2024-09-16 | Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies | Pruning neural networks (NNs) can streamline them but risks removing vital parameters from safe reinforcement learning (RL) policies. We introduce an interpretable RL method called VERINTER, which combines NN pruning with model checking to ensure interpretable RL safety. VERINTER exactly quantifies the effects of pruni... | [
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273022845 | 2410.00023 | 2024-09-16 | Self-Tuning Spectral Clustering for Speaker Diarization | Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to the need for careful tuning before constructing the Laplacian. In this study, we present a novel pruning algorithm to create a sparse affinity ... | [
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272689814 | 2409.10429 | 2024-09-16 | SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition | Automatic Speech Recognition (ASR) models demonstrate outstanding performance on high-resource languages but face significant challenges when applied to low-resource languages due to limited training data and insufficient cross-lingual generalization. Existing adaptation strategies, such as shallow fusion, data augment... | [
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272694158 | 2409.10777 | 2024-09-16 | Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming | Physics-Informed Neural Networks (PINNs) represent a significant advancement in Scientific Machine Learning (SciML), which integrate physical domain knowledge into an empirical loss function as soft constraints and apply existing machine learning methods to train the model. However, recent research has noted that PINNs... | [
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272690060 | 2409.10496 | 2024-09-16 | MusicLIME: Explainable Multimodal Music Understanding | Multimodal models are critical for music understanding tasks, as they capture the complex interplay between audio and lyrics. However, as these models become more prevalent, the need for explainability grows-understanding how these systems make decisions is vital for ensuring fairness, reducing bias, and fostering trus... | [
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272689805 | 2409.10085 | 2024-09-16 | A Riemannian Approach to Ground Metric Learning for Optimal Transport | Optimal transport (OT) theory has attracted much attention in machine learning and signal processing applications. OT defines a notion of distance between probability distributions of source and target data points. A crucial factor that influences OT-based distances is the ground metric of the embedding space in which ... | [
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272694638 | 2409.10787 | 2024-09-16 | Towards Automatic Assessment of Self-Supervised Speech Models using Rank | This study explores using embedding rank as an unsupervised evaluation metric for general-purpose speech encoders trained via self-supervised learning (SSL). Traditionally, assessing the performance of these encoders is resource-intensive and requires labeled data from the downstream tasks. Inspired by the vision domai... | [
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272689738 | 2409.10032 | 2024-09-16 | Embodiment-Agnostic Action Planning via Object-Part Scene Flow | Observing that the key for robotic action planning is to understand the target-object motion when its associated part is manipulated by the end effector, we propose to generate the 3D object-part scene flow and extract its transformations to solve the action trajectories for diverse embodiments. The advantage of our ap... | [
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272690300 | 2409.10191 | 2024-09-16 | LLMs for clinical risk prediction | This study compares the efficacy of GPT-4 and clinalytix Medical AI in predicting the clinical risk of delirium development. Findings indicate that GPT-4 exhibited significant deficiencies in identifying positive cases and struggled to provide reliable probability estimates for delirium risk, while clinalytix Medical A... | [
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272690219 | 2409.09907 | 2024-09-16 | Rapid Adaptation of Earth Observation Foundation Models for Segmentation | This study investigates the efficacy of Low-Rank Adaptation (LoRA) in fine-tuning Earth Observation (EO) foundation models for flood segmentation. We hypothesize that LoRA, a parameter-efficient technique, can significantly accelerate the adaptation of large-scale EO models to this critical task while maintaining high ... | [
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272689885 | 2409.10309 | 2024-09-16 | beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems | Recommender systems often use text-side information to improve their predictions, especially in cold-start or zero-shot recommendation scenarios, where traditional collaborative filtering approaches cannot be used. Many approaches to text-mining side information for recommender systems have been proposed over recent ye... | [
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272690048 | 2409.10161 | 2024-09-16 | SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting | Sim2Real transfer, particularly for manipulation policies relying on RGB images, remains a critical challenge in robotics due to the significant domain shift between synthetic and real-world visual data. In this paper, we propose SplatSim, a novel framework that leverages Gaussian Splatting as the primary rendering pri... | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Robot manipulation",
"Generative AI for embodied AI",
"3D Gaussian splatting",
"Automatic robotic data generation",
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