id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.07775 | Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed
GFlowNets | [
"cs.LG",
"cs.CV"
] | While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetune pretrained diffusion models on some reward functions that are either designed by experts or learned from small-scale datasets. Existing methods for finetuning diffusion models ty... | {
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2412.07776 | Video Motion Transfer with Diffusion Transformers | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We propose DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers (DiT). We first process the reference video with a pre-trained DiT to analyze cross-frame attention maps and extract a patch-wise motion signal called the Attention ... | {
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2412.07778 | MIN: Multi-channel Interaction Network for Drug-Target Interaction with
Protein Distillation | [
"q-bio.QM",
"cs.LG"
] | Traditional drug discovery processes are both time-consuming and require extensive professional expertise. With the accumulation of drug-target interaction (DTI) data from experimental studies, leveraging modern machine-learning techniques to discern patterns between drugs and target proteins has become increasingly fe... | {
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2412.07779 | Evolution of Thought: Diverse and High-Quality Reasoning via
Multi-Objective Optimization | [
"cs.NE",
"cs.AI"
] | As multi-modal large language models (MLLMs) are increasingly applied to complex reasoning tasks, the diversity and quality of reasoning paths become crucial factors affecting their performance. Although current methods aim to enhance reasoning quality through path expansion, they often neglect the diversity of reasoni... | {
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2412.07781 | Can LLMs faithfully generate their layperson-understandable 'self'?: A
Case Study in High-Stakes Domains | [
"cs.HC",
"cs.LG"
] | Large Language Models (LLMs) have significantly impacted nearly every domain of human knowledge. However, the explainability of these models esp. to laypersons, which are crucial for instilling trust, have been examined through various skeptical lenses. In this paper, we introduce a novel notion of LLM explainability t... | {
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2412.07782 | Trustworthy artificial intelligence in the energy sector: Landscape
analysis and evaluation framework | [
"cs.CY",
"cs.AI",
"cs.LG"
] | The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment Li... | {
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2412.07783 | Swin fMRI Transformer Predicts Early Neurodevelopmental Outcomes from
Neonatal fMRI | [
"q-bio.NC",
"cs.CV",
"cs.LG"
] | Brain development in the first few months of human life is a critical phase characterized by rapid structural growth and functional organization. Accurately predicting developmental outcomes during this time is crucial for identifying delays and enabling timely interventions. This study introduces the SwiFT (Swin 4D fM... | {
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2412.07786 | Towards Agentic Schema Refinement | [
"cs.DB",
"cs.AI"
] | Large enterprise databases can be complex and messy, obscuring the data semantics needed for analytical tasks. We propose a semantic layer in-between the database and the user as a set of small and easy-to-interpret database views, effectively acting as a refined version of the schema. To discover these views, we intro... | {
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2412.07787 | Anomaly Detection in California Electricity Price Forecasting: Enhancing
Accuracy and Reliability Using Principal Component Analysis | [
"econ.EM",
"cs.AI",
"cs.ET",
"cs.LG",
"cs.SY",
"eess.SY"
] | Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volatility management. This study focuses on enhancing electricity price forecasting in California's grid, addressing challenges from complex gen... | {
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2412.07789 | Dynamic data summarization for hierarchical spatial clustering | [
"cs.DB",
"cs.DS",
"cs.LG"
] | Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) finds meaningful patterns in spatial data by considering density and spatial proximity. As the clustering algorithm is inherently designed for static applications, so have recent studies focused on accelerating the algorithm for static a... | {
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2412.07791 | Digital Democracy in the Age of Artificial Intelligence | [
"cs.CY",
"cs.AI"
] | This chapter explores the influence of Artificial Intelligence (AI) on digital democracy, focusing on four main areas: citizenship, participation, representation, and the public sphere. It traces the evolution from electronic to virtual and network democracy, underscoring how each stage has broadened democratic engagem... | {
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2412.07793 | Publication Trends in Artificial Intelligence Conferences: The Rise of
Super Prolific Authors | [
"cs.DL",
"cs.AI"
] | Papers published in top conferences contribute influential discoveries that are reshaping the landscape of modern Artificial Intelligence (AI). We analyzed 87,137 papers from 11 AI conferences to examine publication trends over the past decade. Our findings reveal a consistent increase in both the number of papers and ... | {
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2412.07794 | Automatic answering of scientific questions using the FACTS-V1
framework: New methods in research to increase efficiency through the use of
AI | [
"cs.DL",
"cs.AI"
] | The use of artificial intelligence (AI) offers various possibilities to expand and support educational research. Specifically, the implementation of AI can be used to develop new frameworks to establish new research tools that accelerate and meaningfully expand the efficiency of data evaluation and interpretation (Buck... | {
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2412.07796 | MRP-LLM: Multitask Reflective Large Language Models for
Privacy-Preserving Next POI Recommendation | [
"cs.IR",
"cs.AI"
] | Large language models (LLMs) have shown promising potential for next Point-of-Interest (POI) recommendation. However, existing methods only perform direct zero-shot prompting, leading to ineffective extraction of user preferences, insufficient injection of collaborative signals, and a lack of user privacy protection. A... | {
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2412.07797 | Mogo: RQ Hierarchical Causal Transformer for High-Quality 3D Human
Motion Generation | [
"cs.CV"
] | In the field of text-to-motion generation, Bert-type Masked Models (MoMask, MMM) currently produce higher-quality outputs compared to GPT-type autoregressive models (T2M-GPT). However, these Bert-type models often lack the streaming output capability required for applications in video game and multimedia environments, ... | {
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2412.07801 | Learning to Correction: Explainable Feedback Generation for Visual
Commonsense Reasoning Distractor | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Large multimodal models (LMMs) have shown remarkable performance in the visual commonsense reasoning (VCR) task, which aims to answer a multiple-choice question based on visual commonsense within an image. However, the ability of LMMs to correct potential visual commonsense errors in the distractor upon their occurrenc... | {
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2412.07802 | Language Model as Visual Explainer | [
"cs.CV"
] | In this paper, we present Language Model as Visual Explainer LVX, a systematic approach for interpreting the internal workings of vision models using a tree-structured linguistic explanation, without the need for model training. Central to our strategy is the collaboration between vision models and LLM to craft explana... | {
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2412.07804 | XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction
Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Neurogliomas are among the most aggressive forms of cancer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behavior. Magnetic resonance imaging (MRI) is currently the preferred method for diagnosing and monitoring gliomas. However, the lack of specific imaging ... | {
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2412.07806 | Diagnosis and Severity Assessment of Ulcerative Colitis using Self
Supervised Learning | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Ulcerative Colitis (UC) is an incurable inflammatory bowel disease that leads to ulcers along the large intestine and rectum. The increase in the prevalence of UC coupled with gastrointestinal physician shortages stresses the healthcare system and limits the care UC patients receive. A colonoscopy is performed to diagn... | {
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2412.07808 | Boosting Alignment for Post-Unlearning Text-to-Image Generative Models | [
"cs.LG",
"cs.AI"
] | Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns. Driven by these concerns, machine unlearning has become crucial to effectively purge ... | {
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2412.07809 | Fine-grained graph representation learning for heterogeneous mobile
networks with attentive fusion and contrastive learning | [
"cs.LG",
"cs.AI",
"cs.NI"
] | AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and knowledge extracted ... | {
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2412.07811 | Adversarial Autoencoders in Operator Learning | [
"cs.LG"
] | DeepONets and Koopman autoencoders are two prevalent neural operator architectures. These architectures are autoencoders. An adversarial addition to an autoencoder have improved performance of autoencoders in various areas of machine learning. In this paper, the use an adversarial addition for these two neural operator... | {
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2412.07812 | Multi-Response Preference Optimization with Augmented Ranking Dataset | [
"cs.CL",
"cs.LG"
] | Recent advancements in Large Language Models (LLMs) have been remarkable, with new models consistently surpassing their predecessors. These advancements are underpinned by extensive research on various training mechanisms. Among these, Preference Optimization has played a significant role in improving the performance o... | {
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2412.07813 | How Can Incentives and Cut Layer Selection Influence Data Contribution
in Split Federated Learning? | [
"cs.GT",
"cs.AI",
"cs.LG"
] | To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advantages of federated and split learning. However, recent studies have largely overlooked competitive situations. In this framework, the SFL mod... | {
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2412.07815 | Mask prior-guided denoising diffusion improves inverse protein folding | [
"q-bio.BM",
"cs.LG"
] | Inverse protein folding generates valid amino acid sequences that can fold into a desired protein structure, with recent deep-learning advances showing significant potential and competitive performance. However, challenges remain in predicting highly uncertain regions, such as those with loops and disorders. To tackle ... | {
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2412.07817 | Modern Middlewares for Automated Vehicles: A Tutorial | [
"cs.SE",
"cs.DC",
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper offers a tutorial on current middlewares in automated vehicles. Our aim is to provide the reader with an overview of current middlewares and to identify open challenges in this field. We start by explaining the fundamentals of software architecture in distributed systems and the distinguishing requirements o... | {
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2412.07818 | FastDDS-Based Middleware System for Remote X-Ray Image Classification
Using Raspberry Pi | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Internet of Things (IoT) based healthcare systems offer significant potential for improving the delivery of healthcare services in humanitarian engineering, providing essential healthcare services to millions of underserved people in remote areas worldwide. However, these areas have poor network infrastructure, making ... | {
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2412.07819 | Intelligent System for Automated Molecular Patent Infringement
Assessment | [
"cs.LG",
"cs.AI"
] | Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven processes. However, molecules generated by artificial intelligence may unintentionally infringe on existing patents, posing legal and financial... | {
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2412.07820 | Hyperband-based Bayesian Optimization for Black-box Prompt Selection | [
"cs.LG",
"cs.AI"
] | Optimal prompt selection is crucial for maximizing large language model (LLM) performance on downstream tasks. As the most powerful models are proprietary and can only be invoked via an API, users often manually refine prompts in a black-box setting by adjusting instructions and few-shot examples until they achieve goo... | {
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2412.07821 | Derivative-Based Mir Spectroscopy for Blood Glucose Estimation Using
Pca-Driven Regression Models | [
"eess.IV",
"cs.LG",
"physics.med-ph"
] | In this study, we presented two innovative methods, which are Threshold-Based Derivative (TBD) and Adaptive Derivative Peak Detection(ADPD), that enhance the accuracy of Learning models for blood glucose estimation using Mid-Infrared (MIR) spectroscopy. In these presented methods, we have enhanced the model's accuracy ... | {
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2412.07822 | MAGE: A Multi-Agent Engine for Automated RTL Code Generation | [
"cs.AR",
"cs.LG"
] | The automatic generation of RTL code (e.g., Verilog) through natural language instructions has emerged as a promising direction with the advancement of large language models (LLMs). However, producing RTL code that is both syntactically and functionally correct remains a significant challenge. Existing single-LLM-agent... | {
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2412.07823 | Optimizing Locomotor Task Sets in Biological Joint Moment Estimation for
Hip Exoskeleton Applications | [
"cs.RO",
"cs.LG"
] | Accurate estimation of a user's biological joint moment from wearable sensor data is vital for improving exoskeleton control during real-world locomotor tasks. However, most state-of-the-art methods rely on deep learning techniques that necessitate extensive in-lab data collection, posing challenges in acquiring suffic... | {
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2412.07825 | 3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark | [
"cs.CV"
] | 3D spatial reasoning is the ability to analyze and interpret the positions, orientations, and spatial relationships of objects within the 3D space. This allows models to develop a comprehensive understanding of the 3D scene, enabling their applicability to a broader range of areas, such as autonomous navigation, roboti... | {
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2412.07826 | SALON: Self-supervised Adaptive Learning for Off-road Navigation | [
"cs.RO"
] | Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a tremendous amount of... | {
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2412.07836 | Machine learning-driven conservative-to-primitive conversion in hybrid
piecewise polytropic and tabulated equations of state | [
"gr-qc",
"astro-ph.IM",
"cs.AI",
"physics.comp-ph"
] | We present a novel machine learning (ML) method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address ... | {
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2412.07859 | A Bi-Level Optimization Approach to Joint Trajectory Optimization for
Redundant Manipulators | [
"cs.RO",
"math.OC"
] | In this work, we present an approach to minimizing the time necessary for the end-effector of a redundant robot manipulator to traverse a Cartesian path by optimizing the trajectory of its joints. Each joint has limits in the ranges of position, velocity and acceleration, the latter making jerks in joint space undesira... | {
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2412.07867 | Bumblebee: Foundation Model for Particle Physics Discovery | [
"hep-ex",
"cs.LG",
"hep-ph"
] | Bumblebee is a foundation model for particle physics discovery, inspired by BERT. By removing positional encodings and embedding particle 4-vectors, Bumblebee captures both generator- and reconstruction-level information while ensuring sequence-order invariance. Pre-trained on a masked task, it improves dileptonic top ... | {
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2412.07872 | Evaluating the Potential of Federated Learning for Maize Leaf Disease
Prediction | [
"cs.LG",
"cs.AI"
] | The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine l... | {
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2412.07877 | Score-Optimal Diffusion Schedules | [
"stat.ML",
"cs.LG"
] | Denoising diffusion models (DDMs) offer a flexible framework for sampling from high dimensional data distributions. DDMs generate a path of probability distributions interpolating between a reference Gaussian distribution and a data distribution by incrementally injecting noise into the data. To numerically simulate th... | {
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2412.07878 | Comparative Analysis of Deep Learning Approaches for Harmful Brain
Activity Detection Using EEG | [
"cs.LG",
"cs.AI",
"eess.SP",
"q-bio.NC"
] | The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely diagnosis and intervention. Electroencephalography (EEG) provides a non-invasive method for monitoring brain activity, but the manual interpretation of EEG signals are time-... | {
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2412.07880 | Towards Foundation-model-based Multiagent System to Accelerate AI for
Social Impact | [
"cs.AI"
] | AI for social impact (AI4SI) offers significant potential for addressing complex societal challenges in areas such as public health, agriculture, education, conservation, and public safety. However, existing AI4SI research is often labor-intensive and resource-demanding, limiting its accessibility and scalability; the ... | {
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2412.07881 | Predicting NOx emissions in Biochar Production Plants using Machine
Learning | [
"eess.SP",
"cs.LG"
] | The global Biochar Industry has witnessed a surge in biochar production, with a total of 350k mt/year production in 2023. With the pressing climate goals set and the potential of Biochar Carbon Removal (BCR) as a climate-relevant technology, scaling up the number of new plants to over 1000 facilities per year by 2030 b... | {
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2412.07883 | On Faster Marginalization with Squared Circuits via Orthonormalization | [
"cs.LG",
"cs.AI"
] | Squared tensor networks (TNs) and their generalization as parameterized computational graphs -- squared circuits -- have been recently used as expressive distribution estimators in high dimensions. However, the squaring operation introduces additional complexity when marginalizing variables or computing the partition f... | {
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2412.07885 | RUMC: A Rule-based Classifier Inspired by Evolutionary Methods | [
"cs.LG",
"cs.NE"
] | As the field of data analysis grows rapidly due to the large amounts of data being generated, effective data classification has become increasingly important. This paper introduces the RUle Mutation Classifier (RUMC), which represents a significant improvement over the Rule Aggregation ClassifiER (RACER). RUMC uses inn... | {
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2412.07888 | Graph convolutional networks enable fast hemorrhagic stroke monitoring
with electrical impedance tomography | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG",
"math.AP"
] | Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to computationally expensive nonlinear model-based methods. Methods: A post-processing approach with graph convolutional networks is employed. Utilizing the flexibility of th... | {
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2412.07889 | Low-Latency Scalable Streaming for Event-Based Vision | [
"cs.CV",
"cs.MM",
"cs.NI"
] | Recently, we have witnessed the rise of novel ``event-based'' camera sensors for high-speed, low-power video capture. Rather than recording discrete image frames, these sensors output asynchronous ``event'' tuples with microsecond precision, only when the brightness change of a given pixel exceeds a certain threshold. ... | {
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2412.07890 | Protocol Learning, Decentralized Frontier Risk and the No-Off Problem | [
"cs.LG",
"cs.DC"
] | Frontier models are currently developed and distributed primarily through two channels: centralized proprietary APIs or open-sourcing of pre-trained weights. We identify a third paradigm - Protocol Learning - where models are trained across decentralized networks of incentivized participants. This approach has the pote... | {
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2412.07891 | Utility-Scale Bifacial Solar Photovoltaic System: Optimum Sizing and
Techno-Economic Evaluation | [
"eess.SY",
"cs.SY"
] | Classical monofacial solar photovoltaic systems have gained prevalence and are widely reported in the literature because they have a lower initial cost compared with bifacial systems. However, limited investigation of both systems has been done on a utility scale with different performance indicators. This paper introd... | {
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2412.07894 | Demystifying Workload Imbalances in Large Transformer Model Training
over Variable-length Sequences | [
"cs.DC",
"cs.LG"
] | To optimize large Transformer model training, efficient parallel computing and advanced data management are essential. However, current methods often assume a stable and uniform training workload, neglecting imbalances in data sampling and packing that can impede performance. Specifically, data sampling imbalance arise... | {
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2412.07895 | How Should We Represent History in Interpretable Models of Clinical
Policies? | [
"cs.LG",
"cs.AI",
"stat.AP"
] | Modeling policies for sequential clinical decision-making based on observational data is useful for describing treatment practices, standardizing frequent patterns in treatment, and evaluating alternative policies. For each task, it is essential that the policy model is interpretable. Learning accurate models requires ... | {
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2412.07899 | Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building
Footprint Extraction from Remote Sensing Imagery | [
"cs.CV",
"cs.RO"
] | Extraction of building footprint polygons from remotely sensed data is essential for several urban understanding tasks such as reconstruction, navigation, and mapping. Despite significant progress in the area, extracting accurate polygonal building footprints remains an open problem. In this paper, we introduce Pix2Pol... | {
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2412.07902 | Low-Rank Correction for Quantized LLMs | [
"stat.ML",
"cs.LG"
] | We consider the problem of model compression for Large Language Models (LLMs) at post-training time, where the task is to compress a well-trained model using only a small set of calibration input data. In this work, we introduce a new low-rank approach to correct for quantization errors of \emph{activations} in LLMs: w... | {
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2412.07904 | Score Change of Variables | [
"cs.LG",
"cs.AI",
"math.PR"
] | We derive a general change of variables formula for score functions, showing that for a smooth, invertible transformation $\mathbf{y} = \phi(\mathbf{x})$, the transformed score function $\nabla_{\mathbf{y}} \log q(\mathbf{y})$ can be expressed directly in terms of $\nabla_{\mathbf{x}} \log p(\mathbf{x})$. Using this re... | {
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2412.07906 | Rethinking Emotion Annotations in the Era of Large Language Models | [
"cs.CL"
] | Modern affective computing systems rely heavily on datasets with human-annotated emotion labels, for training and evaluation. However, human annotations are expensive to obtain, sensitive to study design, and difficult to quality control, because of the subjective nature of emotions. Meanwhile, Large Language Models (L... | {
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2412.07907 | Modified Baum-Welch Algorithm for Joint Blind Channel Estimation and
Turbo Equalization | [
"eess.SP",
"cs.IT",
"math.IT"
] | Blind estimation of intersymbol interference channels based on the Baum-Welch (BW) algorithm, a specific implementation of the expectation-maximization (EM) algorithm for training hidden Markov models, is robust and does not require labeled data. However, it is known for its extensive computation cost, slow convergence... | {
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2412.07909 | Explaining and Mitigating the Modality Gap in Contrastive Multimodal
Learning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Multimodal learning has recently gained significant popularity, demonstrating impressive performance across various zero-shot classification tasks and a range of perceptive and generative applications. Models such as Contrastive Language-Image Pretraining (CLIP) are designed to bridge different modalities, such as imag... | {
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2412.07911 | Turbo Receiver Design with Joint Detection and Demapping for Coded
Differential BPSK in Bursty Impulsive Noise Channels | [
"cs.IT",
"eess.SP",
"math.IT"
] | It has been recognized that the impulsive noise (IN) generated by power devices poses significant challenges to wireless receivers in practice. In this paper, we assess the achievable information rate (AIR) and the performance of practical turbo receiver designs for a well-established Markov-Middleton IN model. We util... | {
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2412.07915 | Mitigating exponential concentration in covariant quantum kernels for
subspace and real-world data | [
"quant-ph",
"cs.LG"
] | Fidelity quantum kernels have shown promise in classification tasks, particularly when a group structure in the data can be identified and exploited through a covariant feature map. In fact, there exist classification problems on which covariant kernels provide a provable advantage, thus establishing a separation betwe... | {
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2412.07917 | Distributed Intrusion Detection System using Semantic-based Rules for
SCADA in Smart Grid | [
"eess.SY",
"cs.CR",
"cs.SY"
] | Cyber-physical system (CPS) security for the smart grid enables secure communication for the SCADA and wide-area measurement system data. Power utilities world-wide use various SCADA protocols, namely DNP3, Modbus, and IEC 61850, for the data exchanges across substation field devices, remote terminal units (RTUs), and ... | {
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2412.07919 | Identifying Quantum Mechanical Statistics in Italian Corpora | [
"q-bio.NC",
"cs.CL",
"quant-ph"
] | We present a theoretical and empirical investigation of the statistical behaviour of the words in a text produced by human language. To this aim, we analyse the word distribution of various texts of Italian language selected from a specific literary corpus. We firstly generalise a theoretical framework elaborated by ou... | {
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2412.07922 | Robust Multiple Description Neural Video Codec with Masked Transformer
for Dynamic and Noisy Networks | [
"cs.CV",
"cs.AI"
] | Multiple Description Coding (MDC) is a promising error-resilient source coding method that is particularly suitable for dynamic networks with multiple (yet noisy and unreliable) paths. However, conventional MDC video codecs suffer from cumbersome architectures, poor scalability, limited loss resilience, and lower compr... | {
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2412.07923 | Asking Again and Again: Exploring LLM Robustness to Repeated Questions | [
"cs.CL"
] | This study examines whether large language models (LLMs), such as ChatGPT, specifically the latest GPT-4o-mini, exhibit sensitivity to repeated prompts and whether repeating a question can improve response accuracy. We hypothesize that reiterating a question within a single prompt might enhance the model's focus on key... | {
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2412.07935 | Non-Normal Diffusion Models | [
"cs.LG",
"cs.AI"
] | Diffusion models generate samples by incrementally reversing a process that turns data into noise. We show that when the step size goes to zero, the reversed process is invariant to the distribution of these increments. This reveals a previously unconsidered parameter in the design of diffusion models: the distribution... | {
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2412.07937 | Style-agnostic evaluation of ASR using multiple reference transcripts | [
"cs.CL"
] | Word error rate (WER) as a metric has a variety of limitations that have plagued the field of speech recognition. Evaluation datasets suffer from varying style, formality, and inherent ambiguity of the transcription task. In this work, we attempt to mitigate some of these differences by performing style-agnostic evalua... | {
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2412.07940 | HEDS 3.0: The Human Evaluation Data Sheet Version 3.0 | [
"cs.HC",
"cs.CL"
] | This paper presents version 3.0 of the Human Evaluation Datasheet (HEDS). This update is the result of our experience using HEDS in the context of numerous recent human evaluation experiments, including reproduction studies, and of feedback received. Our main overall goal was to improve clarity, and to enable users to ... | {
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2412.07941 | Beyond Static Assumptions: the Predictive Justified Perspective Model
for Epistemic Planning | [
"cs.AI"
] | Epistemic Planning (EP) is an important research area dedicated to reasoning about the knowledge and beliefs of agents in multi-agent cooperative or adversarial settings. The Justified Perspective (JP) model is the state-of-the-art approach to solving EP problems with efficiency and expressiveness. However, all existin... | {
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2412.07942 | Neural Scaling Laws Rooted in the Data Distribution | [
"cs.LG",
"cond-mat.dis-nn"
] | Deep neural networks exhibit empirical neural scaling laws, with error decreasing as a power law with increasing model or data size, across a wide variety of architectures, tasks, and datasets. This universality suggests that scaling laws may result from general properties of natural learning tasks. We develop a mathem... | {
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2412.07944 | PGRID: Power Grid Reconstruction in Informal Developments Using
High-Resolution Aerial Imagery | [
"cs.CV"
] | As of 2023, a record 117 million people have been displaced worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of ... | {
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2412.07947 | GPT-2 Through the Lens of Vector Symbolic Architectures | [
"cs.LG",
"cs.AI"
] | Understanding the general priniciples behind transformer models remains a complex endeavor. Experiments with probing and disentangling features using sparse autoencoders (SAE) suggest that these models might manage linear features embedded as directions in the residual stream. This paper explores the resemblance betwee... | {
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2412.07948 | Frechet Music Distance: A Metric For Generative Symbolic Music
Evaluation | [
"cs.SD",
"cs.AI",
"cs.MM",
"eess.AS"
] | In this paper we introduce the Frechet Music Distance (FMD), a novel evaluation metric for generative symbolic music models, inspired by the Frechet Inception Distance (FID) in computer vision and Frechet Audio Distance (FAD) in generative audio. FMD calculates the distance between distributions of reference and genera... | {
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2412.07951 | From Lived Experience to Insight: Unpacking the Psychological Risks of
Using AI Conversational Agents | [
"cs.HC",
"cs.AI",
"cs.CY"
] | Recent gain in popularity of AI conversational agents has led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experienc... | {
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2412.07956 | Reciprocal Learning of Intent Inferral with Augmented Visual Feedback
for Stroke | [
"cs.RO",
"cs.AI",
"cs.HC",
"cs.LG"
] | Intent inferral, the process by which a robotic device predicts a user's intent from biosignals, offers an effective and intuitive way to control wearable robots. Classical intent inferral methods treat biosignal inputs as unidirectional ground truths for training machine learning models, where the internal state of th... | {
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2412.07958 | PAFFA: Premeditated Actions For Fast Agents | [
"cs.AI"
] | Modern AI assistants have made significant progress in natural language understanding and API/tool integration, with emerging efforts to incorporate diverse interfaces (such as Web interfaces) for enhanced scalability and functionality. However, current approaches that heavily rely on repeated LLM-driven HTML parsing a... | {
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2412.07959 | Deep-Learning Control of Lower-Limb Exoskeletons via simplified
Therapist Input | [
"cs.RO",
"cs.LG"
] | Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of normative walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require ex... | {
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2412.07961 | Forking Paths in Neural Text Generation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Estimating uncertainty in Large Language Models (LLMs) is important for properly evaluating LLMs, and ensuring safety for users. However, prior approaches to uncertainty estimation focus on the final answer in generated text, ignoring intermediate steps that might dramatically impact the outcome. We hypothesize that th... | {
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2412.07962 | Mayfly: Private Aggregate Insights from Ephemeral Streams of On-Device
User Data | [
"cs.CR",
"cs.DB"
] | This paper introduces Mayfly, a federated analytics approach enabling aggregate queries over ephemeral on-device data streams without central persistence of sensitive user data. Mayfly minimizes data via on-device windowing and contribution bounding through SQL-programmability, anonymizes user data via streaming differ... | {
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2412.07965 | HalluCana: Fixing LLM Hallucination with A Canary Lookahead | [
"cs.CL"
] | In this paper, we present HalluCana, a canary lookahead to detect and correct factuality hallucinations of Large Language Models (LLMs) in long-form generation. HalluCana detects and intervenes as soon as traces of hallucination emerge, during and even before generation. To support timely detection, we exploit the inte... | {
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2412.07966 | Balancing Shared and Task-Specific Representations: A Hybrid Approach to
Depth-Aware Video Panoptic Segmentation | [
"cs.CV"
] | In this work, we present Multiformer, a novel approach to depth-aware video panoptic segmentation (DVPS) based on the mask transformer paradigm. Our method learns object representations that are shared across segmentation, monocular depth estimation, and object tracking subtasks. In contrast to recent unified approache... | {
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2412.07971 | Distributed Gradient Descent with Many Local Steps in Overparameterized
Models | [
"cs.LG",
"cs.DC",
"stat.ML"
] | In distributed training of machine learning models, gradient descent with local iterative steps is a very popular method, variants of which are commonly known as Local-SGD or the Federated Averaging (FedAvg). In this method, gradient steps based on local datasets are taken independently in distributed compute nodes to ... | {
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2412.07972 | Phase-aware Training Schedule Simplifies Learning in Flow-Based
Generative Models | [
"cs.LG",
"stat.ML"
] | We analyze the training of a two-layer autoencoder used to parameterize a flow-based generative model for sampling from a high-dimensional Gaussian mixture. Previous work shows that the phase where the relative probability between the modes is learned disappears as the dimension goes to infinity without an appropriate ... | {
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2412.07975 | Machines of Meaning | [
"cs.AI",
"cs.CL",
"cs.CY",
"cs.LG"
] | One goal of Artificial Intelligence is to learn meaningful representations for natural language expressions, but what this entails is not always clear. A variety of new linguistic behaviours present themselves embodied as computers, enhanced humans, and collectives with various kinds of integration and communication. B... | {
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2412.07976 | Torsion Resistant Strain Limiting Layers Enable High Grip Strength of
Electrically-Driven Handed Shearing Auxetic Grippers | [
"cs.RO"
] | Soft grippers have demonstrated a strong ability to successfully pick and manipulate many objects. A key limitation to their wider adoption is their inability to grasp larger payloads due to objects slipping out of grasps. We have overcome this limitation by introducing a torsionally rigid strain limiting layer (TR-SLL... | {
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2412.07977 | Thinking Fast and Laterally: Multi-Agentic Approach for Reasoning about
Uncertain Emerging Events | [
"cs.AI"
] | This paper introduces lateral thinking to implement System-2 reasoning capabilities in AI systems, focusing on anticipatory and causal reasoning under uncertainty. We present a framework for systematic generation and modeling of lateral thinking queries and evaluation datasets. We introduce Streaming Agentic Lateral Th... | {
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2412.07978 | Agents for self-driving laboratories applied to quantum computing | [
"cs.AI",
"quant-ph"
] | Fully automated self-driving laboratories are promising to enable high-throughput and large-scale scientific discovery by reducing repetitive labour. However, effective automation requires deep integration of laboratory knowledge, which is often unstructured, multimodal, and difficult to incorporate into current AI sys... | {
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2412.07979 | AmCLR: Unified Augmented Learning for Cross-Modal Representations | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Contrastive learning has emerged as a pivotal framework for representation learning, underpinning advances in both unimodal and bimodal applications like SimCLR and CLIP. To address fundamental limitations like large batch size dependency and bimodality, methods such as SogCLR leverage stochastic optimization for the g... | {
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2412.07980 | TTVD: Towards a Geometric Framework for Test-Time Adaptation Based on
Voronoi Diagram | [
"cs.CV",
"cs.AI"
] | Deep learning models often struggle with generalization when deploying on real-world data, due to the common distributional shift to the training data. Test-time adaptation (TTA) is an emerging scheme used at inference time to address this issue. In TTA, models are adapted online at the same time when making prediction... | {
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2412.07981 | Where Common Knowledge Cannot Be Formed, Common Belief Can -- Planning
with Multi-Agent Belief Using Group Justified Perspectives | [
"cs.AI"
] | Epistemic planning is the sub-field of AI planning that focuses on changing knowledge and belief. It is important in both multi-agent domains where agents need to have knowledge/belief regarding the environment, but also the beliefs of other agents, including nested beliefs. When modeling knowledge in multi-agent setti... | {
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2412.07982 | Data-Driven Assessment of Vehicle-to-Grid Capabilities in Supporting
Grid During Emergencies: Case Study of Travis County, TX | [
"eess.SY",
"cs.SY"
] | As extreme weather events become more common and threaten power grids, the continuing adoption of electric vehicles (EVs) introduces a growing opportunity for their use as a distributed energy storage resource. This energy storage can be used as backup generation through the use of vehicle-to-grid (V2G) technology, whe... | {
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2412.07984 | Diffusion-Based Attention Warping for Consistent 3D Scene Editing | [
"cs.CV"
] | We present a novel method for 3D scene editing using diffusion models, designed to ensure view consistency and realism across perspectives. Our approach leverages attention features extracted from a single reference image to define the intended edits. These features are warped across multiple views by aligning them wit... | {
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2412.07986 | Provenance Analysis and Semiring Semantics for First-Order Logic | [
"cs.LO",
"cs.DB"
] | A provenance analysis for a query evaluation or a model checking computation extracts information on how its result depends on the atomic facts of the model or database. Traditional work on data provenance was, to a large extent, restricted to positive query languages or the negation-free fragment of first-order logic ... | {
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} |
2412.07990 | Adaptive Querying for Reward Learning from Human Feedback | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Learning from human feedback is a popular approach to train robots to adapt to user preferences and improve safety. Existing approaches typically consider a single querying (interaction) format when seeking human feedback and do not leverage multiple modes of user interaction with a robot. We examine how to learn a pen... | {
"Other": 0,
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"cs.SY": 0
} |
2412.07991 | dsLassoCov: a federated machine learning approach incorporating
covariate control | [
"q-bio.QM",
"cs.LG"
] | Machine learning has been widely adopted in biomedical research, fueled by the increasing availability of data. However, integrating datasets across institutions is challenging due to legal restrictions and data governance complexities. Federated learning allows the direct, privacy preserving training of machine learni... | {
"Other": 0,
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} |
2412.07992 | Concept Bottleneck Large Language Models | [
"cs.CL",
"cs.LG"
] | We introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in i... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2412.07996 | Enhancing Remote Adversarial Patch Attacks on Face Detectors with Tiling
and Scaling | [
"cs.CV",
"cs.CR",
"cs.LG"
] | This paper discusses the attack feasibility of Remote Adversarial Patch (RAP) targeting face detectors. The RAP that targets face detectors is similar to the RAP that targets general object detectors, but the former has multiple issues in the attack process the latter does not. (1) It is possible to detect objects of v... | {
"Other": 0,
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"cs.CR": 1,
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"cs.MA": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.07997 | Accurate Prediction of Temperature Indicators in Eastern China Using a
Multi-Scale CNN-LSTM-Attention model | [
"cs.LG"
] | In recent years, the importance of accurate weather forecasting has become increasingly prominent due to the impacts of global climate change and the rapid development of data science. Traditional forecasting methods often struggle to handle the complexity and nonlinearity inherent in climate data. To address these cha... | {
"Other": 0,
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"cs.SY": 0
} |
2412.07998 | RALI@TREC iKAT 2024: Achieving Personalization via Retrieval Fusion in
Conversational Search | [
"cs.IR"
] | The Recherche Appliquee en Linguistique Informatique (RALI) team participated in the 2024 TREC Interactive Knowledge Assistance (iKAT) Track. In personalized conversational search, effectively capturing a user's complex search intent requires incorporating both contextual information and key elements from the user prof... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
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"cs.CY": 0,
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"cs.IR": 1,
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"cs.NE": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.08005 | Survey on Human-Vehicle Interactions and AI Collaboration for Optimal
Decision-Making in Automated Driving | [
"eess.SY",
"cs.SY"
] | The capabilities of automated vehicles are advancing rapidly, yet achieving full autonomy remains a significant challenge, requiring ongoing human cognition in decision-making processes. Incorporating human cognition into control algorithms has become increasingly important, as researchers work to develop strategies th... | {
"Other": 0,
"cs.AI": 0,
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"cs.CR": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2412.08009 | FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field
Reconstruction from Sparse Sensor Measurements | [
"physics.flu-dyn",
"cs.LG"
] | Reconstructing high-fidelity fluid flow fields from sparse sensor measurements is vital for many science and engineering applications but remains challenging because of dimensional disparities between state and observational spaces. Due to such dimensional differences, the measurement operator becomes ill-conditioned a... | {
"Other": 0,
"cs.AI": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.08010 | Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty
with Human Decision-Making Simulations | [
"cs.LG",
"physics.soc-ph",
"quant-ph"
] | Modern machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy. However, like many computer software systems, they can fail by generating confusing or erroneous outputs or by deferring to human operators to interpret the results and make final decisions. In this paper, we empl... | {
"Other": 0,
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} |
2412.08012 | Of Dice and Games: A Theory of Generalized Boosting | [
"cs.LG",
"stat.ML"
] | Cost-sensitive loss functions are crucial in many real-world prediction problems, where different types of errors are penalized differently; for example, in medical diagnosis, a false negative prediction can lead to worse consequences than a false positive prediction. However, traditional PAC learning theory has mostly... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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