id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.04653 | Hidden in the Noise: Two-Stage Robust Watermarking for Images | [
"cs.CV",
"cs.AI",
"cs.LG"
] | As the quality of image generators continues to improve, deepfakes become a topic of considerable societal debate. Image watermarking allows responsible model owners to detect and label their AI-generated content, which can mitigate the harm. Yet, current state-of-the-art methods in image watermarking remain vulnerable... | {
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2412.04655 | From Models to Systems: A Comprehensive Fairness Framework for
Compositional Recommender Systems | [
"cs.AI"
] | Fairness research in machine learning often centers on ensuring equitable performance of individual models. However, real-world recommendation systems are built on multiple models and even multiple stages, from candidate retrieval to scoring and serving, which raises challenges for responsible development and deploymen... | {
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2412.04657 | An Efficient Model Maintenance Approach for MLOps | [
"cs.SE",
"cs.LG"
] | In recent years, many industries have utilized machine learning models (ML) in their systems. Ideally, machine learning models should be trained on and applied to data from the same distributions. However, the data evolves over time in many application areas, leading to data and concept drift, which in turn causes the ... | {
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2412.04658 | Learning for Layered Safety-Critical Control with Predictive Control
Barrier Functions | [
"eess.SY",
"cs.LG",
"cs.RO",
"cs.SY"
] | Safety filters leveraging control barrier functions (CBFs) are highly effective for enforcing safe behavior on complex systems. It is often easier to synthesize CBFs for a Reduced order Model (RoM), and track the resulting safe behavior on the Full order Model (FoM) -- yet gaps between the RoM and FoM can result in saf... | {
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2412.04659 | LiveNet: Robust, Minimally Invasive Multi-Robot Control for Safe and
Live Navigation in Constrained Environments | [
"cs.RO",
"cs.MA",
"cs.SY",
"eess.SY"
] | Robots in densely populated real-world environments frequently encounter constrained and cluttered situations such as passing through narrow doorways, hallways, and corridor intersections, where conflicts over limited space result in collisions or deadlocks among the robots. Current decentralized state-of-the-art optim... | {
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2412.04661 | HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and
Representation Learning | [
"cs.IR",
"cs.AI"
] | Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effectiveness of RAG depends on the relevance of retrieved documents, which is influenced by the semantic alignment of embeddings with the domain'... | {
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2412.04664 | Multiclass Post-Earthquake Building Assessment Integrating Optical and
SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Timely and accurate assessments of building damage are crucial for effective response and recovery in the aftermath of earthquakes. Conventional preliminary damage assessments (PDA) often rely on manual door-to-door inspections, which are not only time-consuming but also pose significant safety risks. To safely expedit... | {
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2412.04665 | ProPLIKS: Probablistic 3D human body pose estimation | [
"cs.CV"
] | We present a novel approach for 3D human pose estimation by employing probabilistic modeling. This approach leverages the advantages of normalizing flows in non-Euclidean geometries to address uncertain poses. Specifically, our method employs normalizing flow tailored to the SO(3) rotational group, incorporating a coup... | {
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2412.04666 | LAA-Net: A Physical-prior-knowledge Based Network for Robust Nighttime
Depth Estimation | [
"cs.CV"
] | Existing self-supervised monocular depth estimation (MDE) models attempt to improve nighttime performance by using GANs to transfer nighttime images into their daytime versions. However, this can introduce inconsistencies due to the complexities of real-world daytime lighting variations, which may finally lead to inacc... | {
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2412.04668 | Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation | [
"cs.CV",
"cs.AI"
] | With the rapid scaling of neural networks, data storage and communication demands have intensified. Dataset distillation has emerged as a promising solution, condensing information from extensive datasets into a compact set of synthetic samples by solving a bilevel optimization problem. However, current methods face ch... | {
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2412.04671 | Fully Distributed, Flexible Compositional Visual Representations via
Soft Tensor Products | [
"cs.LG",
"cs.AI"
] | Since the inception of the classicalist vs. connectionist debate, it has been argued that the ability to systematically combine symbol-like entities into compositional representations is crucial for human intelligence. In connectionist systems, the field of disentanglement has gained prominence for its ability to produ... | {
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2412.04673 | Socially-Informed Reconstruction for Pedestrian Trajectory Forecasting | [
"cs.CV",
"cs.AI"
] | Pedestrian trajectory prediction remains a challenge for autonomous systems, particularly due to the intricate dynamics of social interactions. Accurate forecasting requires a comprehensive understanding not only of each pedestrian's previous trajectory but also of their interaction with the surrounding environment, an... | {
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2412.04677 | Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter
interactions | [
"cs.LG",
"cs.AI",
"hep-ph",
"physics.comp-ph",
"quant-ph"
] | With the approach of the High Luminosity Large Hadron Collider (HL-LHC) era set to begin particle collisions by the end of this decade, it is evident that the computational demands of traditional collision simulation methods are becoming increasingly unsustainable. Existing approaches, which rely heavily on first-princ... | {
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2412.04678 | Unsupervised Segmentation by Diffusing, Walking and Cutting | [
"cs.CV"
] | We propose an unsupervised image segmentation method using features from pre-trained text-to-image diffusion models. Inspired by classic spectral clustering approaches, we construct adjacency matrices from self-attention layers between image patches and recursively partition using Normalised Cuts. A key insight is that... | {
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2412.04680 | Superpixel Tokenization for Vision Transformers: Preserving Semantic
Integrity in Visual Tokens | [
"cs.CV"
] | Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success lies in the attention mechanism, which models relationships among tokens. While the tokenization process in NLP inherently ensures that a s... | {
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2412.04682 | Two stages domain invariant representation learners solve the large
co-variate shift in unsupervised domain adaptation with two dimensional data
domains | [
"cs.LG",
"cs.AI"
] | Recent developments in the unsupervised domain adaptation (UDA) enable the unsupervised machine learning (ML) prediction for target data, thus this will accelerate real world applications with ML models such as image recognition tasks in self-driving. Researchers have reported the UDA techniques are not working well un... | {
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2412.04683 | From Principles to Practice: A Deep Dive into AI Ethics and Regulations | [
"cs.AI"
] | In the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between innovation and regulation has become an emerging focus of our society. Despite tremendous advancements in AI's capabilities to excel in specific tasks and contribute to diverse sectors, establishing a high degree of trust in... | {
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2412.04690 | LLM-Align: Utilizing Large Language Models for Entity Alignment in
Knowledge Graphs | [
"cs.CL",
"cs.AI"
] | Entity Alignment (EA) seeks to identify and match corresponding entities across different Knowledge Graphs (KGs), playing a crucial role in knowledge fusion and integration. Embedding-based entity alignment (EA) has recently gained considerable attention, resulting in the emergence of many innovative approaches. Initia... | {
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2412.04692 | Smoothie: Label Free Language Model Routing | [
"cs.AI",
"cs.LG"
] | Large language models (LLMs) are increasingly used in applications where LLM inputs may span many different tasks. Recent work has found that the choice of LLM is consequential, and different LLMs may be good for different input samples. Prior approaches have thus explored how engineers might select an LLM to use for e... | {
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2412.04697 | Privacy-Preserving Retrieval Augmented Generation with Differential
Privacy | [
"cs.CR",
"cs.AI",
"cs.CL"
] | With the recent remarkable advancement of large language models (LLMs), there has been a growing interest in utilizing them in the domains with highly sensitive data that lies outside their training data. For this purpose, retrieval augmented generation (RAG) is particularly effective -- it assists LLMs by directly pro... | {
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2412.04698 | One-Hop Sub-Query Result Caches for Graph Database Systems | [
"cs.DB",
"cs.PF"
] | This paper introduces a novel one-hop sub-query result cache for processing graph read transactions, gR-Txs, in a graph database system. The one-hop navigation is from a vertex using either its in-coming or out-going edges with selection predicates that filter edges and vertices. Its cache entry identifies a unique one... | {
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2412.04700 | SpasticMyoElbow: Physical Human-Robot Interaction Simulation Framework
for Modelling Elbow Spasticity | [
"cs.RO"
] | Robotic devices hold great potential for efficient and reliable assessment of neuromotor abnormalities in post-stroke patients. However, spasticity caused by stroke is still assessed manually in clinical settings. The limited and variable nature of data collected from patients has long posed a major barrier to quantita... | {
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2412.04703 | Transformers Struggle to Learn to Search | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Search is an ability foundational in many important tasks, and recent studies have shown that large language models (LLMs) struggle to perform search robustly. It is unknown whether this inability is due to a lack of data, insufficient model parameters, or fundamental limitations of the transformer architecture. In thi... | {
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2412.04704 | On Interpreting the Effectiveness of Unsupervised Software Traceability
with Information Theory | [
"cs.SE",
"cs.AI"
] | Traceability is a cornerstone of modern software development, ensuring system reliability and facilitating software maintenance. While unsupervised techniques leveraging Information Retrieval (IR) and Machine Learning (ML) methods have been widely used for predicting trace links, their effectiveness remains underexplor... | {
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2412.04707 | Parametric-ControlNet: Multimodal Control in Foundation Models for
Precise Engineering Design Synthesis | [
"cs.AI",
"cs.CE",
"cs.CV",
"cs.HC"
] | This paper introduces a generative model designed for multimodal control over text-to-image foundation generative AI models such as Stable Diffusion, specifically tailored for engineering design synthesis. Our model proposes parametric, image, and text control modalities to enhance design precision and diversity. First... | {
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2412.04714 | PCTreeS: 3D Point Cloud Tree Species Classification Using Airborne LiDAR
Images | [
"cs.CV",
"cs.AI"
] | Reliable large-scale data on the state of forests is crucial for monitoring ecosystem health, carbon stock, and the impact of climate change. Current knowledge of tree species distribution relies heavily on manual data collection in the field, which often takes years to complete, resulting in limited datasets that cove... | {
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2412.04715 | Addressing Attribute Leakages in Diffusion-based Image Editing without
Training | [
"cs.CV"
] | Diffusion models have become a cornerstone in image editing, offering flexibility with language prompts and source images. However, a key challenge is attribute leakage, where unintended modifications occur in non-target regions or within target regions due to attribute interference. Existing methods often suffer from ... | {
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2412.04717 | NoLoR: An ASR-Based Framework for Expedited Endangered Language
Documentation with Neo-Aramaic as a Case Study | [
"cs.CL",
"cs.AI"
] | The documentation of the Neo-Aramaic dialects before their extinction has been described as the most urgent task in all of Semitology today. The death of this language will be an unfathomable loss to the descendents of the indigenous speakers of Aramaic, now predominantly diasporic after forced displacement due to viol... | {
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2412.04718 | Adaptive Optimization for Enhanced Efficiency in Large-Scale Language
Model Training | [
"cs.AI"
] | With the rapid development of natural language processing technology, large-scale language models (LLM) have achieved remarkable results in a variety of tasks. However, how to effectively train these huge models and improve their performance and computational efficiency remains an important challenge. This paper propos... | {
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2412.04719 | Mix-Modality Person Re-Identification: A New and Practical Paradigm | [
"cs.CV"
] | Current visible-infrared cross-modality person re-identification research has only focused on exploring the bi-modality mutual retrieval paradigm, and we propose a new and more practical mix-modality retrieval paradigm. Existing Visible-Infrared person re-identification (VI-ReID) methods have achieved some results in t... | {
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2412.04720 | Passive Six-Dimensional Movable Antenna (6DMA)-Assisted Multiuser
Communication | [
"cs.IT",
"eess.SP",
"math.IT"
] | Six-dimensional movable antenna (6DMA) is a promising solution for enhancing wireless network capacity through the adjustment of both three-dimensional (3D) positions and 3D rotations of distributed antenna surfaces. Previous works mainly consider 6DMA surfaces composed of active antenna elements, thus termed as active... | {
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2412.04726 | BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for
Varieties of English | [
"cs.CL",
"cs.AI"
] | Despite large language models (LLMs) being known to exhibit bias against non-mainstream varieties, there are no known labeled datasets for sentiment analysis of English. To address this gap, we introduce BESSTIE, a benchmark for sentiment and sarcasm classification for three varieties of English: Australian (en-AU), In... | {
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2412.04727 | Learning to Translate Noise for Robust Image Denoising | [
"eess.IV",
"cs.CV"
] | Deep learning-based image denoising techniques often struggle with poor generalization performance to out-of-distribution real-world noise. To tackle this challenge, we propose a novel noise translation framework that performs denoising on an image with translated noise rather than directly denoising an original noisy ... | {
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2412.04728 | Robots in the Wild: Contextually-Adaptive Human-Robot Interactions in
Urban Public Environments | [
"cs.RO",
"cs.HC"
] | The increasing transition of human-robot interaction (HRI) context from controlled settings to dynamic, real-world public environments calls for enhanced adaptability in robotic systems. This can go beyond algorithmic navigation or traditional HRI strategies in structured settings, requiring the ability to navigate com... | {
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2412.04729 | Espresso: High Compression For Rich Extraction From Videos for Your
Vision-Language Model | [
"cs.CV"
] | Most of the current vision-language models (VLMs) for videos struggle to understand videos longer than a few seconds. This is primarily due to the fact that they do not scale to utilizing a large number of frames. In order to address this limitation, we propose Espresso, a novel method that extracts and compresses spat... | {
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2412.04730 | Tuning Trains Speed in Railway Scheduling | [
"eess.SY",
"cs.SY"
] | Railway scheduling consists in ensuring that a set of trains evolve in a shared rail network without collisions, while meeting schedule constraints. This problem is notoriously difficult, even more in the case of uncertain or even unknown train speeds. We propose here a modeling and verification approach for railway sc... | {
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2412.04731 | TelOps: AI-driven Operations and Maintenance for Telecommunication
Networks | [
"cs.AI"
] | Telecommunication Networks (TNs) have become the most important infrastructure for data communications over the last century. Operations and maintenance (O&M) is extremely important to ensure the availability, effectiveness, and efficiency of TN communications. Different from the popular O&M technique for IT systems (e... | {
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2412.04733 | An Experimental Evaluation of Imputation Models for Spatial-Temporal
Traffic Data | [
"cs.LG"
] | Traffic data imputation is a critical preprocessing step in intelligent transportation systems, enabling advanced transportation services. Despite significant advancements in this field, selecting the most suitable model for practical applications remains challenging due to three key issues: 1) incomprehensive consider... | {
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2412.04734 | Sensing-Aided 6G Drone Communications: Real-World Datasets and
Demonstration | [
"eess.SP",
"cs.IT",
"math.IT"
] | In the advent of next-generation wireless communication, millimeter-wave (mmWave) and terahertz (THz) technologies are pivotal for their high data rate capabilities. However, their reliance on large antenna arrays and narrow directive beams for ensuring adequate receive signal power introduces significant beam training... | {
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2412.04735 | A dynamical measure of algorithmically infused visibility | [
"cs.SI",
"cs.CY",
"stat.AP",
"stat.OT"
] | This work focuses on the nature of visibility in societies where the behaviours of humans and algorithms influence each other - termed algorithmically infused societies. We propose a quantitative measure of visibility, with implications and applications to an array of disciplines including communication studies, politi... | {
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2412.04737 | Generative Humanization for Therapeutic Antibodies | [
"cs.LG",
"q-bio.QM"
] | Antibody therapies have been employed to address some of today's most challenging diseases, but must meet many criteria during drug development before reaching a patient. Humanization is a sequence optimization strategy that addresses one critical risk called immunogenicity - a patient's immune response to the drug - b... | {
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2412.04738 | DHIL-GT: Scalable Graph Transformer with Decoupled Hierarchy Labeling | [
"cs.LG"
] | Graph Transformer (GT) has recently emerged as a promising neural network architecture for learning graph-structured data. However, its global attention mechanism with quadratic complexity concerning the graph scale prevents wider application to large graphs. While current methods attempt to enhance GT scalability by a... | {
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2412.04739 | Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias | [
"cs.CV"
] | In medical image analysis, model predictions can be affected by sensitive attributes, such as race and gender, leading to fairness concerns and potential biases in diagnostic outcomes. To mitigate this, we present a causal modeling framework, which aims to reduce the impact of sensitive attributes on diagnostic predict... | {
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2412.04741 | Question Answering for Decisionmaking in Green Building Design: A
Multimodal Data Reasoning Method Driven by Large Language Models | [
"cs.AI",
"cs.CL",
"cs.HC"
] | In recent years, the critical role of green buildings in addressing energy consumption and environmental issues has become widely acknowledged. Research indicates that over 40% of potential energy savings can be achieved during the early design stage. Therefore, decision-making in green building design (DGBD), which is... | {
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2412.04746 | Diff4Steer: Steerable Diffusion Prior for Generative Music Retrieval
with Semantic Guidance | [
"cs.SD",
"cs.IR",
"cs.MM",
"eess.AS"
] | Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users' diverse and uncertain retrieval needs. To address this limitation, we introduce Diff4Steer, a novel generative retrieval framework that employs lightweight diffusion models to synthesize dive... | {
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2412.04747 | Code generation and runtime techniques for enabling data-efficient deep
learning training on GPUs | [
"cs.DC",
"cs.NE"
] | As deep learning models scale, their training cost has surged significantly. Due to both hardware advancements and limitations in current software stacks, the need for data efficiency has risen. Data efficiency refers to the effective hiding of data access latency and the avoidance of unnecessary data movements. Major ... | {
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2412.04748 | Decomposed Distribution Matching in Dataset Condensation | [
"cs.CV"
] | Dataset Condensation (DC) aims to reduce deep neural networks training efforts by synthesizing a small dataset such that it will be as effective as the original large dataset. Conventionally, DC relies on a costly bi-level optimization which prohibits its practicality. Recent research formulates DC as a distribution ma... | {
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2412.04749 | Machine learning algorithms to predict the risk of rupture of
intracranial aneurysms: a systematic review | [
"cs.CV",
"cs.LG",
"q-bio.QM"
] | Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. Th... | {
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2412.04752 | GABAR: Graph Attention-Based Action Ranking for Relational Policy
Learning | [
"cs.LG"
] | We propose a novel approach to learn relational policies for classical planning based on learning to rank actions. We introduce a new graph representation that explicitly captures action information and propose a Graph Neural Network architecture augmented with Gated Recurrent Units (GRUs) to learn action rankings. Our... | {
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2412.04755 | Latent Space Characterization of Autoencoder Variants | [
"cs.LG",
"cs.CV",
"cs.IT",
"math.IT"
] | Understanding the latent spaces learned by deep learning models is crucial in exploring how they represent and generate complex data. Autoencoders (AEs) have played a key role in the area of representation learning, with numerous regularization techniques and training principles developed not only to enhance their abil... | {
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2412.04756 | ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large
Language Models | [
"cs.CR",
"cs.CL"
] | The increasing frequency and sophistication of cybersecurity vulnerabilities in software systems underscore the urgent need for robust and effective methods of vulnerability assessment. However, existing approaches often rely on highly technical and abstract frameworks, which hinders understanding and increases the lik... | {
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2412.04757 | Ltri-LLM: Streaming Long Context Inference for LLMs with Training-Free
Dynamic Triangular Attention Pattern | [
"cs.CL",
"cs.LG"
] | The quadratic computational complexity of the attention mechanism in current Large Language Models (LLMs) renders inference with long contexts prohibitively expensive. To address this challenge, various approaches aim to retain critical portions of the context to optimally approximate Full Attention (FA) through Key-Va... | {
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2412.04758 | Measuring Goal-Directedness | [
"cs.AI",
"cs.LG"
] | We define maximum entropy goal-directedness (MEG), a formal measure of goal-directedness in causal models and Markov decision processes, and give algorithms for computing it. Measuring goal-directedness is important, as it is a critical element of many concerns about harm from AI. It is also of philosophical interest, ... | {
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2412.04759 | REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context
in New Environments | [
"cs.AI"
] | Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the most effective way to build generalist agents? We propose a novel approach to pre-train relatively small policies on relatively small datas... | {
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2412.04764 | Short-term Streamflow and Flood Forecasting based on Graph Convolutional
Recurrent Neural Network and Residual Error Learning | [
"cs.AI",
"cs.LG",
"physics.geo-ph"
] | Accurate short-term streamflow and flood forecasting are critical for mitigating river flood impacts, especially given the increasing climate variability. Machine learning-based streamflow forecasting relies on large streamflow datasets derived from rating curves. Uncertainties in rating curve modeling could introduce ... | {
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2412.04766 | DAWN-SI: Data-Aware and Noise-Informed Stochastic Interpolation for
Solving Inverse Problems | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Inverse problems, which involve estimating parameters from incomplete or noisy observations, arise in various fields such as medical imaging, geophysics, and signal processing. These problems are often ill-posed, requiring regularization techniques to stabilize the solution. In this work, we employ $\textit{Stochastic ... | {
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2412.04767 | Towards counterfactual fairness through auxiliary variables | [
"cs.LG",
"cs.DS",
"stat.ML"
] | The challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motivated substantial research in recent years. Counterfactual fairness ensures that predictions remain consistent across counterfactual variation... | {
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2412.04769 | Revitalizing Reconstruction Models for Multi-class Anomaly Detection via
Class-Aware Contrastive Learning | [
"cs.CV"
] | For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource management. Recent efforts have shifted toward training a single model capable of handling multiple classes. However, directly extending early AD m... | {
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2412.04774 | Foundation Models for Low-Resource Language Education (Vision Paper) | [
"cs.CL"
] | Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nua... | {
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2412.04775 | A Temporally Correlated Latent Exploration for Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Efficient exploration remains one of the longstanding problems of deep reinforcement learning. Instead of depending solely on extrinsic rewards from the environments, existing methods use intrinsic rewards to enhance exploration. However, we demonstrate that these methods are vulnerable to Noisy TV and stochasticity. T... | {
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2412.04776 | Megatron: Evasive Clean-Label Backdoor Attacks against Vision
Transformer | [
"cs.CV",
"cs.CR"
] | Vision transformers have achieved impressive performance in various vision-related tasks, but their vulnerability to backdoor attacks is under-explored. A handful of existing works focus on dirty-label attacks with wrongly-labeled poisoned training samples, which may fail if a benign model trainer corrects the labels. ... | {
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2412.04778 | IterL2Norm: Fast Iterative L2-Normalization | [
"cs.LG"
] | Transformer-based large language models are a memory-bound model whose operation is based on a large amount of data that are marginally reused. Thus, the data movement between a host and accelerator likely dictates the total wall-clock time. Layer normalization is one of the key workloads in the transformer model, foll... | {
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2412.04780 | Anomaly Detection and Classification in Knowledge Graphs | [
"cs.LG"
] | Anomalies such as redundant, inconsistent, contradictory, and deficient values in a Knowledge Graph (KG) are unavoidable, as these graphs are often curated manually, or extracted using machine learning and natural language processing techniques. Therefore, anomaly detection is a task that can enhance the quality of KGs... | {
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2412.04781 | DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental
Learning for Clustering in Transmissibility-based Online Structural Anomaly
Detection | [
"cs.LG",
"physics.data-an",
"stat.ML"
] | Clustering based on vibration responses, such as transmissibility functions (TFs), is promising in structural anomaly detection, but most existing approaches struggle with determining the optimal cluster number and handling high-dimensional streaming data, while their shallow structures also make them sensitive to manu... | {
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2412.04782 | A Survey of Sustainability in Large Language Models: Applications,
Economics, and Challenges | [
"cs.AI",
"cs.CE"
] | Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning. Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns rega... | {
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2412.04783 | KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment | [
"cs.CV",
"cs.AI",
"eess.SP"
] | Wireless sensing has recently found widespread applications in diverse environments, including homes, offices, and public spaces. By analyzing patterns in channel state information (CSI), it is possible to infer human actions for tasks such as person identification, gesture recognition, and fall detection. However, CSI... | {
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2412.04784 | NLP-ADBench: NLP Anomaly Detection Benchmark | [
"cs.CL",
"cs.LG"
] | Anomaly detection (AD) is a critical machine learning task with diverse applications in web systems, including fraud detection, content moderation, and user behavior analysis. Despite its significance, AD in natural language processing (NLP) remains underexplored, limiting advancements in detecting anomalies in text da... | {
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2412.04785 | Differentially Private Random Feature Model | [
"cs.LG",
"cs.CR"
] | Designing privacy-preserving machine learning algorithms has received great attention in recent years, especially in the setting when the data contains sensitive information. Differential privacy (DP) is a widely used mechanism for data analysis with privacy guarantees. In this paper, we produce a differentially privat... | {
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2412.04786 | Slicing Vision Transformer for Flexible Inference | [
"cs.CV",
"cs.LG"
] | Vision Transformers (ViT) is known for its scalability. In this work, we target to scale down a ViT to fit in an environment with dynamic-changing resource constraints. We observe that smaller ViTs are intrinsically the sub-networks of a larger ViT with different widths. Thus, we propose a general framework, named Scal... | {
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2412.04787 | Direct Quantized Training of Language Models with Stochastic Rounding | [
"cs.LG",
"cs.CL"
] | Although recent quantized Large Language Models (LLMs), such as BitNet, have paved the way for significant reduction in memory usage during deployment with binary or ternary weights, training these models still demands substantial memory footprints. This is partly because high-precision (i.e., unquantized) weight matri... | {
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2412.04788 | GUIDE: A Global Unified Inference Engine for Deploying Large Language
Models in Heterogeneous Environments | [
"cs.AI"
] | Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, and workload complexities.Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily ... | {
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2412.04789 | DrIFT: Autonomous Drone Dataset with Integrated Real and Synthetic Data,
Flexible Views, and Transformed Domains | [
"cs.CV"
] | Dependable visual drone detection is crucial for the secure integration of drones into the airspace. However, drone detection accuracy is significantly affected by domain shifts due to environmental changes, varied points of view, and background shifts. To address these challenges, we present the DrIFT dataset, specifi... | {
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2412.04792 | Multi-class heart disease Detection, Classification, and Prediction
using Machine Learning Models | [
"cs.AI"
] | Heart disease is a leading cause of premature death worldwide, particularly among middle-aged and older adults, with men experiencing a higher prevalence. According to the World Health Organization (WHO), non-communicable diseases, including heart disease, account for 25\% (17.9 million) of global deaths, with over 43,... | {
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2412.04795 | Ternary near-extremal self-dual codes of lengths $36$, $48$ and $60$ | [
"cs.IT",
"math.CO",
"math.IT"
] | For lengths $36$, $48$ and $60$, we construct new ternary near-extremal self-dual codes with weight enumerators for which no ternary near-extremal self-dual codes were previously known to exist. | {
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2412.04798 | A Multi-physics Model of Flow from Coronary Angiography: Insights into
Microvascular Function | [
"cs.CE"
] | Coronary Artery Disease (CAD) and Coronary Microvascular Disease (CMD) can lead to insufficient blood flow to the myocardium, affecting millions of people globally. Coronary angiography, one of the most commonly used imaging modalities, offers valuable information that assists in diagnosing these diseases. However, the... | {
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2412.04799 | Estimating the treatment effect over time under general interference
through deep learner integrated TMLE | [
"cs.AI"
] | Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal inference methods fail here due to their assumption of independent individuals. We introduce DeepNetTMLE, a deep-learning-enhanced Targeted Maximum Likelihood Estimation (TMLE) m... | {
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2412.04802 | Modality Decoupling is All You Need: A Simple Solution for Unsupervised
Hyperspectral Image Fusion | [
"eess.IV",
"cs.CV"
] | Hyperspectral Image Fusion (HIF) aims to fuse low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) to reconstruct high spatial and high spectral resolution images. Current methods typically apply direct fusion from the two modalities without valid supervision, failing to full... | {
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2412.04805 | A Unified Approach for Multi-granularity Search over Spatial Datasets | [
"cs.DB"
] | There has been increased interest in data search as a means to find relevant datasets or data points in data lakes and repositories. Although approaches have been proposed to support spatial dataset search and data point search, they consider the two types of searches independently. To enable search operations ranging ... | {
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2412.04806 | Rethinking Time Series Forecasting with LLMs via Nearest Neighbor
Contrastive Learning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Adapting Large Language Models (LLMs) that are extensively trained on abundant text data, and customizing the input prompt to enable time series forecasting has received considerable attention. While recent work has shown great potential for adapting the learned prior of LLMs, the formulation of the prompt to finetune ... | {
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2412.04812 | Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and
Perspectives | [
"eess.IV",
"cs.CV"
] | Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medica... | {
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2412.04814 | LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment | [
"cs.CV"
] | Recent advancements in text-to-video (T2V) generative models have shown impressive capabilities. However, these models are still inadequate in aligning synthesized videos with human preferences (e.g., accurately reflecting text descriptions), which is particularly difficult to address, as human preferences are inherent... | {
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2412.04820 | Assessing Similarity Measures for the Evaluation of Human-Robot Motion
Correspondence | [
"cs.RO",
"cs.HC"
] | One key area of research in Human-Robot Interaction is solving the human-robot correspondence problem, which asks how a robot can learn to reproduce a human motion demonstration when the human and robot have different dynamics and kinematic structures. Evaluating these correspondence problem solutions often requires th... | {
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2412.04821 | CCS: Continuous Learning for Customized Incremental Wireless Sensing
Services | [
"cs.LG"
] | Wireless sensing has made significant progress in tasks ranging from action recognition, vital sign estimation, pose estimation, etc. After over a decade of work, wireless sensing currently stands at the tipping point transitioning from proof-of-concept systems to the large-scale deployment. We envision a future servic... | {
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2412.04826 | Pushing Rendering Boundaries: Hard Gaussian Splatting | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magnitude of view-space positional gradients to grow Gaussians to reduce rendering loss. However, this average operation smooths the positional g... | {
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2412.04827 | PanoDreamer: 3D Panorama Synthesis from a Single Image | [
"cs.CV",
"cs.GR"
] | In this paper, we present PanoDreamer, a novel method for producing a coherent 360$^\circ$ 3D scene from a single input image. Unlike existing methods that generate the scene sequentially, we frame the problem as single-image panorama and depth estimation. Once the coherent panoramic image and its corresponding depth a... | {
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2412.04828 | DAug: Diffusion-based Channel Augmentation for Radiology Image Retrieval
and Classification | [
"cs.CV"
] | Medical image understanding requires meticulous examination of fine visual details, with particular regions requiring additional attention. While radiologists build such expertise over years of experience, it is challenging for AI models to learn where to look with limited amounts of training data. This limitation resu... | {
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2412.04829 | Learning-based Control for Tendon-Driven Continuum Robotic Arms | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper presents a learning-based approach for centralized position control of Tendon Driven Continuum Robots (TDCRs) using Deep Reinforcement Learning (DRL), with a particular focus on the Sim-to-Real transfer of control policies. The proposed control method employs the Modified Transpose Jacobian (MTJ) control str... | {
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2412.04831 | Customized Generation Reimagined: Fidelity and Editability Harmonized | [
"cs.CV"
] | Customized generation aims to incorporate a novel concept into a pre-trained text-to-image model, enabling new generations of the concept in novel contexts guided by textual prompts. However, customized generation suffers from an inherent trade-off between concept fidelity and editability, i.e., between precisely model... | {
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2412.04832 | WRF-GS: Wireless Radiation Field Reconstruction with 3D Gaussian
Splatting | [
"cs.NI",
"cs.AI",
"cs.LG"
] | Wireless channel modeling plays a pivotal role in designing, analyzing, and optimizing wireless communication systems. Nevertheless, developing an effective channel modeling approach has been a longstanding challenge. This issue has been escalated due to the denser network deployment, larger antenna arrays, and wider b... | {
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} |
2412.04833 | Wavelet Diffusion Neural Operator | [
"cs.LG"
] | Simulating and controlling physical systems described by partial differential equations (PDEs) are crucial tasks across science and engineering. Recently, diffusion generative models have emerged as a competitive class of methods for these tasks due to their ability to capture long-term dependencies and model high-dime... | {
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2412.04835 | Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards
for Visuomotor Robot Policy Alignment | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | Visuomotor robot policies, increasingly pre-trained on large-scale datasets, promise significant advancements across robotics domains. However, aligning these policies with end-user preferences remains a challenge, particularly when the preferences are hard to specify. While reinforcement learning from human feedback (... | {
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2412.04836 | Adaptive Dropout for Pruning Conformers | [
"cs.CL",
"eess.AS"
] | This paper proposes a method to effectively perform joint training-and-pruning based on adaptive dropout layers with unit-wise retention probabilities. The proposed method is based on the estimation of a unit-wise retention probability in a dropout layer. A unit that is estimated to have a small retention probability c... | {
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} |
2412.04842 | UniMLVG: Unified Framework for Multi-view Long Video Generation with
Comprehensive Control Capabilities for Autonomous Driving | [
"cs.CV"
] | The creation of diverse and realistic driving scenarios has become essential to enhance perception and planning capabilities of the autonomous driving system. However, generating long-duration, surround-view consistent driving videos remains a significant challenge. To address this, we present UniMLVG, a unified framew... | {
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} |
2412.04845 | Using Machine Learning to Discover Parsimonious and
Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff
Dynamics | [
"cs.LG",
"cs.AI"
] | Despite excellent real-world predictive performance of modern machine learning (ML) methods, many scientists hesitate to discard traditional physical-conceptual (PC) approaches due to their relative interpretability, which contributes to credibility during decision-making. In this context, a currently underexplored asp... | {
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} |
2412.04846 | eXpath: Explaining Knowledge Graph Link Prediction with Ontological
Closed Path Rules | [
"cs.AI",
"cs.DB",
"cs.IR",
"cs.LG"
] | Link prediction (LP) is crucial for Knowledge Graphs (KG) completion but commonly suffers from interpretability issues. While several methods have been proposed to explain embedding-based LP models, they are generally limited to local explanations on KG and are deficient in providing human interpretable semantics. Base... | {
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} |
2412.04847 | MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for
Generalist Agents | [
"cs.NE",
"cs.AI",
"cs.LG"
] | Currently, state-of-the-art RL methods excel in single-task settings, but they still struggle to generalize across multiple tasks due to catastrophic forgetting challenges, where previously learned tasks are forgotten as new tasks are introduced. This multi-task learning capability is significantly important for genera... | {
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} |
2412.04852 | SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image
Diffusion Models | [
"cs.CV"
] | Recent advances in large-scale text-to-image (T2I) diffusion models have enabled a variety of downstream applications, including style customization, subject-driven personalization, and conditional generation. As T2I models require extensive data and computational resources for training, they constitute highly valued i... | {
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} |
2412.04853 | Budgeted Spatial Data Acquisition: When Coverage and Connectivity Matter | [
"cs.DB"
] | Data is undoubtedly becoming a commodity like oil, land, and labor in the 21st century. Although there have been many successful marketplaces for data trading, the existing data marketplaces lack consideration of the case where buyers want to acquire a collection of datasets (instead of one), and the overall spatial co... | {
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} |
2412.04855 | GS-Matching: Reconsidering Feature Matching task in Point Cloud
Registration | [
"cs.CV"
] | Traditional point cloud registration (PCR) methods for feature matching often employ the nearest neighbor policy. This leads to many-to-one matches and numerous potential inliers without any corresponding point. Recently, some approaches have framed the feature matching task as an assignment problem to achieve optimal ... | {
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} |
2412.04856 | Can Large Language Models Effectively Process and Execute Financial
Trading Instructions? | [
"cs.CE"
] | The development of Large Language Models (LLMs) has created transformative opportunities for the financial industry, especially in the area of financial trading. However, how to integrate LLMs with trading systems has become a challenge. To address this problem, we propose an intelligent trade order recognition pipelin... | {
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} |
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