id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.19832 | Sensitive Content Classification in Social Media: A Holistic Resource
and Evaluation | [
"cs.CL"
] | The detection of sensitive content in large datasets is crucial for ensuring that shared and analysed data is free from harmful material. However, current moderation tools, such as external APIs, suffer from limitations in customisation, accuracy across diverse sensitive categories, and privacy concerns. Additionally, ... | {
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2411.19835 | Feedback-driven object detection and iterative model improvement | [
"cs.CV",
"cs.LG"
] | Automated object detection has become increasingly valuable across diverse applications, yet efficient, high-quality annotation remains a persistent challenge. In this paper, we present the development and evaluation of a platform designed to interactively improve object detection models. The platform allows uploading ... | {
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2411.19841 | Parallel Stacked Aggregated Network for Voice Authentication in
IoT-Enabled Smart Devices | [
"cs.SD",
"cs.CR",
"cs.NE",
"eess.AS"
] | Voice authentication on IoT-enabled smart devices has gained prominence in recent years due to increasing concerns over user privacy and security. The current authentication systems are vulnerable to different voice-spoofing attacks (e.g., replay, voice cloning, and audio deepfakes) that mimic legitimate voices to dece... | {
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2411.19842 | Scaling Transformers for Low-Bitrate High-Quality Speech Coding | [
"eess.AS",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.SP"
] | The tokenization of speech with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Traditionally such tokenization models have concentrated on low parameter-count architectures using only components with strong inductive bias... | {
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2411.19845 | A Visual-inertial Localization Algorithm using Opportunistic Visual
Beacons and Dead-Reckoning for GNSS-Denied Large-scale Applications | [
"cs.CV",
"cs.LG",
"eess.SP"
] | With the development of smart cities, the demand for continuous pedestrian navigation in large-scale urban environments has significantly increased. While global navigation satellite systems (GNSS) provide low-cost and reliable positioning services, they are often hindered in complex urban canyon environments. Thus, ex... | {
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2411.19853 | Towards Class-wise Robustness Analysis | [
"cs.LG",
"cs.CV"
] | While being very successful in solving many downstream tasks, the application of deep neural networks is limited in real-life scenarios because of their susceptibility to domain shifts such as common corruptions, and adversarial attacks. The existence of adversarial examples and data corruption significantly reduces th... | {
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2411.19854 | Timely and Energy-Efficient Multi-Step Update Processing | [
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | This work explores systems where source updates require multiple sequential processing steps. We model and analyze the Age of Information (AoI) performance of various system designs under both parallel and series server setups. In parallel setups, each processor executes all computation steps with multiple processors w... | {
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2411.19855 | Artificial intelligence contribution to translation industry: looking
back and forward | [
"cs.CL"
] | This study provides a comprehensive analysis of artificial intelligence (AI) contribution to translation industry (ACTI) research, synthesizing it over forty-one years from 1980-2024. 13220 articles were retrieved from three sources, namely WoS, Scopus, and Lens. We provided two types of analysis, viz., scientometric a... | {
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2411.19858 | What fifty-one years of Linguistics and Artificial Intelligence research
tell us about their correlation: A scientometric review | [
"cs.CL"
] | There is a strong correlation between linguistics and artificial intelligence (AI), best manifested by deep learning language models. This study provides a thorough scientometric analysis of this correlation, synthesizing the intellectual production during 51 years, from 1974 to 2024. It involves 5750 Web of Science-in... | {
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2411.19860 | SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection | [
"cs.CV",
"cs.LG"
] | In this work, we present SpaRC, a novel Sparse fusion transformer for 3D perception that integrates multi-view image semantics with Radar and Camera point features. The fusion of radar and camera modalities has emerged as an efficient perception paradigm for autonomous driving systems. While conventional approaches uti... | {
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2411.19862 | Cross-Domain Recommendation Meets Large Language Models | [
"cs.IR"
] | Cross-domain recommendation (CDR) has emerged as a promising solution to the cold-start problem, faced by single-domain recommender systems. However, existing CDR models rely on complex neural architectures, large datasets, and significant computational resources, making them less effective in data-scarce scenarios or ... | {
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2411.19865 | Reverse Thinking Makes LLMs Stronger Reasoners | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Reverse thinking plays a crucial role in human reasoning. Humans can reason not only from a problem to a solution but also in reverse, i.e., start from the solution and reason towards the problem. This often enhances overall reasoning performance as it enables consistency checks between their forward and backward think... | {
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2411.19866 | Misinformation Dissemination: Effects of Network Density in Segregated
Communities | [
"cs.SI",
"cs.CY",
"cs.MA",
"physics.soc-ph"
] | Understanding the relationship between network features and misinformation propagation is crucial for mitigating the spread of false information. Here, we investigate how network density and segregation affect the dissemination of misinformation using a susceptible-infectious-recovered framework. We find that a higher ... | {
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2411.19869 | AIDetx: a compression-based method for identification of
machine-learning generated text | [
"cs.CL",
"cs.LG"
] | This paper introduces AIDetx, a novel method for detecting machine-generated text using data compression techniques. Traditional approaches, such as deep learning classifiers, often suffer from high computational costs and limited interpretability. To address these limitations, we propose a compression-based classifica... | {
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2411.19870 | DeMo: Decoupled Momentum Optimization | [
"cs.LG",
"cs.AI"
] | Training large neural networks typically requires sharing gradients between accelerators through specialized high-speed interconnects. Drawing from the signal processing principles of frequency decomposition and energy compaction, we demonstrate that synchronizing full optimizer states and model parameters during train... | {
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2411.19875 | Enhanced anomaly detection in well log data through the application of
ensemble GANs | [
"physics.geo-ph",
"cs.AI",
"cs.LG"
] | Although generative adversarial networks (GANs) have shown significant success in modeling data distributions for image datasets, their application to structured or tabular data, such as well logs, remains relatively underexplored. This study extends the ensemble GANs (EGANs) framework to capture the distribution of we... | {
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2411.19876 | LUMIA: Linear probing for Unimodal and MultiModal Membership Inference
Attacks leveraging internal LLM states | [
"cs.CR",
"cs.AI"
] | Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. To address this, we propose the use of Linear Probes ... | {
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2411.19882 | Open source Differentiable ODE Solving Infrastructure | [
"cs.LG"
] | Ordinary Differential Equations (ODEs) are widely used in physics, chemistry, and biology to model dynamic systems, including reaction kinetics, population dynamics, and biological processes. In this work, we integrate GPU-accelerated ODE solvers into the open-source DeepChem framework, making these tools easily access... | {
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2411.19886 | PDDLFuse: A Tool for Generating Diverse Planning Domains | [
"cs.AI"
] | Various real-world challenges require planning algorithms that can adapt to a broad range of domains. Traditionally, the creation of planning domains has relied heavily on human implementation, which limits the scale and diversity of available domains. While recent advancements have leveraged generative AI technologies... | {
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2411.19888 | FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For
Anomaly Segmentation | [
"cs.CV",
"cs.LG"
] | Anomaly segmentation is a valuable computer vision task for safety-critical applications that need to be aware of unexpected events. Current state-of-the-art (SOTA) scene-level anomaly segmentation approaches rely on diverse inlier class labels during training, limiting their ability to leverage vast unlabeled datasets... | {
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2411.19890 | Reverse-type Data Processing Inequality | [
"quant-ph",
"cs.IT",
"math-ph",
"math.IT",
"math.MP"
] | The quantum data processing inequality asserts that two quantum states become harder to distinguish when a noisy channel is applied. On the other hand, a reverse quantum data processing inequality characterizes whether distinguishability is preserved after the application of a noisy channel. In this work, we explore th... | {
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2411.19894 | Noncommutative Model Selection and the Data-Driven Estimation of Real
Cohomology Groups | [
"cs.CG",
"cs.LG",
"math.AT"
] | We propose three completely data-driven methods for estimating the real cohomology groups $H^k (X ; \mathbb{R})$ of a compact metric-measure space $(X, d_X, \mu_X)$ embedded in a metric-measure space $(Y,d_Y,\mu_Y)$, given a finite set of points $S$ sampled from a uniform distrbution $\mu_X$ on $X$, possibly corrupted ... | {
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2411.19895 | GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting | [
"cs.CV",
"cs.CR"
] | 3D Gaussian Splatting (3DGS) has recently created impressive assets for various applications. However, the copyright of these assets is not well protected as existing watermarking methods are not suited for 3DGS considering security, capacity, and invisibility. Besides, these methods often require hours or even days fo... | {
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2411.19896 | Efficient quantum-enhanced classical simulation for patches of quantum
landscapes | [
"quant-ph",
"cs.LG",
"stat.ML"
] | Understanding the capabilities of classical simulation methods is key to identifying where quantum computers are advantageous. Not only does this ensure that quantum computers are used only where necessary, but also one can potentially identify subroutines that can be offloaded onto a classical device. In this work, we... | {
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2411.19901 | Memory Efficient GPU-based Label Propagation Algorithm (LPA) for
Community Detection on Large Graphs | [
"cs.DC",
"cs.SI"
] | Community detection involves grouping nodes in a graph with dense connections within groups, than between them. We previously proposed efficient multicore (GVE-LPA) and GPU-based ($\nu$-LPA) implementations of Label Propagation Algorithm (LPA) for community detection. However, these methods incur high memory overhead d... | {
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2411.19902 | Noncommutative Model Selection for Data Clustering and Dimension
Reduction Using Relative von Neumann Entropy | [
"stat.ML",
"cs.LG",
"stat.OT"
] | We propose a pair of completely data-driven algorithms for unsupervised classification and dimension reduction, and we empirically study their performance on a number of data sets, both simulated data in three-dimensions and images from the COIL-20 data set. The algorithms take as input a set of points sampled from a u... | {
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2411.19903 | $C^{3}$-NeRF: Modeling Multiple Scenes via Conditional-cum-Continual
Neural Radiance Fields | [
"cs.CV",
"cs.LG"
] | Neural radiance fields (NeRF) have exhibited highly photorealistic rendering of novel views through per-scene optimization over a single 3D scene. With the growing popularity of NeRF and its variants, they have become ubiquitous and have been identified as efficient 3D resources. However, they are still far from being ... | {
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2411.19906 | Classical and Quantum Algorithms for the Deterministic L-system
Inductive Inference Problem | [
"quant-ph",
"cs.CL",
"cs.DS",
"cs.FL",
"cs.LG"
] | L-systems can be made to model and create simulations of many biological processes, such as plant development. Finding an L-system for a given process is typically solved by hand, by experts, in a massively time-consuming process. It would be significant if this could be done automatically from data, such as from seque... | {
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2411.19908 | Another look at inference after prediction | [
"stat.ML",
"cs.LG"
] | From structural biology to epidemiology, predictions from machine learning (ML) models increasingly complement costly gold-standard data to enable faster, more affordable, and scalable scientific inquiry. In response, prediction-based (PB) inference has emerged to accommodate statistical analysis using a large volume o... | {
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2411.19913 | Quantifying the synthetic and real domain gap in aerial scene
understanding | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Quantifying the gap between synthetic and real-world imagery is essential for improving both transformer-based models - that rely on large volumes of data - and datasets, especially in underexplored domains like aerial scene understanding where the potential impact is significant. This paper introduces a novel methodol... | {
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2411.19918 | Handling irresolvable conflicts in the Semantic Web: an RDF-based
conflict-tolerant version of the Deontic Traditional Scheme | [
"cs.AI"
] | This paper presents a new ontology that implements the well-known Deontic Traditional Scheme in RDFs and SPARQL, fit to handle irresolvable conflicts, i.e., situations in which two or more statements prescribe conflicting obligations, prohibitions, or permissions, with none of them being "stronger" than the other one(s... | {
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2411.19921 | SIMS: Simulating Human-Scene Interactions with Real World Script
Planning | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.GR"
] | Simulating long-term human-scene interaction is a challenging yet fascinating task. Previous works have not effectively addressed the generation of long-term human scene interactions with detailed narratives for physics-based animation. This paper introduces a novel framework for the planning and controlling of long-ho... | {
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2411.19922 | Dynamic EEG-fMRI mapping: Revealing the relationship between brain
connectivity and cognitive state | [
"cs.LG",
"cs.AI"
] | This study investigated the dynamic connectivity patterns between EEG and fMRI modalities, contributing to our understanding of brain network interactions. By employing a comprehensive approach that integrated static and dynamic analyses of EEG-fMRI data, we were able to uncover distinct connectivity states and charact... | {
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2411.19923 | Scalable Out-of-distribution Robustness in the Presence of Unobserved
Confounders | [
"cs.LG",
"stat.ML"
] | We consider the task of out-of-distribution (OOD) generalization, where the distribution shift is due to an unobserved confounder ($Z$) affecting both the covariates ($X$) and the labels ($Y$). In this setting, traditional assumptions of covariate and label shift are unsuitable due to the confounding, which introduces ... | {
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2411.19930 | On Domain-Specific Post-Training for Multimodal Large Language Models | [
"cs.CL",
"cs.CV",
"cs.LG"
] | Recent years have witnessed the rapid development of general multimodal large language models (MLLMs). However, adapting general MLLMs to specific domains, such as scientific fields and industrial applications, remains less explored. This paper systematically investigates domain adaptation of MLLMs through post-trainin... | {
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2411.19939 | VLSBench: Unveiling Visual Leakage in Multimodal Safety | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.CV"
] | Safety concerns of Multimodal large language models (MLLMs) have gradually become an important problem in various applications. Surprisingly, previous works indicate a counter-intuitive phenomenon that using textual unlearning to align MLLMs achieves comparable safety performances with MLLMs trained with image-text pai... | {
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2411.19941 | Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA
Benchmark | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Following the successful 2023 edition, we organised the Second Perception Test challenge as a half-day workshop alongside the IEEE/CVF European Conference on Computer Vision (ECCV) 2024, with the goal of benchmarking state-of-the-art video models and measuring the progress since last year using the Perception Test benc... | {
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2411.19942 | Free-form Generation Enhances Challenging Clothed Human Modeling | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Achieving realistic animated human avatars requires accurate modeling of pose-dependent clothing deformations. Existing learning-based methods heavily rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like SMPL to model deformation. However, these methods struggle to handle loose clothing, such ... | {
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2411.19943 | Critical Tokens Matter: Token-Level Contrastive Estimation Enhances
LLM's Reasoning Capability | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Mathematical reasoning tasks pose significant challenges for large language models (LLMs) because they require precise logical deduction and sequence analysis. In this work, we introduce the concept of critical tokens -- elements within reasoning trajectories that significantly influence incorrect outcomes. We present ... | {
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2411.19946 | DELT: A Simple Diversity-driven EarlyLate Training for Dataset
Distillation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advances in dataset distillation have led to solutions in two main directions. The conventional batch-to-batch matching mechanism is ideal for small-scale datasets and includes bi-level optimization methods on models and syntheses, such as FRePo, RCIG, and RaT-BPTT, as well as other methods like distribution mat... | {
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2411.19950 | AlphaTablets: A Generic Plane Representation for 3D Planar
Reconstruction from Monocular Videos | [
"cs.CV",
"cs.LG"
] | We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation. By representing 3D planes as rectangles with alpha channels, AlphaTablets combine the advantages of current 2D and 3D plane representations, enabling accurate, consistent and ... | {
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2411.19951 | T2Vid: Translating Long Text into Multi-Image is the Catalyst for
Video-LLMs | [
"cs.CV",
"cs.CL",
"cs.LG"
] | The success of Multimodal Large Language Models (MLLMs) in the image domain has garnered wide attention from the research community. Drawing on previous successful experiences, researchers have recently explored extending the success to the video understanding realms. Apart from training from scratch, an efficient way ... | {
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2412.00011 | The use of knowledge in open-ended systems | [
"cs.NE",
"cs.AI",
"cs.LO",
"econ.TH"
] | Economists model knowledge use and acquisition as a cause-and-effect calculus associating observations made by a decision-maker about their world with possible underlying causes. Knowledge models are well-established for static contexts, but not for contexts of innovative and unbounded change. We develop a representati... | {
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2412.00012 | The Femininomenon of Inequality: A Data-Driven Analysis and Cluster
Profiling in Indonesia | [
"cs.CY",
"cs.LG"
] | This study addresses the persistent challenges of Workplace Gender Equality (WGE) in Indonesia, examining regional disparities in gender empowerment and inequality through the Gender Empowerment Index (IDG) and Gender Inequality Index (IKG). Despite Indonesia's economic growth and incremental progress in gender equalit... | {
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2412.00017 | Frost/Defrost Models for Air-Source Heat Pumps with Retained Water
Refreezing Considered | [
"eess.SY",
"cs.SY"
] | Cyclic frosting and defrosting operations constitute a common characteristic of air-source heat pumps in cold climates during winter. Simulation models that can capture simultaneous heat and mass transfer phenomena associated with frost/defrost behaviors and their impact on the overall heat pump system performance are ... | {
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2412.00020 | Partitioning Message Passing for Graph Fraud Detection | [
"cs.AI",
"cs.LG",
"cs.SI"
] | Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing GNN-based GFD models are designed to augment graph structure to accommodate the inductive bias of GNNs towards homophily, by excluding hete... | {
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2412.00023 | Evaluating Large Language Models on Business Process Modeling:
Framework, Benchmark, and Self-Improvement Analysis | [
"cs.DB"
] | Large Language Models (LLMs) are rapidly transforming various fields, and their potential in Business Process Management (BPM) is substantial. This paper assesses the capabilities of LLMs on business process modeling using a framework for automating this task, a comprehensive benchmark, and an analysis of LLM self-impr... | {
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2412.00026 | Spatial-variant causal Bayesian inference for rapid seismic ground
failures and impacts estimation | [
"physics.geo-ph",
"cs.AI"
] | Rapid and accurate estimation of post-earthquake ground failures and building damage is critical for effective post-disaster responses. Progression in remote sensing technologies has paved the way for rapid acquisition of detailed, localized data, enabling swift hazard estimation through analysis of correlation deviati... | {
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2412.00028 | A Context-Based Numerical Format Prediction for a Text-To-Speech System | [
"eess.AS",
"cs.LG"
] | Many of the existing TTS systems cannot accurately synthesize text containing a variety of numerical formats, resulting in reduced intelligibility of the synthesized speech. This research aims to develop a numerical format classifier that can classify six types of numeric contexts. Experiments were carried out using th... | {
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2412.00029 | Planning vs Reasoning: Ablations to Test Capabilities of LoRA layers | [
"cs.AI"
] | Low-Rank Adaptation (LoRA) layers have emerged as a promising approach for efficient model fine-tuning, but their capabilities and limitations have not been fully explored. This paper: 1) Investigates the fundamental question of whether LoRA layers are effective at increasing reasoning + planning abilities 2) We introd... | {
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2412.00033 | Can an AI Agent Safely Run a Government? Existence of Probably
Approximately Aligned Policies | [
"cs.AI",
"cs.CY"
] | While autonomous agents often surpass humans in their ability to handle vast and complex data, their potential misalignment (i.e., lack of transparency regarding their true objective) has thus far hindered their use in critical applications such as social decision processes. More importantly, existing alignment methods... | {
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2412.00034 | Data-Driven Prescriptive Analytics Applications: A Comprehensive Survey | [
"cs.DB"
] | Prescriptive Analytics (PSA), an emerging business analytics field suggesting concrete options for solving business problems, has seen an increasing amount of interest after more than a decade of multidisciplinary research. This paper is a comprehensive survey of existing applications within PSA in terms of their use c... | {
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2412.00036 | Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial
Market Dynamics | [
"q-fin.CP",
"cs.AI",
"cs.CE",
"q-fin.PM"
] | We propose a highly efficient and accurate methodology for generating synthetic financial market data using a diffusion model approach. The synthetic data produced by our methodology align closely with observed market data in several key aspects: (i) they pass the two-sample Cramer - von Mises test for portfolios of as... | {
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2412.00044 | Creating Hierarchical Dispositions of Needs in an Agent | [
"cs.LG",
"cs.AI"
] | We present a novel method for learning hierarchical abstractions that prioritize competing objectives, leading to improved global expected rewards. Our approach employs a secondary rewarding agent with multiple scalar outputs, each associated with a distinct level of abstraction. The traditional agent then learns to ma... | {
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2412.00049 | A Survey of Recent Advances and Challenges in Deep Audio-Visual
Correlation Learning | [
"cs.MM",
"cs.AI",
"cs.CV",
"cs.SD",
"eess.AS"
] | Audio-visual correlation learning aims to capture and understand natural phenomena between audio and visual data. The rapid growth of Deep Learning propelled the development of proposals that process audio-visual data and can be observed in the number of proposals in the past years. Thus encouraging the development of ... | {
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2412.00050 | Mapping waterways worldwide with deep learning | [
"cs.CV",
"cs.LG"
] | Waterways shape earth system processes and human societies, and a better understanding of their distribution can assist in a range of applications from earth system modeling to human development and disaster response. Most efforts to date to map the world's waterways have required extensive modeling and contextual expe... | {
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2412.00051 | TransFair: Transferring Fairness from Ocular Disease Classification to
Progression Prediction | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.CY"
] | The use of artificial intelligence (AI) in automated disease classification significantly reduces healthcare costs and improves the accessibility of services. However, this transformation has given rise to concerns about the fairness of AI, which disproportionately affects certain groups, particularly patients from und... | {
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2412.00052 | Brick Kiln Dataset for Pakistan's IGP Region Using AI | [
"cs.CV"
] | Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution sources. To address this, we present a two-fold AI approach that combines lo... | {
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2412.00053 | LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series
Forecasting | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Recent research has shown that large language models (LLMs) can be effectively used for real-world time series forecasting due to their strong natural language understanding capabilities. However, aligning time series into semantic spaces of LLMs comes with high computational costs and inference complexity, particularl... | {
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2412.00054 | Less is More: Efficient Model Merging with Binary Task Switch | [
"cs.LG"
] | As an effective approach to equip models with multi-task capabilities without additional training, model merging has garnered significant attention. However, existing methods face challenges of redundant parameter conflicts and the excessive storage burden of parameters. In this work, through controlled experiments, we... | {
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2412.00055 | High-precision medical speech recognition through synthetic data and
semantic correction: UNITED-MEDASR | [
"eess.AS",
"cs.CL",
"cs.SD"
] | Automatic Speech Recognition (ASR) systems in the clinical domain face significant challenges, notably the need to recognise specialised medical vocabulary accurately and meet stringent precision requirements. We introduce United-MedASR, a novel architecture that addresses these challenges by integrating synthetic data... | {
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2412.00056 | Improving Medical Diagnostics with Vision-Language Models: Convex
Hull-Based Uncertainty Analysis | [
"cs.CV",
"cs.AI"
] | In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs' consistency and uncertainty, particularly in critical applications such as healthcare, which demand a hig... | {
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2412.00058 | Real-time volumetric free-hand ultrasound imaging for large-sized
organs: A study of imaging the whole spine | [
"eess.IV",
"cs.CV"
] | Three-dimensional (3D) ultrasound imaging can overcome the limitations of conventional two dimensional (2D) ultrasound imaging in structural observation and measurement. However, conducting volumetric ultrasound imaging for large-sized organs still faces difficulties including long acquisition time, inevitable patient ... | {
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2412.00059 | Adaptive Coordinate-Wise Step Sizes for Quasi-Newton Methods: A
Learning-to-Optimize Approach | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Tuning effective step sizes is crucial for the stability and efficiency of optimization algorithms. While adaptive coordinate-wise step sizes tuning methods have been explored in first-order methods, second-order methods still lack efficient techniques. Current approaches, including hypergradient descent and cutting pl... | {
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2412.00060 | MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating
Multimodal Large Language Models Understanding of Complex Image | [
"cs.CV",
"cs.AI"
] | Multimodal large language models (MLLMs) have shown remarkable progress in high-level semantic tasks such as visual question answering, image captioning, and emotion recognition. However, despite advancements, there remains a lack of standardized benchmarks for evaluating MLLMs performance in multi-object sentiment ana... | {
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2412.00061 | Speculative Decoding with CTC-based Draft Model for LLM Inference
Acceleration | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usually introduces a draft model to assist the base LLM where the draft model produces drafts and the base... | {
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2412.00062 | Deep Learning-Based Electricity Price Forecast for Virtual Bidding in
Wholesale Electricity Market | [
"cs.LG",
"q-fin.CP"
] | Virtual bidding plays an important role in two-settlement electric power markets, as it can reduce discrepancies between day-ahead and real-time markets. Renewable energy penetration increases volatility in electricity prices, making accurate forecasting critical for virtual bidders, reducing uncertainty and maximizing... | {
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2412.00064 | DiffGuard: Text-Based Safety Checker for Diffusion Models | [
"cs.CV",
"cs.AI"
] | Recent advances in Diffusion Models have enabled the generation of images from text, with powerful closed-source models like DALL-E and Midjourney leading the way. However, open-source alternatives, such as StabilityAI's Stable Diffusion, offer comparable capabilities. These open-source models, hosted on Hugging Face, ... | {
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2412.00065 | DYRECT Computed Tomography: DYnamic Reconstruction of Events on a
Continuous Timescale | [
"eess.IV",
"cs.CV"
] | Time-resolved high-resolution X-ray Computed Tomography (4D $\mu$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the... | {
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2412.00067 | Targeted Therapy in Data Removal: Object Unlearning Based on Scene
Graphs | [
"cs.CV",
"cs.LG"
] | Users may inadvertently upload personally identifiable information (PII) to Machine Learning as a Service (MLaaS) providers. When users no longer want their PII on these services, regulations like GDPR and COPPA mandate a right to forget for these users. As such, these services seek efficient methods to remove the infl... | {
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2412.00068 | Enhanced Lung Cancer Survival Prediction using Semi-Supervised
Pseudo-Labeling and Learning from Diverse PET/CT Datasets | [
"cs.CV",
"physics.data-an"
] | Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets to enhance lung cancer (LCa) survival predictions, analyzing Handcrafted and Deep Radiomic Features (HRF/DRF) from PET/CT scans with Hybrid Machine Learning Systems (HMLS). Methods: We collected 199 LCa patie... | {
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2412.00069 | Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE
Layer Pruning | [
"cs.LG",
"cs.CL"
] | Mixture-of-Experts (MoE) has garnered significant attention for its ability to scale up neural networks while utilizing the same or even fewer active parameters. However, MoE does not alleviate the massive memory requirements of networks, which limits their practicality in real-world applications, especially in the era... | {
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2412.00070 | Recurrent Stochastic Configuration Networks with Hybrid Regularization
for Nonlinear Dynamics Modelling | [
"cs.LG",
"cs.AI",
"cs.CE",
"stat.ML"
] | Recurrent stochastic configuration networks (RSCNs) have shown great potential in modelling nonlinear dynamic systems with uncertainties. This paper presents an RSCN with hybrid regularization to enhance both the learning capacity and generalization performance of the network. Given a set of temporal data, the well-kno... | {
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2412.00071 | COAP: Memory-Efficient Training with Correlation-Aware Gradient
Projection | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | Training large-scale neural networks in vision, and multimodal domains demands substantial memory resources, primarily due to the storage of optimizer states. While LoRA, a popular parameter-efficient method, reduces memory usage, it often suffers from suboptimal performance due to the constraints of low-rank updates. ... | {
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2412.00072 | The Muon Space GNSS-R Surface Soil Moisture Product | [
"cs.LG",
"cs.CE",
"cs.CV",
"physics.space-ph"
] | Muon Space (Muon) is building a constellation of small satellites, many of which will carry global navigation satellite system-reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R ne... | {
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2412.00073 | Addressing Vulnerabilities in AI-Image Detection: Challenges and
Proposed Solutions | [
"cs.CV",
"cs.AI"
] | The rise of advanced AI models like Generative Adversarial Networks (GANs) and diffusion models such as Stable Diffusion has made the creation of highly realistic images accessible, posing risks of misuse in misinformation and manipulation. This study evaluates the effectiveness of convolutional neural networks (CNNs),... | {
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2412.00074 | Safe to Serve: Aligning Instruction-Tuned Models for Safety and
Helpfulness | [
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning and text generation. However, these models can inadvertently generate unsafe or biased responses when prompted with problematic inputs, raising significant ethical and practical concerns for real-world deployment. This research ... | {
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2412.00076 | Flaws of ImageNet, Computer Vision's Favourite Dataset | [
"cs.CV"
] | Since its release, ImageNet-1k dataset has become a gold standard for evaluating model performance. It has served as the foundation for numerous other datasets and training tasks in computer vision. As models have improved in accuracy, issues related to label correctness have become increasingly apparent. In this blog ... | {
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2412.00077 | Selfish Evolution: Making Discoveries in Extreme Label Noise with the
Help of Overfitting Dynamics | [
"cs.CV",
"astro-ph.IM",
"cs.AI",
"cs.LG"
] | Motivated by the scarcity of proper labels in an astrophysical application, we have developed a novel technique, called Selfish Evolution, which allows for the detection and correction of corrupted labels in a weakly supervised fashion. Unlike methods based on early stopping, we let the model train on the noisy dataset... | {
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2412.00081 | Task Singular Vectors: Reducing Task Interference in Model Merging | [
"cs.LG",
"stat.ML"
] | Task Arithmetic has emerged as a simple yet effective method to merge models without additional training. However, by treating entire networks as flat parameter vectors, it overlooks key structural information and is susceptible to task interference. In this paper, we study task vectors at the layer level, focusing on ... | {
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2412.00082 | Dual Prototyping with Domain and Class Prototypes for Affective
Brain-Computer Interface in Unseen Target Conditions | [
"cs.LG",
"cs.AI",
"cs.HC",
"eess.SP"
] | EEG signals have emerged as a powerful tool in affective brain-computer interfaces, playing a crucial role in emotion recognition. However, current deep transfer learning-based methods for EEG recognition face challenges due to the reliance of both source and target data in model learning, which significantly affect mo... | {
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2412.00083 | Visual Error Patterns in Multi-Modal AI: A Statistical Approach | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.AP"
] | Multi-modal large language models (MLLMs), such as GPT-4o, excel at integrating text and visual data but face systematic challenges when interpreting ambiguous or incomplete visual stimuli. This study leverages statistical modeling to analyze the factors driving these errors, using a dataset of geometric stimuli charac... | {
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2412.00084 | Unpacking the Individual Components of Diffusion Policy | [
"cs.LG",
"cs.RO"
] | Imitation Learning presents a promising approach for learning generalizable and complex robotic skills. The recently proposed Diffusion Policy generates robot action sequences through a conditional denoising diffusion process, achieving state-of-the-art performance compared to other imitation learning methods. This pap... | {
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2412.00085 | Residual Attention Single-Head Vision Transformer Network for Rolling
Bearing Fault Diagnosis in Noisy Environments | [
"cs.CV",
"eess.IV"
] | Rolling bearings play a crucial role in industrial machinery, directly influencing equipment performance, durability, and safety. However, harsh operating conditions, such as high speeds and temperatures, often lead to bearing malfunctions, resulting in downtime, economic losses, and safety hazards. This paper proposes... | {
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2412.00086 | Dynamic Non-Prehensile Object Transport via Model-Predictive
Reinforcement Learning | [
"cs.RO",
"cs.LG"
] | We investigate the problem of teaching a robot manipulator to perform dynamic non-prehensile object transport, also known as the `robot waiter' task, from a limited set of real-world demonstrations. We propose an approach that combines batch reinforcement learning (RL) with model-predictive control (MPC) by pretraining... | {
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2412.00087 | Physics-Informed Deep Learning Model for Line-integral Diagnostics
Across Fusion Devices | [
"cs.LG",
"cs.CE"
] | Rapid reconstruction of 2D plasma profiles from line-integral measurements is important in nuclear fusion. This paper introduces a physics-informed model architecture called Onion, that can enhance the performance of models and be adapted to various backbone networks. The model under Onion incorporates physical informa... | {
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2412.00088 | Stochastic Taylor Derivative Estimator: Efficient amortization for
arbitrary differential operators | [
"cs.LG"
] | Optimizing neural networks with loss that contain high-dimensional and high-order differential operators is expensive to evaluate with back-propagation due to $\mathcal{O}(d^{k})$ scaling of the derivative tensor size and the $\mathcal{O}(2^{k-1}L)$ scaling in the computation graph, where $d$ is the dimension of the do... | {
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2412.00090 | Energy-Efficient Split Learning for Fine-Tuning Large Language Models in
Edge Networks | [
"cs.LG",
"cs.CL",
"cs.DC"
] | In this letter, we propose an energy-efficient split learning (SL) framework for fine-tuning large language models (LLMs) using geo-distributed personal data at the network edge, where LLMs are split and alternately across massive mobile devices and an edge server. Considering the device heterogeneity and channel dynam... | {
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2412.00091 | Graph Canvas for Controllable 3D Scene Generation | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Spatial intelligence is foundational to AI systems that interact with the physical world, particularly in 3D scene generation and spatial comprehension. Current methodologies for 3D scene generation often rely heavily on predefined datasets, and struggle to adapt dynamically to changing spatial relationships. In this p... | {
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} |
2412.00094 | A Novel Approach to Image Steganography Using Generative Adversarial
Networks | [
"cs.CR",
"cs.CV",
"cs.LG"
] | The field of steganography has long been focused on developing methods to securely embed information within various digital media while ensuring imperceptibility and robustness. However, the growing sophistication of detection tools and the demand for increased data hiding capacity have revealed limitations in traditio... | {
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} |
2412.00095 | OPCap:Object-aware Prompting Captioning | [
"cs.CV"
] | In the field of image captioning, the phenomenon where missing or nonexistent objects are used to explain an image is referred to as object bias (or hallucination). To mitigate this issue, we propose a target-aware prompting strategy. This method first extracts object labels and their spatial information from the image... | {
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} |
2412.00098 | Fine-Tuning Large Language Models for Scientific Text Classification: A
Comparative Study | [
"cs.CL"
] | The exponential growth of online textual content across diverse domains has necessitated advanced methods for automated text classification. Large Language Models (LLMs) based on transformer architectures have shown significant success in this area, particularly in natural language processing (NLP) tasks. However, gene... | {
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} |
2412.00099 | Mixture of Cache-Conditional Experts for Efficient Mobile Device
Inference | [
"cs.LG",
"cs.AI",
"cs.AR"
] | Mixture of Experts (MoE) LLMs have recently gained attention for their ability to enhance performance by selectively engaging specialized subnetworks or "experts" for each input. However, deploying MoEs on memory-constrained devices remains challenging, particularly when generating tokens sequentially with a batch size... | {
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} |
2412.00100 | Steering Rectified Flow Models in the Vector Field for Controlled Image
Generation | [
"cs.CV",
"cs.LG",
"stat.ML"
] | Diffusion models (DMs) excel in photorealism, image editing, and solving inverse problems, aided by classifier-free guidance and image inversion techniques. However, rectified flow models (RFMs) remain underexplored for these tasks. Existing DM-based methods often require additional training, lack generalization to pre... | {
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} |
2412.00101 | Multi-Label Contrastive Learning : A Comprehensive Study | [
"cs.LG"
] | Multi-label classification, which involves assigning multiple labels to a single input, has emerged as a key area in both research and industry due to its wide-ranging applications. Designing effective loss functions is crucial for optimizing deep neural networks for this task, as they significantly influence model per... | {
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} |
2412.00102 | ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual
Question Answering? | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Multi-modal Large Language Models (MLLMs) are gaining significant attention for their ability to process multi-modal data, providing enhanced contextual understanding of complex problems. MLLMs have demonstrated exceptional capabilities in tasks such as Visual Question Answering (VQA); however, they often struggle with... | {
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} |
2412.00103 | MLLM-Search: A Zero-Shot Approach to Finding People using Multimodal
Large Language Models | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Robotic search of people in human-centered environments, including healthcare settings, is challenging as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person's... | {
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} |
2412.00104 | Differential learning kinetics govern the transition from memorization
to generalization during in-context learning | [
"cs.LG",
"cond-mat.dis-nn",
"cs.AI",
"cs.NE",
"q-bio.NC"
] | Transformers exhibit in-context learning (ICL): the ability to use novel information presented in the context without additional weight updates. Recent work shows that ICL emerges when models are trained on a sufficiently diverse set of tasks and the transition from memorization to generalization is sharp with increasi... | {
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} |
2412.00105 | Predicting Extubation Failure in Intensive Care: The Development of a
Novel, End-to-End Actionable and Interpretable Prediction System | [
"cs.LG",
"cs.CE"
] | Predicting extubation failure in intensive care is challenging due to complex data and the severe consequences of inaccurate predictions. Machine learning shows promise in improving clinical decision-making but often fails to account for temporal patient trajectories and model interpretability, highlighting the need fo... | {
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} |
2412.00107 | Virtual Sensing to Enable Real-Time Monitoring of Inaccessible Locations
\& Unmeasurable Parameters | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Real-time monitoring of critical parameters is essential for energy systems' safe and efficient operation. However, traditional sensors often fail and degrade in harsh environments where physical sensors cannot be placed (inaccessible locations). In addition, there are important parameters that cannot be directly measu... | {
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} |
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