id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.18520 | Perturbation Ontology based Graph Attention Networks | [
"cs.LG"
] | In recent years, graph representation learning has undergone a paradigm shift, driven by the emergence and proliferation of graph neural networks (GNNs) and their heterogeneous counterparts. Heterogeneous GNNs have shown remarkable success in extracting low-dimensional embeddings from complex graphs that encompass dive... | {
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2411.18521 | Towards Motion Compensation in Autonomous Robotic Subretinal Injections | [
"cs.RO"
] | Exudative (wet) age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, typically treated with intravitreal injections. Emerging therapies, such as subretinal injections of stem cells, gene therapy, small molecules or RPE cells require precise delivery to avoid damaging delicate retina... | {
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2411.18523 | Non-reciprocal Beyond Diagonal RIS: Sum-Rate Maximization in Full-Duplex
Communications | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Reconfigurable intelligent surface (RIS) has been envisioned as a key technology in future wireless communication networks to enable smart radio environment. To further enhance the passive beamforming capability of RIS, beyond diagonal (BD)-RIS has been proposed considering reconfigurable interconnections among differe... | {
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2411.18526 | NeuroAI for AI Safety | [
"cs.AI",
"cs.LG"
] | As AI systems become increasingly powerful, the need for safe AI has become more pressing. Humans are an attractive model for AI safety: as the only known agents capable of general intelligence, they perform robustly even under conditions that deviate significantly from prior experiences, explore the world safely, unde... | {
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2411.18530 | Emergence of Self-Identity in AI: A Mathematical Framework and Empirical
Study with Generative Large Language Models | [
"cs.CL",
"math.MG"
] | This paper introduces a mathematical framework for defining and quantifying self-identity in artificial intelligence (AI) systems, addressing a critical gap in the theoretical foundations of artificial consciousness. While existing approaches to artificial self-awareness often rely on heuristic implementations or philo... | {
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2411.18531 | Statistic Maximal Leakage | [
"cs.IT",
"math.IT"
] | We introduce a privacy measure called statistic maximal leakage that quantifies how much a privacy mechanism leaks about a specific secret, relative to the adversary's prior information about that secret. Statistic maximal leakage is an extension of the well-known maximal leakage. Unlike maximal leakage, which protects... | {
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2411.18533 | Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern
Recognition | [
"cs.CV"
] | The patterns on wafer maps play a crucial role in helping engineers identify the causes of production issues during semiconductor manufacturing. In order to reduce costs and improve accuracy, automation technology is essential, and recent developments in deep learning have led to impressive results in wafer map pattern... | {
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2411.18539 | AdaVLN: Towards Visual Language Navigation in Continuous Indoor
Environments with Moving Humans | [
"cs.CV",
"cs.RO"
] | Visual Language Navigation is a task that challenges robots to navigate in realistic environments based on natural language instructions. While previous research has largely focused on static settings, real-world navigation must often contend with dynamic human obstacles. Hence, we propose an extension to the task, ter... | {
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2411.18548 | PhyCAGE: Physically Plausible Compositional 3D Asset Generation from a
Single Image | [
"cs.CV"
] | We present PhyCAGE, the first approach for physically plausible compositional 3D asset generation from a single image. Given an input image, we first generate consistent multi-view images for components of the assets. These images are then fitted with 3D Gaussian Splatting representations. To ensure that the Gaussians ... | {
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2411.18551 | Concentration of Cumulative Reward in Markov Decision Processes | [
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | In this paper, we investigate the concentration properties of cumulative rewards in Markov Decision Processes (MDPs), focusing on both asymptotic and non-asymptotic settings. We introduce a unified approach to characterize reward concentration in MDPs, covering both infinite-horizon settings (i.e., average and discount... | {
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2411.18552 | FAM Diffusion: Frequency and Attention Modulation for High-Resolution
Image Generation with Stable Diffusion | [
"cs.CV"
] | Diffusion models are proficient at generating high-quality images. They are however effective only when operating at the resolution used during training. Inference at a scaled resolution leads to repetitive patterns and structural distortions. Retraining at higher resolutions quickly becomes prohibitive. Thus, methods ... | {
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2411.18553 | Retrofitting Large Language Models with Dynamic Tokenization | [
"cs.CL"
] | Current language models (LMs) use a fixed, static subword tokenizer. This default choice typically results in degraded efficiency and language capabilities, especially in languages other than English. To address this issue, we challenge the static design and propose retrofitting LMs with dynamic tokenization: a way to ... | {
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2411.18562 | DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous
Manipulation | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simpler manipulation tasks, they often produce unrealistic ghost states (e.g., the object automatically moves without hand contact) or lack adaptability when handling... | {
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2411.18564 | Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs | [
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they often struggle with spatial reasoning. This paper presents a novel neural-symbolic framework that enhances LLMs' spatial reasoning abilities through iterative feedback between LLMs and Answer Set Programming (ASP). We ... | {
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2411.18571 | Challenges in Adapting Multilingual LLMs to Low-Resource Languages using
LoRA PEFT Tuning | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, yet challenges persist in adapting these models for low-resource languages. In this study, we investigate the effects of Low-Rank Adaptation (LoRA) Parameter-Efficient Fine-Tuning (PEFT) on multilingual Gemma models for Marathi, a lang... | {
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2411.18572 | Exploring Depth Information for Detecting Manipulated Face Videos | [
"cs.CV"
] | Face manipulation detection has been receiving a lot of attention for the reliability and security of the face images/videos. Recent studies focus on using auxiliary information or prior knowledge to capture robust manipulation traces, which are shown to be promising. As one of the important face features, the face dep... | {
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2411.18575 | Functional relevance based on the continuous Shapley value | [
"stat.ML",
"cs.AI",
"cs.LG",
"stat.AP"
] | The presence of Artificial Intelligence (AI) in our society is increasing, which brings with it the need to understand the behaviour of AI mechanisms, including machine learning predictive algorithms fed with tabular data, text, or images, among other types of data. This work focuses on interpretability of predictive m... | {
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2411.18577 | On Importance of Code-Mixed Embeddings for Hate Speech Identification | [
"cs.CL",
"cs.LG"
] | Code-mixing is the practice of using two or more languages in a single sentence, which often occurs in multilingual communities such as India where people commonly speak multiple languages. Classic NLP tools, trained on monolingual data, face challenges when dealing with code-mixed data. Extracting meaningful informati... | {
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2411.18578 | Pruning Deep Convolutional Neural Network Using Conditional Mutual
Information | [
"cs.LG"
] | Convolutional Neural Networks (CNNs) achieve high performance in image classification tasks but are challenging to deploy on resource-limited hardware due to their large model sizes. To address this issue, we leverage Mutual Information, a metric that provides valuable insights into how deep learning models retain and ... | {
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2411.18579 | Surveying the space of descriptions of a composite system with machine
learning | [
"cs.IT",
"cs.LG",
"math.IT",
"physics.data-an"
] | Multivariate information theory provides a general and principled framework for understanding how the components of a complex system are connected. Existing analyses are coarse in nature -- built up from characterizations of discrete subsystems -- and can be computationally prohibitive. In this work, we propose to stud... | {
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2411.18583 | Automated Literature Review Using NLP Techniques and LLM-Based
Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large Language Model (LLM). The ever-increasing number of research articles provides a huge challenge for ma... | {
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2411.18588 | Hierarchical Information Flow for Generalized Efficient Image
Restoration | [
"cs.CV"
] | While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To strike a balance between efficiency and model capacity for a generalized transformer-based IR method, we propose a hierarchical information ... | {
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2411.18594 | Biomolecular Analysis of Soil Samples and Rock Imagery for Tracing
Evidence of Life Using a Mobile Robot | [
"cs.LG",
"cs.CV",
"cs.RO"
] | The search for evidence of past life on Mars presents a tremendous challenge that requires the usage of very advanced robotic technologies to overcome it. Current digital microscopic imagers and spectrometers used for astrobiological examination suffer from limitations such as insufficient resolution, narrow detection ... | {
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2411.18597 | Structured light with a million light planes per second | [
"cs.CV"
] | We introduce a structured light system that captures full-frame depth at rates of a thousand frames per second, four times faster than the previous state of the art. Our key innovation to this end is the design of an acousto-optic light scanning device that can scan light planes at rates up to two million planes per se... | {
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2411.18602 | Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for
Medical Image Analysis | [
"eess.IV",
"cs.CV"
] | Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models in downstream tasks like classification and segmentation. Materials and Methods: We utilized a latent diffusion model to condition the gene... | {
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2411.18607 | Task Arithmetic Through The Lens Of One-Shot Federated Learning | [
"cs.LG"
] | Task Arithmetic is a model merging technique that enables the combination of multiple models' capabilities into a single model through simple arithmetic in the weight space, without the need for additional fine-tuning or access to the original training data. However, the factors that determine the success of Task Arith... | {
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2411.18612 | Robust Offline Reinforcement Learning with Linearly Structured
$f$-Divergence Regularization | [
"cs.LG",
"cs.AI",
"cs.RO",
"stat.ML"
] | The Distributionally Robust Markov Decision Process (DRMDP) is a popular framework for addressing dynamics shift in reinforcement learning by learning policies robust to the worst-case transition dynamics within a constrained set. However, solving its dual optimization oracle poses significant challenges, limiting theo... | {
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2411.18613 | CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models | [
"cs.CV"
] | We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets to enable novel view synthesis at any specified camera poses and timestamps. Combined with a novel sampling approach, this model can transf... | {
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2411.18614 | Optimal root recovery for uniform attachment trees and $d$-regular
growing trees | [
"cs.DS",
"cs.SI",
"math.PR",
"math.ST",
"stat.TH"
] | We consider root-finding algorithms for random rooted trees grown by uniform attachment. Given an unlabeled copy of the tree and a target accuracy $\varepsilon > 0$, such an algorithm outputs a set of nodes that contains the root with probability at least $1 - \varepsilon$. We prove that, for the optimal algorithm, an ... | {
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2411.18615 | Proactive Gradient Conflict Mitigation in Multi-Task Learning: A Sparse
Training Perspective | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Advancing towards generalist agents necessitates the concurrent processing of multiple tasks using a unified model, thereby underscoring the growing significance of simultaneous model training on multiple downstream tasks. A common issue in multi-task learning is the occurrence of gradient conflict, which leads to pote... | {
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2411.18616 | Diffusion Self-Distillation for Zero-Shot Customized Image Generation | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Text-to-image diffusion models produce impressive results but are frustrating tools for artists who desire fine-grained control. For example, a common use case is to create images of a specific instance in novel contexts, i.e., "identity-preserving generation". This setting, along with many other tasks (e.g., relightin... | {
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2411.18620 | Cross-modal Information Flow in Multimodal Large Language Models | [
"cs.AI",
"cs.CL",
"cs.CV"
] | The recent advancements in auto-regressive multimodal large language models (MLLMs) have demonstrated promising progress for vision-language tasks. While there exists a variety of studies investigating the processing of linguistic information within large language models, little is currently known about the inner worki... | {
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2411.18622 | Leveraging Semi-Supervised Learning to Enhance Data Mining for Image
Classification under Limited Labeled Data | [
"cs.CV",
"cs.LG"
] | In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, t... | {
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2411.18623 | Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for
Robust 3D Robotic Manipulation | [
"cs.CV"
] | 3D geometric information is essential for manipulation tasks, as robots need to perceive the 3D environment, reason about spatial relationships, and interact with intricate spatial configurations. Recent research has increasingly focused on the explicit extraction of 3D features, while still facing challenges such as t... | {
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2411.18624 | GeneMAN: Generalizable Single-Image 3D Human Reconstruction from
Multi-Source Human Data | [
"cs.CV"
] | Given a single in-the-wild human photo, it remains a challenging task to reconstruct a high-fidelity 3D human model. Existing methods face difficulties including a) the varying body proportions captured by in-the-wild human images; b) diverse personal belongings within the shot; and c) ambiguities in human postures and... | {
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2411.18625 | Textured Gaussians for Enhanced 3D Scene Appearance Modeling | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has recently emerged as a state-of-the-art 3D reconstruction and rendering technique due to its high-quality results and fast training and rendering time. However, pixels covered by the same Gaussian are always shaded in the same color up to a Gaussian falloff scaling factor. Furthermore, t... | {
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2411.18627 | Topological Approach for Data Assimilation | [
"nlin.CD",
"cs.LG",
"math.AT"
] | Many dynamical systems are difficult or impossible to model using high fidelity physics based models. Consequently, researchers are relying more on data driven models to make predictions and forecasts. Based on limited training data, machine learning models often deviate from the true system states over time and need t... | {
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2411.18630 | Volume Rendering of Human Hand Anatomy | [
"cs.GR",
"cs.CV"
] | We study the design of transfer functions for volumetric rendering of magnetic resonance imaging (MRI) datasets of human hands. Human hands are anatomically complex, containing various organs within a limited space, which presents challenges for volumetric rendering. We focus on hand musculoskeletal organs because they... | {
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2411.18631 | Counterfactual Learning-Driven Representation Disentanglement for
Search-Enhanced Recommendation | [
"cs.IR"
] | For recommender systems in internet platforms, search activities provide additional insights into user interest through query-click interactions with items, and are thus widely used for enhancing personalized recommendation. However, these interacted items not only have transferable features matching users' interest he... | {
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2411.18634 | Semantic, Orthographic, and Morphological Biases in Humans' Wordle
Gameplay | [
"cs.CL"
] | We show that human players' gameplay in the game of Wordle is influenced by the semantics, orthography, and morphology of the player's previous guesses. We demonstrate this influence by comparing actual human players' guesses to near-optimal guesses, showing that human players' guesses are biased to be similar to previ... | {
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2411.18636 | Towards Advanced Speech Signal Processing: A Statistical Perspective on
Convolution-Based Architectures and its Applications | [
"cs.SD",
"cs.AI",
"cs.CL",
"eess.AS"
] | This article surveys convolution-based models including convolutional neural networks (CNNs), Conformers, ResNets, and CRNNs-as speech signal processing models and provide their statistical backgrounds and speech recognition, speaker identification, emotion recognition, and speech enhancement applications. Through comp... | {
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2411.18640 | A quantum inspired predictor of Parkinsons disease built on a diverse,
multimodal dataset | [
"q-bio.QM",
"cs.LG"
] | Parkinsons disease, the fastest growing neurodegenerative disorder globally, has seen a 50 percent increase in cases within just two years. As speech, memory, and motor symptoms worsen over time, early diagnosis is crucial for preserving patients quality of life. While machine-learning-based detection has shown promise... | {
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2411.18644 | Scene Co-pilot: Procedural Text to Video Generation with Human in the
Loop | [
"cs.CV"
] | Video generation has achieved impressive quality, but it still suffers from artifacts such as temporal inconsistency and violation of physical laws. Leveraging 3D scenes can fundamentally resolve these issues by providing precise control over scene entities. To facilitate the easy generation of diverse photorealistic s... | {
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2411.18645 | Bi-ICE: An Inner Interpretable Framework for Image Classification via
Bi-directional Interactions between Concept and Input Embeddings | [
"cs.CV",
"cs.LG"
] | Inner interpretability is a promising field focused on uncovering the internal mechanisms of AI systems and developing scalable, automated methods to understand these systems at a mechanistic level. While significant research has explored top-down approaches starting from high-level problems or algorithmic hypotheses a... | {
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2411.18648 | MADE: Graph Backdoor Defense with Masked Unlearning | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Graph Neural Networks (GNNs) have garnered significant attention from researchers due to their outstanding performance in handling graph-related tasks, such as social network analysis, protein design, and so on. Despite their widespread application, recent research has demonstrated that GNNs are vulnerable to backdoor ... | {
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2411.18649 | Dynamic Logistic Ensembles with Recursive Probability and Automatic
Subset Splitting for Enhanced Binary Classification | [
"cs.LG",
"cs.AI"
] | This paper presents a novel approach to binary classification using dynamic logistic ensemble models. The proposed method addresses the challenges posed by datasets containing inherent internal clusters that lack explicit feature-based separations. By extending traditional logistic regression, we develop an algorithm t... | {
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2411.18650 | RoMo: Robust Motion Segmentation Improves Structure from Motion | [
"cs.CV"
] | There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video. While these tasks rely heavily on known camera poses, the problem of finding such poses using structure-from-motion (SfM) often depends on robustly separating static from dynamic parts of a video.... | {
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2411.18651 | Verbalized Representation Learning for Interpretable Few-Shot
Generalization | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Humans recognize objects after observing only a few examples, a remarkable capability enabled by their inherent language understanding of the real-world environment. Developing verbalized and interpretable representation can significantly improve model generalization in low-data settings. In this work, we propose Verba... | {
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2411.18652 | Surf-NeRF: Surface Regularised Neural Radiance Fields | [
"cs.CV"
] | Neural Radiance Fields (NeRFs) provide a high fidelity, continuous scene representation that can realistically represent complex behaviour of light. Despite recent works like Ref-NeRF improving geometry through physics-inspired models, the ability for a NeRF to overcome shape-radiance ambiguity and converge to a repres... | {
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2411.18653 | PRSI: Privacy-Preserving Recommendation Model Based on Vector Splitting
and Interactive Protocols | [
"cs.CR",
"cs.AI"
] | With the development of the internet, recommending interesting products to users has become a highly valuable research topic for businesses. Recommendation systems play a crucial role in addressing this issue. To prevent the leakage of each user's (client's) private data, Federated Recommendation Systems (FedRec) have ... | {
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2411.18654 | AToM: Aligning Text-to-Motion Model at Event-Level with GPT-4Vision
Reward | [
"cs.CV"
] | Recently, text-to-motion models have opened new possibilities for creating realistic human motion with greater efficiency and flexibility. However, aligning motion generation with event-level textual descriptions presents unique challenges due to the complex relationship between textual prompts and desired motion outco... | {
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2411.18656 | The Return of Pseudosciences in Artificial Intelligence: Have Machine
Learning and Deep Learning Forgotten Lessons from Statistics and History? | [
"stat.ML",
"cs.AI",
"cs.LG"
] | In today's world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking, to theft detection via video analysis, and even predicting political or sexual orientation from facial images.... | {
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2411.18657 | ScaleViz: Scaling Visualization Recommendation Models on Large Data | [
"cs.AI",
"cs.HC",
"stat.ML"
] | Automated visualization recommendations (vis-rec) help users to derive crucial insights from new datasets. Typically, such automated vis-rec models first calculate a large number of statistics from the datasets and then use machine-learning models to score or classify multiple visualizations choices to recommend the mo... | {
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2411.18658 | HDI-Former: Hybrid Dynamic Interaction ANN-SNN Transformer for Object
Detection Using Frames and Events | [
"cs.CV"
] | Combining the complementary benefits of frames and events has been widely used for object detection in challenging scenarios. However, most object detection methods use two independent Artificial Neural Network (ANN) branches, limiting cross-modality information interaction across the two visual streams and encounterin... | {
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2411.18659 | DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large
Vision-Language Models | [
"cs.CV",
"cs.AI"
] | Large vision-language models (LVLMs) have demonstrated exceptional performance on complex multimodal tasks. However, they continue to suffer from significant hallucination issues, including object, attribute, and relational hallucinations. To accurately detect these hallucinations, we investigated the variations in cro... | {
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2411.18660 | OOD-HOI: Text-Driven 3D Whole-Body Human-Object Interactions Generation
Beyond Training Domains | [
"cs.CV"
] | Generating realistic 3D human-object interactions (HOIs) from text descriptions is a active research topic with potential applications in virtual and augmented reality, robotics, and animation. However, creating high-quality 3D HOIs remains challenging due to the lack of large-scale interaction data and the difficulty ... | {
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2411.18662 | HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion
Prior | [
"cs.CV"
] | Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noisy text prompts and their lack of spatial information. In this paper, we present HoliSDiP, a framework that leverages semantic segmentation ... | {
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2411.18663 | FAIR Digital Objects for the Realization of Globally Aligned Data Spaces | [
"cs.DB"
] | The FAIR principles are globally accepted guidelines for improved data management practices with the potential to align data spaces on a global scale. In practice, this is only marginally achieved through the different ways in which organizations interpret and implement these principles. The concept of FAIR Digital Obj... | {
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2411.18664 | Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling | [
"cs.CV"
] | Diffusion models have emerged as a powerful tool for generating high-quality images, videos, and 3D content. While sampling guidance techniques like CFG improve quality, they reduce diversity and motion. Autoguidance mitigates these issues but demands extra weak model training, limiting its practicality for large-scale... | {
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2411.18665 | SpotLight: Shadow-Guided Object Relighting via Diffusion | [
"cs.CV",
"cs.GR"
] | Recent work has shown that diffusion models can be used as powerful neural rendering engines that can be leveraged for inserting virtual objects into images. Unlike typical physics-based renderers, however, neural rendering engines are limited by the lack of manual control over the lighting setup, which is often essent... | {
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2411.18666 | 3D Scene Graph Guided Vision-Language Pre-training | [
"cs.CV"
] | 3D vision-language (VL) reasoning has gained significant attention due to its potential to bridge the 3D physical world with natural language descriptions. Existing approaches typically follow task-specific, highly specialized paradigms. Therefore, these methods focus on a limited range of reasoning sub-tasks and rely ... | {
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2411.18667 | Point Cloud Unsupervised Pre-training via 3D Gaussian Splatting | [
"cs.CV"
] | Pre-training on large-scale unlabeled datasets contribute to the model achieving powerful performance on 3D vision tasks, especially when annotations are limited. However, existing rendering-based self-supervised frameworks are computationally demanding and memory-intensive during pre-training due to the inherent natur... | {
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2411.18668 | Towards Chunk-Wise Generation for Long Videos | [
"cs.CV"
] | Generating long-duration videos has always been a significant challenge due to the inherent complexity of spatio-temporal domain and the substantial GPU memory demands required to calculate huge size tensors. While diffusion based generative models achieve state-of-the-art performance in video generation task, they are... | {
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2411.18669 | SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation
Models to Any Imaging Modality | [
"cs.CV"
] | Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact. However, it is extremely challenging for sensors in many different fields to collect similar scales of natural images to train strong foundation models. To this end, this work presents a simple and ... | {
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2411.18671 | TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any
Point in Long Video | [
"cs.CV"
] | In this paper, we present TAPTRv3, which is built upon TAPTRv2 to improve its point tracking robustness in long videos. TAPTRv2 is a simple DETR-like framework that can accurately track any point in real-world videos without requiring cost-volume. TAPTRv3 improves TAPTRv2 by addressing its shortage in querying high qua... | {
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2411.18672 | FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray
Report Generation Models | [
"cs.CV"
] | Medical vision-language model models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability. We introduce FactCheXcker, a modular framework that de-hallucinates radiology report measurements by leveraging an improved query-co... | {
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2411.18673 | AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion
Transformers | [
"cs.CV"
] | Numerous works have recently integrated 3D camera control into foundational text-to-video models, but the resulting camera control is often imprecise, and video generation quality suffers. In this work, we analyze camera motion from a first principles perspective, uncovering insights that enable precise 3D camera manip... | {
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2411.18674 | Active Data Curation Effectively Distills Large-Scale Multimodal Models | [
"cs.CV",
"cs.LG"
] | Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones. Prior works have explored ever more complex KD strategies involving different objective functions, teacher-ensembles, and weight inheritance. In this work we explore an alternative, yet simple approach -- active d... | {
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2411.18675 | GaussianSpeech: Audio-Driven Gaussian Avatars | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.SD",
"eess.AS"
] | We introduce GaussianSpeech, a novel approach that synthesizes high-fidelity animation sequences of photo-realistic, personalized 3D human head avatars from spoken audio. To capture the expressive, detailed nature of human heads, including skin furrowing and finer-scale facial movements, we propose to couple speech sig... | {
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2411.18676 | Embodied Red Teaming for Auditing Robotic Foundation Models | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Language-conditioned robot models have the potential to enable robots to perform a wide range of tasks based on natural language instructions. However, assessing their safety and effectiveness remains challenging because it is difficult to test all the different ways a single task can be phrased. Current benchmarks hav... | {
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2411.18677 | MatchDiffusion: Training-free Generation of Match-cuts | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Match-cuts are powerful cinematic tools that create seamless transitions between scenes, delivering strong visual and metaphorical connections. However, crafting match-cuts is a challenging, resource-intensive process requiring deliberate artistic planning. In MatchDiffusion, we present the first training-free method f... | {
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2411.18688 | Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via
Inference-Time Alignment | [
"cs.CR",
"cs.AI",
"cs.LG"
] | With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that despite training-time safety alignment, these models remain vulnerable to jailbreak attacks. In this work, we first highlight an important safe... | {
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2411.18699 | An indicator for effectiveness of text-to-image guardrails utilizing the
Single-Turn Crescendo Attack (STCA) | [
"cs.CR",
"cs.CL"
] | The Single-Turn Crescendo Attack (STCA), first introduced in Aqrawi and Abbasi [2024], is an innovative method designed to bypass the ethical safeguards of text-to-text AI models, compelling them to generate harmful content. This technique leverages a strategic escalation of context within a single prompt, combined wit... | {
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2411.18700 | On the Effectiveness of Incremental Training of Large Language Models | [
"cs.CL",
"cs.AI"
] | Training large language models is a computationally intensive process that often requires substantial resources to achieve state-of-the-art results. Incremental layer-wise training has been proposed as a potential strategy to optimize the training process by progressively introducing layers, with the expectation that t... | {
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2411.18702 | Random Walks with Tweedie: A Unified Framework for Diffusion Models | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | We present a simple template for designing generative diffusion model algorithms based on an interpretation of diffusion sampling as a sequence of random walks. Score-based diffusion models are widely used to generate high-quality images. Diffusion models have also been shown to yield state-of-the-art performance in ma... | {
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2411.18704 | Exponential Moving Average of Weights in Deep Learning: Dynamics and
Benefits | [
"cs.LG"
] | Weight averaging of Stochastic Gradient Descent (SGD) iterates is a popular method for training deep learning models. While it is often used as part of complex training pipelines to improve generalization or serve as a `teacher' model, weight averaging lacks proper evaluation on its own. In this work, we present a syst... | {
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2411.18708 | Embracing AI in Education: Understanding the Surge in Large Language
Model Use by Secondary Students | [
"cs.HC",
"cs.AI"
] | The impressive essay writing and problem-solving capabilities of large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education. Our goal is to gain insights into the widespread use of LLMs among secondary students to inform their future development. Despite school restrictions, our survey o... | {
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2411.18711 | Evaluating Vision-Language Models as Evaluators in Path Planning | [
"cs.CV",
"cs.CL"
] | Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intriguing question: if these models cannot plan well, can they still contribute to the planning framework as a helpful plan evaluator? In this work... | {
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2411.18714 | Explainable deep learning improves human mental models of self-driving
cars | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Self-driving cars increasingly rely on deep neural networks to achieve human-like driving. However, the opacity of such black-box motion planners makes it challenging for the human behind the wheel to accurately anticipate when they will fail, with potentially catastrophic consequences. Here, we introduce concept-wrapp... | {
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2411.18716 | Addressing bias in Recommender Systems: A Case Study on Data Debiasing
Techniques in Mobile Games | [
"cs.LG"
] | The mobile gaming industry, particularly the free-to-play sector, has been around for more than a decade, yet it still experiences rapid growth. The concept of games-as-service requires game developers to pay much more attention to recommendations of content in their games. With recommender systems (RS), the inevitable... | {
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2411.18719 | Timing Matters: Enhancing User Experience through Temporal Prediction in
Smart Homes | [
"cs.LG",
"cs.AI"
] | Have you ever considered the sheer volume of actions we perform using IoT (Internet of Things) devices within our homes, offices, and daily environments? From the mundane act of flicking a light switch to the precise adjustment of room temperatures, we are surrounded by a wealth of data, each representing a glimpse int... | {
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2411.18721 | A Machine Learning Approach Capturing Hidden Parameters in Autonomous
Thin-Film Deposition | [
"cond-mat.mtrl-sci",
"cs.RO"
] | The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully autonomous cycle of deposition, characterization, and decision-making. Additionally, the inherent sensitivity of thin film growth to hidden para... | {
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2411.18727 | Generative Visual Communication in the Era of Vision-Language Models | [
"cs.CV",
"cs.AI"
] | Visual communication, dating back to prehistoric cave paintings, is the use of visual elements to convey ideas and information. In today's visually saturated world, effective design demands an understanding of graphic design principles, visual storytelling, human psychology, and the ability to distill complex informati... | {
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2411.18728 | The Last Mile to Supervised Performance: Semi-Supervised Domain
Adaptation for Semantic Segmentation | [
"cs.CV",
"cs.LG"
] | Supervised deep learning requires massive labeled datasets, but obtaining annotations is not always easy or possible, especially for dense tasks like semantic segmentation. To overcome this issue, numerous works explore Unsupervised Domain Adaptation (UDA), which uses a labeled dataset from another domain (source), or ... | {
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2411.18729 | Multi-Task Model Merging via Adaptive Weight Disentanglement | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Model merging has recently gained attention as an economical and scalable approach to incorporate task-specific weights from various tasks into a unified multi-task model. For example, in Task Arithmetic (TA), adding the fine-tuned weights of different tasks can enhance the model's performance on those tasks, while sub... | {
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2411.18730 | Foundation Models in Radiology: What, How, When, Why and Why Not | [
"cs.LG"
] | Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models, are trained on extensive corpora of unlabeled data and demonstrate high performance a... | {
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2411.18731 | The Performance of the LSTM-based Code Generated by Large Language
Models (LLMs) in Forecasting Time Series Data | [
"cs.AI",
"cs.SE"
] | As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep learning models and codes and thus may opt to leverage LLMs to generat... | {
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2411.18745 | DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration | [
"cs.CV"
] | In this work, we address a challenge in video inpainting: reconstructing occluded regions in dynamic, real-world scenarios. Motivated by the need for continuous human motion monitoring in healthcare settings, where facial features are frequently obscured, we propose a diffusion-based video-level inpainting model, DiffM... | {
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2411.18746 | Inference Privacy: Properties and Mechanisms | [
"cs.CR",
"cs.IT",
"cs.LG",
"math.IT"
] | Ensuring privacy during inference stage is crucial to prevent malicious third parties from reconstructing users' private inputs from outputs of public models. Despite a large body of literature on privacy preserving learning (which ensures privacy of training data), there is no existing systematic framework to ensure t... | {
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2411.18750 | OSU-Wing PIC Phase I Evaluation: Baseline Workload and Situation
Awareness Results | [
"cs.HC",
"cs.RO"
] | The common theory is that human pilot's performance degrades when responsible for an increased number of uncrewed aircraft systems (UAS). This theory was developed in the early 2010's for ground robots and not highly autonomous UAS. It has been shown that increasing autonomy can mitigate some performance impacts associ... | {
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2411.18752 | Locally Differentially Private Online Federated Learning With Correlated
Noise | [
"cs.LG",
"cs.DC",
"stat.ML"
] | We introduce a locally differentially private (LDP) algorithm for online federated learning that employs temporally correlated noise to improve utility while preserving privacy. To address challenges posed by the correlated noise and local updates with streaming non-IID data, we develop a perturbed iterate analysis tha... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.18755 | Cyber-Attack Technique Classification Using Two-Stage Trained Large
Language Models | [
"cs.LG",
"cs.CL",
"cs.CR"
] | Understanding the attack patterns associated with a cyberattack is crucial for comprehending the attacker's behaviors and implementing the right mitigation measures. However, majority of the information regarding new attacks is typically presented in unstructured text, posing significant challenges for security analyst... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 1,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.18759 | Classification of Deceased Patients from Non-Deceased Patients using
Random Forest and Support Vector Machine Classifiers | [
"cs.LG"
] | Analyzing large datasets and summarizing it into useful information is the heart of the data mining process. In healthcare, information can be converted into knowledge about patient historical patterns and possible future trends. During the COVID-19 pandemic, data mining COVID-19 patient information poses an opportunit... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
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"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.18762 | Kernelized offset-free data-driven predictive control for nonlinear
systems | [
"eess.SY",
"cs.SY"
] | This paper presents a kernelized offset-free data-driven predictive control scheme for nonlinear systems. Traditional model-based and data-driven predictive controllers often struggle with inaccurate predictors or persistent disturbances, especially in the case of nonlinear dynamics, leading to tracking offsets and sta... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.18764 | CoVis: A Collaborative Framework for Fine-grained Graphic Visual
Understanding | [
"cs.CV",
"cs.AI"
] | Graphic visual content helps in promoting information communication and inspiration divergence. However, the interpretation of visual content currently relies mainly on humans' personal knowledge background, thereby affecting the quality and efficiency of information acquisition and understanding. To improve the qualit... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
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"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.18766 | Collective steering in finite time: controllability on
$\text{GL}^+(n,\mathbb{R})$ | [
"math.OC",
"cs.SY",
"eess.SY"
] | We consider the problem of steering a collection of n particles that obey identical n-dimensional linear dynamics via a common state feedback law towards a rearrangement of their positions, cast as a controllability problem for a dynamical system evolving on the space of matrices with positive determinant. We show that... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
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"cs.IR": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.18767 | Multi-Task Learning for Integrated Automated Contouring and Voxel-Based
Dose Prediction in Radiotherapy | [
"physics.med-ph",
"cs.CV",
"cs.LG"
] | Deep learning-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment planning as separate tasks. Moreover in deep learning (DL), the contouring and ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.18776 | Fall Leaf Adversarial Attack on Traffic Sign Classification | [
"cs.CV",
"cs.CR"
] | Adversarial input image perturbation attacks have emerged as a significant threat to machine learning algorithms, particularly in image classification setting. These attacks involve subtle perturbations to input images that cause neural networks to misclassify the input images, even though the images remain easily reco... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.18784 | MRI Breast tissue segmentation using nnU-Net for biomechanical modeling | [
"cs.CV",
"eess.IV",
"physics.med-ph"
] | Integrating 2D mammography with 3D magnetic resonance imaging (MRI) is crucial for improving breast cancer diagnosis and treatment planning. However, this integration is challenging due to differences in imaging modalities and the need for precise tissue segmentation and alignment. This paper addresses these challenges... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.18788 | Investigating Plausibility of Biologically Inspired Bayesian Learning in
ANNs | [
"cs.LG"
] | Catastrophic forgetting has been the leading issue in the domain of lifelong learning in artificial systems. Current artificial systems are reasonably good at learning domains they have seen before; however, as soon as they encounter something new, they either go through a significant performance deterioration or if yo... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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