id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.06855 | A Unified Multi-Task Learning Architecture for Hate Detection Leveraging
User-Based Information | [
"cs.CL"
] | Hate speech, offensive language, aggression, racism, sexism, and other abusive language are common phenomena in social media. There is a need for Artificial Intelligence(AI)based intervention which can filter hate content at scale. Most existing hate speech detection solutions have utilized the features by treating eac... | {
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2411.06858 | Scientific machine learning in ecological systems: A study on the
predator-prey dynamics | [
"cs.LG",
"cs.AI",
"stat.ML"
] | In this study, we apply two pillars of Scientific Machine Learning: Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs) to the Lotka Volterra Predator Prey Model, a fundamental ecological model describing the dynamic interactions between predator and prey populations. The Lo... | {
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2411.06860 | Enhancing Phishing Detection through Feature Importance Analysis and
Explainable AI: A Comparative Study of CatBoost, XGBoost, and EBM Models | [
"cs.CR",
"cs.AI"
] | Phishing attacks remain a persistent threat to online security, demanding robust detection methods. This study investigates the use of machine learning to identify phishing URLs, emphasizing the crucial role of feature selection and model interpretability for improved performance. Employing Recursive Feature Eliminatio... | {
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2411.06863 | Computable Model-Independent Bounds for Adversarial Quantum Machine
Learning | [
"cs.LG",
"cs.AI",
"cs.ET",
"quant-ph"
] | By leveraging the principles of quantum mechanics, QML opens doors to novel approaches in machine learning and offers potential speedup. However, machine learning models are well-documented to be vulnerable to malicious manipulations, and this susceptibility extends to the models of QML. This situation necessitates a t... | {
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2411.06864 | Veri-Car: Towards Open-world Vehicle Information Retrieval | [
"cs.CV"
] | Many industrial and service sectors require tools to extract vehicle characteristics from images. This is a complex task not only by the variety of noise, and large number of classes, but also by the constant introduction of new vehicle models to the market. In this paper, we present Veri-Car, an information retrieval ... | {
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2411.06866 | Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense
Question Answering | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.SI"
] | Commonsense question answering is a crucial task that requires machines to employ reasoning according to commonsense. Previous studies predominantly employ an extracting-and-modeling paradigm to harness the information in KG, which first extracts relevant subgraphs based on pre-defined rules and then proceeds to design... | {
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2411.06868 | Effect sizes as a statistical feature-selector-based learning to detect
breast cancer | [
"stat.ML",
"cs.LG",
"eess.IV"
] | Breast cancer detection is still an open research field, despite a tremendous effort devoted to work in this area. Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale. Feature selection is widely used to reduce the dimensionality of data by select... | {
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2411.06869 | CapeLLM: Support-Free Category-Agnostic Pose Estimation with Multimodal
Large Language Models | [
"cs.CV",
"cs.LG"
] | Category-agnostic pose estimation (CAPE) has traditionally relied on support images with annotated keypoints, a process that is often cumbersome and may fail to fully capture the necessary correspondences across diverse object categories. Recent efforts have begun exploring the use of text-based queries, where the need... | {
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2411.06870 | AI-Native Multi-Access Future Networks -- The REASON Architecture | [
"cs.NI",
"cs.AI",
"cs.SY",
"eess.SY"
] | The development of the sixth generation of communication networks (6G) has been gaining momentum over the past years, with a target of being introduced by 2030. Several initiatives worldwide are developing innovative solutions and setting the direction for the key features of these networks. Some common emerging themes... | {
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2411.06872 | Multi-Modal interpretable automatic video captioning | [
"cs.CV",
"cs.AI"
] | Video captioning aims to describe video contents using natural language format that involves understanding and interpreting scenes, actions and events that occurs simultaneously on the view. Current approaches have mainly concentrated on visual cues, often neglecting the rich information available from other important ... | {
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2411.06877 | LLM-Assisted Relevance Assessments: When Should We Ask LLMs for Help? | [
"cs.IR"
] | Test collections are information retrieval tools that allow researchers to quickly and easily evaluate ranking algorithms. While test collections have become an integral part of IR research, the process of data creation involves significant effort in manual annotations, which often makes it very expensive and time-cons... | {
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2411.06878 | GraphRPM: Risk Pattern Mining on Industrial Large Attributed Graphs | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.SI"
] | Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological relationships among users, thereby offering enhanced interpretability in comparison to black-box models commonly utilized for classification... | {
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2411.06879 | Classification of residential and non-residential buildings based on
satellite data using deep learning | [
"cs.CV",
"eess.IV"
] | Accurate classification of buildings into residential and non-residential categories is crucial for urban planning, infrastructure development, population estimation and resource allocation. It is a complex job to carry out automatic classification of residential and nonresidential buildings manually using satellite da... | {
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2411.06881 | WassFFed: Wasserstein Fair Federated Learning | [
"cs.LG",
"stat.ML"
] | Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training data in FL is inherently geographically distributed among diverse user groups. Existing research on fairness predominantly assumes access to t... | {
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2411.06883 | Scalable Distributed Least Squares Algorithm for Linear Algebraic
Equations via Scheduling | [
"eess.SY",
"cs.SY"
] | In this work, we propose a novel discrete-time distributed algorithm for finding least squares solutions of linear algebraic equations with a scheduling protocol to further enhance its scalability. Each agent in the network is assumed to know some rows of the coefficient matrix and the corresponding entries in the obse... | {
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2411.06885 | Multidimensional Polynomial Phase Estimation | [
"eess.SP",
"cs.IT",
"math.IT"
] | An estimation method is presented for polynomial phase signals, i.e., those adopting the form of a complex exponential whose phase is polynomial in its indices. Transcending the scope of existing techniques, the proposed estimator can handle an arbitrary number of dimensions and an arbitrary set of polynomial degrees a... | {
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2411.06887 | Symmetrizable systems | [
"math.OC",
"cs.SY",
"eess.SY"
] | Transforming an asymmetric system into a symmetric system makes it possible to exploit the simplifying properties of symmetry in control problems. We define and characterize the family of symmetrizable systems. These systems can be transformed into symmetric systems by a linear transformation of their inputs and output... | {
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2411.06890 | SPARTAN: A Sparse Transformer Learning Local Causation | [
"cs.LG",
"stat.ML"
] | Causal structures play a central role in world models that flexibly adapt to changes in the environment. While recent works motivate the benefits of discovering local causal graphs for dynamics modelling, in this work we demonstrate that accurately capturing these relationships in complex settings remains challenging f... | {
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2411.06893 | Multi-scale Frequency Enhancement Network for Blind Image Deblurring | [
"cs.CV"
] | Image deblurring is an essential image preprocessing technique, aiming to recover clear and detailed images form blurry ones. However, existing algorithms often fail to effectively integrate multi-scale feature extraction with frequency enhancement, limiting their ability to reconstruct fine textures. Additionally, non... | {
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2411.06896 | BuckTales : A multi-UAV dataset for multi-object tracking and
re-identification of wild antelopes | [
"cs.CV"
] | Understanding animal behaviour is central to predicting, understanding, and mitigating impacts of natural and anthropogenic changes on animal populations and ecosystems. However, the challenges of acquiring and processing long-term, ecologically relevant data in wild settings have constrained the scope of behavioural r... | {
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2411.06899 | LongSafetyBench: Long-Context LLMs Struggle with Safety Issues | [
"cs.CL",
"cs.AI",
"cs.LG"
] | With the development of large language models (LLMs), the sequence length of these models continues to increase, drawing significant attention to long-context language models. However, the evaluation of these models has been primarily limited to their capabilities, with a lack of research focusing on their safety. Exis... | {
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2411.06905 | Co-Scheduling of Energy and Production in Discrete Manufacturing
Considering Decision-Dependent Uncertainties | [
"eess.SY",
"cs.SY"
] | Modern discrete manufacturing requires real-time energy and production co-scheduling to reduce business costs. In discrete manufacturing, production lines and equipment are complex and numerous, which introduces significant uncertainty during the production process. Among these uncertainties, decision-dependent uncerta... | {
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2411.06908 | EVQAScore: A Fine-grained Metric for Video Question Answering Data
Quality Evaluation | [
"cs.CV",
"cs.CL"
] | Video question-answering (QA) is a core task in video understanding. Evaluating the quality of video QA and video caption data quality for training video large language models (VideoLLMs) is an essential challenge. Although various methods have been proposed for assessing video caption quality, there remains a lack of ... | {
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2411.06911 | Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular diseases. Most recent techniques rely on deep learning and usually require an extensive amount of labeled data. To overcome this problem, few-shot ... | {
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2411.06916 | Slowing Down Forgetting in Continual Learning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | A common challenge in continual learning (CL) is catastrophic forgetting, where the performance on old tasks drops after new, additional tasks are learned. In this paper, we propose a novel framework called ReCL to slow down forgetting in CL. Our framework exploits an implicit bias of gradient-based neural networks due... | {
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2411.06917 | Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal
Air Quality Sensor Fusion | [
"cs.LG",
"eess.SP"
] | The deployment of affordable Internet of Things (IoT) sensors for air pollution monitoring has increased in recent years due to their scalability and cost-effectiveness. However, accurately calibrating these sensors in uncontrolled environments remains a significant challenge. While expensive reference sensors can prov... | {
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2411.06919 | Understanding Generalization in Quantum Machine Learning with Margins | [
"quant-ph",
"cs.LG"
] | Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those relying on uniform bounds, across both classical and quantum settings. In this work, we present a mar... | {
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2411.06920 | Safe Planner: Empowering Safety Awareness in Large Pre-Trained Models
for Robot Task Planning | [
"cs.RO"
] | Robot task planning is an important problem for autonomous robots in long-horizon challenging tasks. As large pre-trained models have demonstrated superior planning ability, recent research investigates utilizing large models to achieve autonomous planning for robots in diverse tasks. However, since the large models ar... | {
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2411.06921 | UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language
Models | [
"cs.CV"
] | Pre-trained vision-language models (e.g., CLIP) have shown powerful zero-shot transfer capabilities. But they still struggle with domain shifts and typically require labeled data to adapt to downstream tasks, which could be costly. In this work, we aim to leverage unlabeled data that naturally spans multiple domains to... | {
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2411.06927 | Multi-modal Iterative and Deep Fusion Frameworks for Enhanced Passive
DOA Sensing via a Green Massive H2AD MIMO Receiver | [
"cs.AI"
] | Most existing DOA estimation methods assume ideal source incident angles with minimal noise. Moreover, directly using pre-estimated angles to calculate weighted coefficients can lead to performance loss. Thus, a green multi-modal (MM) fusion DOA framework is proposed to realize a more practical, low-cost and high time-... | {
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2411.06928 | Multi-class Decoding of Attended Speaker Direction Using
Electroencephalogram and Audio Spatial Spectrum | [
"cs.SD",
"cs.AI",
"cs.CL",
"eess.AS"
] | Decoding the directional focus of an attended speaker from listeners' electroencephalogram (EEG) signals is essential for developing brain-computer interfaces to improve the quality of life for individuals with hearing impairment. Previous works have concentrated on binary directional focus decoding, i.e., determining ... | {
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2411.06946 | Cancer-Answer: Empowering Cancer Care with Advanced Large Language
Models | [
"cs.CL"
] | Gastrointestinal (GI) tract cancers account for a substantial portion of the global cancer burden, where early diagnosis is critical for improved management and patient outcomes. The complex aetiologies and overlapping symptoms across GI cancers often delay diagnosis, leading to suboptimal treatment strategies. Cancer-... | {
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2411.06948 | Bipedal walking with continuously compliant robotic legs | [
"cs.RO"
] | In biomechanics and robotics, elasticity plays a crucial role in enhancing locomotion efficiency and stability. Traditional approaches in legged robots often employ series elastic actuators (SEA) with discrete rigid components, which, while effective, add weight and complexity. This paper presents an innovative alterna... | {
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2411.06950 | Sniff AI: Is My 'Spicy' Your 'Spicy'? Exploring LLM's Perceptual
Alignment with Human Smell Experiences | [
"cs.CL",
"cs.HC"
] | Aligning AI with human intent is important, yet perceptual alignment-how AI interprets what we see, hear, or smell-remains underexplored. This work focuses on olfaction, human smell experiences. We conducted a user study with 40 participants to investigate how well AI can interpret human descriptions of scents. Partici... | {
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2411.06958 | Data-driven discovery of mechanical models directly from MRI spectral
data | [
"physics.med-ph",
"cs.LG",
"eess.IV"
] | Finding interpretable biomechanical models can provide insight into the functionality of organs with regard to physiology and disease. However, identifying broadly applicable dynamical models for in vivo tissue remains challenging. In this proof of concept study we propose a reconstruction framework for data-driven dis... | {
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2411.06959 | ENAT: Rethinking Spatial-temporal Interactions in Token-based Image
Synthesis | [
"cs.CV",
"cs.AI"
] | Recently, token-based generation have demonstrated their effectiveness in image synthesis. As a representative example, non-autoregressive Transformers (NATs) can generate decent-quality images in a few steps. NATs perform generation in a progressive manner, where the latent tokens of a resulting image are incrementall... | {
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2411.06963 | The untapped potential of electrically-driven phase transition actuators
to power innovative soft robot designs | [
"cs.RO"
] | In the quest for electrically-driven soft actuators, the focus has shifted away from liquid-gas phase transition, commonly associated with reduced strain rates and actuation delays, in favour of electrostatic and other electrothermal actuation methods. This prevented the technology from capitalizing on its unique chara... | {
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2411.06965 | Imitation from Diverse Behaviors: Wasserstein Quality Diversity
Imitation Learning with Single-Step Archive Exploration | [
"cs.LG",
"cs.AI"
] | Learning diverse and high-performance behaviors from a limited set of demonstrations is a grand challenge. Traditional imitation learning methods usually fail in this task because most of them are designed to learn one specific behavior even with multiple demonstrations. Therefore, novel techniques for quality diversit... | {
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2411.06966 | Robust Fine-tuning of Zero-shot Models via Variance Reduction | [
"cs.CV"
] | When fine-tuning zero-shot models like CLIP, our desideratum is for the fine-tuned model to excel in both in-distribution (ID) and out-of-distribution (OOD). Recently, ensemble-based models (ESM) have been shown to offer significant robustness improvement, while preserving high ID accuracy. However, our study finds tha... | {
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2411.06969 | A Hyperspectral Imaging Dataset and Methodology for Intraoperative
Pixel-Wise Classification of Metastatic Colon Cancer in the Liver | [
"eess.IV",
"cs.CV"
] | Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, there is currently a shortage of pixel-wise annotated HSI data necessary for training deep learning (DL) models. Additionally, the number of HSI-based research studies remains limited, and in many cas... | {
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2411.06971 | MapSAM: Adapting Segment Anything Model for Automated Feature Detection
in Historical Maps | [
"cs.CV"
] | Automated feature detection in historical maps can significantly accelerate the reconstruction of the geospatial past. However, this process is often constrained by the time-consuming task of manually digitizing sufficient high-quality training data. The emergence of visual foundation models, such as the Segment Anythi... | {
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2411.06976 | A Hierarchical Compression Technique for 3D Gaussian Splatting
Compression | [
"cs.CV",
"cs.MM"
] | 3D Gaussian Splatting (GS) demonstrates excellent rendering quality and generation speed in novel view synthesis. However, substantial data size poses challenges for storage and transmission, making 3D GS compression an essential technology. Current 3D GS compression research primarily focuses on developing more compac... | {
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2411.06980 | xNVMe: Unleashing Storage Hardware-Software Co-design | [
"cs.OS",
"cs.DB",
"cs.DC"
] | NVMe SSD hardware has witnessed widespread deployment as commodity and enterprise hardware due to its high performance and rich feature set. Despite the open specifications of various NVMe protocols by the NVMe Express group and NVMe being of software abstractions to program the underlying hardware. The myriad storage ... | {
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2411.06989 | The Backpropagation of the Wave Network | [
"cs.CL",
"cs.AI"
] | This paper provides an in-depth analysis of Wave Network, a novel token representation method derived from the Wave Network, designed to capture both global and local semantics of input text through wave-inspired complex vectors. In complex vector token representation, each token is represented with a magnitude compone... | {
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2411.06990 | Causal-discovery-based root-cause analysis and its application in
time-series prediction error diagnosis | [
"stat.ML",
"cs.LG"
] | Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain "black boxes", making prediction error diagnosis challenging, especially with outliers. This lack of transparency hinders trust and reliability in industrial applications. Heuristic attribution ... | {
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2411.06991 | SIESEF-FusionNet: Spatial Inter-correlation Enhancement and
Spatially-Embedded Feature Fusion Network for LiDAR Point Cloud Semantic
Segmentation | [
"cs.CV"
] | The ambiguity at the boundaries of different semantic classes in point cloud semantic segmentation often leads to incorrect decisions in intelligent perception systems, such as autonomous driving. Hence, accurate delineation of the boundaries is crucial for improving safety in autonomous driving. A novel spatial inter-... | {
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2411.06995 | Which PPML Would a User Choose? A Structured Decision Support Framework
for Developers to Rank PPML Techniques Based on User Acceptance Criteria | [
"cs.AI",
"cs.CR",
"cs.LG",
"cs.SE"
] | Using Privacy-Enhancing Technologies (PETs) for machine learning often influences the characteristics of a machine learning approach, e.g., the needed computational power, timing of the answers or how the data can be utilized. When designing a new service, the developer faces the problem that some decisions require a t... | {
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2411.07003 | Enhancing Robot Assistive Behaviour with Reinforcement Learning and
Theory of Mind | [
"cs.RO",
"cs.AI",
"cs.HC"
] | The adaptation to users' preferences and the ability to infer and interpret humans' beliefs and intents, which is known as the Theory of Mind (ToM), are two crucial aspects for achieving effective human-robot collaboration. Despite its importance, very few studies have investigated the impact of adaptive robots with To... | {
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2411.07006 | Estimating Causal Effects in Partially Directed Parametric Causal Factor
Graphs | [
"cs.AI",
"cs.DS",
"cs.LG"
] | Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference while maintaining exact answers. In this paper, we show how lifting can be applied to causal inference in partially directed graphs, i.e., g... | {
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2411.07007 | Non-Adversarial Inverse Reinforcement Learning via Successor Feature
Matching | [
"cs.LG",
"cs.AI"
] | In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is treated as an adversarial game, where an adversary searches over reward models, and a learner optimizes the reward through repeated RL procedures. This game-solving... | {
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2411.07008 | Permutative redundancy and uncertainty of the objective in deep learning | [
"cs.AI"
] | Implications of uncertain objective functions and permutative symmetry of traditional deep learning architectures are discussed. It is shown that traditional architectures are polluted by an astronomical number of equivalent global and local optima. Uncertainty of the objective makes local optima unattainable, and, as ... | {
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2411.07009 | Hierarchical Conditional Tabular GAN for Multi-Tabular Synthetic Data
Generation | [
"cs.LG",
"cs.DB"
] | The generation of synthetic data is a state-of-the-art approach to leverage when access to real data is limited or privacy regulations limit the usability of sensitive data. A fair amount of research has been conducted on synthetic data generation for single-tabular datasets, but only a limited amount of research has b... | {
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2411.07013 | A neural-network based anomaly detection system and a safety protocol to
protect vehicular network | [
"cs.LG",
"cs.AI",
"cs.NI"
] | This thesis addresses the use of Cooperative Intelligent Transport Systems (CITS) to improve road safety and efficiency by enabling vehicle-to-vehicle communication, highlighting the importance of secure and accurate data exchange. To ensure safety, the thesis proposes a Machine Learning-based Misbehavior Detection Sys... | {
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2411.07015 | Leveraging LSTM for Predictive Modeling of Satellite Clock Bias | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Satellite clock bias prediction plays a crucial role in enhancing the accuracy of satellite navigation systems. In this paper, we propose an approach utilizing Long Short-Term Memory (LSTM) networks to predict satellite clock bias. We gather data from the PRN 8 satellite of the Galileo and preprocess it to obtain a sin... | {
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2411.07018 | Data-Driven Gradient Optimization for Field Emission Management in a
Superconducting Radio-Frequency Linac | [
"physics.acc-ph",
"cs.LG"
] | Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty q... | {
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2411.07019 | UniHR: Hierarchical Representation Learning for Unified Knowledge Graph
Link Prediction | [
"cs.CL",
"cs.AI"
] | Beyond-triple fact representations including hyper-relational facts with auxiliary key-value pairs, temporal facts with additional timestamps, and nested facts implying relationships between facts, are gaining significant attention. However, existing link prediction models are usually designed for one specific type of ... | {
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2411.07021 | Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation | [
"cs.IR"
] | Retrieval-augmented generation (RAG) has shown impressive capability in providing reliable answer predictions and addressing hallucination problems. A typical RAG implementation uses powerful retrieval models to extract external information and large language models (LLMs) to generate answers. In contrast, recent LLM-b... | {
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2411.07022 | HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph
Representation Learning | [
"cs.LG"
] | The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for deriving insights in IoT scenarios such as smart cities, industrial IoT, and intelligent transportation sys... | {
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2411.07025 | Scaling Mesh Generation via Compressive Tokenization | [
"cs.GR",
"cs.CV"
] | We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequ... | {
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2411.07031 | Evaluating the Accuracy of Chatbots in Financial Literature | [
"cs.AI",
"econ.EM"
] | We evaluate the reliability of two chatbots, ChatGPT (4o and o1-preview versions), and Gemini Advanced, in providing references on financial literature and employing novel methodologies. Alongside the conventional binary approach commonly used in the literature, we developed a nonbinary approach and a recency measure t... | {
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2411.07032 | Scaling Long-Horizon Online POMDP Planning via Rapid State Space
Sampling | [
"cs.RO"
] | Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs (e.g., $\geq15$ steps) remain difficult to solve. This paper proposes a new approximate online POM... | {
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2411.07037 | LIFBench: Evaluating the Instruction Following Performance and Stability
of Large Language Models in Long-Context Scenarios | [
"cs.CL"
] | As Large Language Models (LLMs) evolve in natural language processing (NLP), their ability to stably follow instructions in long-context inputs has become critical for real-world applications. However, existing benchmarks seldom focus on instruction-following in long-context scenarios or stability on different inputs. ... | {
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2411.07038 | Designing Reliable Experiments with Generative Agent-Based Modeling: A
Comprehensive Guide Using Concordia by Google DeepMind | [
"cs.AI"
] | In social sciences, researchers often face challenges when conducting large-scale experiments, particularly due to the simulations' complexity and the lack of technical expertise required to develop such frameworks. Agent-Based Modeling (ABM) is a computational approach that simulates agents' actions and interactions t... | {
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2411.07039 | Learning Collective Dynamics of Multi-Agent Systems using Event-based
Vision | [
"cs.MA",
"cs.CV"
] | This paper proposes a novel problem: vision-based perception to learn and predict the collective dynamics of multi-agent systems, specifically focusing on interaction strength and convergence time. Multi-agent systems are defined as collections of more than ten interacting agents that exhibit complex group behaviors. U... | {
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2411.07040 | FlexiGen: Stochastic Dataset Generator for Electric Vehicle Charging
Energy Flexibility | [
"eess.SY",
"cs.SY"
] | Electric vehicles (EVs) and renewable energy sources (RES) are vital components of sustainable energy systems, yet their uncoordinated integration can pose substantial challenges to grid stability, such as unmanaged peak loads and energy balance issues. Vehicle-to-Grid (V2G), offer a promising solution to address these... | {
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2411.07042 | Minion: A Technology Probe for Resolving Value Conflicts through
Expert-Driven and User-Driven Strategies in AI Companion Applications | [
"cs.HC",
"cs.AI",
"cs.CL",
"cs.CY"
] | AI companions based on large language models can role-play and converse very naturally. When value conflicts arise between the AI companion and the user, it may offend or upset the user. Yet, little research has examined such conflicts. We first conducted a formative study that analyzed 151 user complaints about confli... | {
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2411.07043 | Unified Bayesian representation for high-dimensional multi-modal
biomedical data for small-sample classification | [
"stat.ML",
"cs.LG"
] | We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions. To do so, the proposed model combines within a common latent space the different data views to extract the relevant information to solve the... | {
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2411.07047 | Automatic Contact-Based 3D Scanning Using Articulated Robotic Arm | [
"cs.RO",
"physics.ins-det"
] | This paper presents an open-loop articulated 6-degree-of-freedom (DoF) robotic system for three-dimensional (3D) scanning of objects by contact-based method. A digitizer probe was used to detect contact with the object. Inverse kinematics (IK) was used to determine the joint angles of the robot corresponding to the pro... | {
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2411.07049 | Pushing the Limit: Verified Performance-Optimal Causally-Consistent
Database Transactions | [
"cs.DB",
"cs.DC",
"cs.FL",
"cs.LO"
] | Modern web services crucially rely on high-performance distributed databases, where concurrent transactions are isolated from each other using concurrency control protocols. Relaxed isolation levels, which permit more complex concurrent behaviors than strong levels like serializability, are used in practice for higher ... | {
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2411.07050 | FedCVD: The First Real-World Federated Learning Benchmark on
Cardiovascular Disease Data | [
"eess.SP",
"cs.AI",
"cs.LG"
] | Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods can help diagnose CVDs early, but their performance relies on access to substantial data with high quality. However, the sensitive nature of ... | {
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2411.07053 | UAV survey coverage path planning of complex regions containing
exclusion zones | [
"cs.RO",
"cs.CG"
] | This article addresses the challenge of UAV survey coverage path planning for areas that are complex concave polygons, containing exclusion zones or obstacles. While standard drone path planners typically generate coverage paths for simple convex polygons, this study proposes a method to manage more intricate regions, ... | {
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2411.07055 | Reconstruction of neuromorphic dynamics from a single scalar time series
using variational autoencoder and neural network map | [
"nlin.PS",
"cs.LG",
"physics.bio-ph"
] | This paper examines the reconstruction of a family of dynamical systems with neuromorphic behavior using a single scalar time series. A model of a physiological neuron based on the Hodgkin-Huxley formalism is considered. Single time series of one of its variables is shown to be enough to train a neural network that can... | {
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2411.07056 | Distributed Spatial Awareness for Robot Swarms | [
"cs.RO"
] | Building a distributed spatial awareness within a swarm of locally sensing and communicating robots enables new swarm algorithms. We use local observations by robots of each other and Gaussian Belief Propagation message passing combined with continuous swarm movement to build a global and distributed swarm-centric fram... | {
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2411.07057 | Randomized Forward Mode Gradient for Spiking Neural Networks in
Scientific Machine Learning | [
"cs.NE",
"cs.NA",
"math.NA",
"math.OC"
] | Spiking neural networks (SNNs) represent a promising approach in machine learning, combining the hierarchical learning capabilities of deep neural networks with the energy efficiency of spike-based computations. Traditional end-to-end training of SNNs is often based on back-propagation, where weight updates are derived... | {
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2411.07061 | General framework for online-to-nonconvex conversion: Schedule-free SGD
is also effective for nonconvex optimization | [
"cs.LG",
"math.OC",
"stat.ML"
] | This work investigates the effectiveness of schedule-free methods, developed by A. Defazio et al. (NeurIPS 2024), in nonconvex optimization settings, inspired by their remarkable empirical success in training neural networks. Specifically, we show that schedule-free SGD achieves optimal iteration complexity for nonsmoo... | {
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2411.07066 | Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Network pruning focuses on computational techniques that aim to reduce a given model's computational cost by removing a subset of its parameters while having minimal impact on performance. Throughout the last decade, the most widely used pruning paradigm has been pruning and re-training, which nowadays is inconvenient ... | {
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2411.07069 | Two-Stage Stochastic Optimization for Low-Carbon Dispatch in a Combined
Energy System | [
"eess.SY",
"cs.SY"
] | While wind and solar power contribute to sustainability, their intermittent nature poses challenges when integrated into the grid. To mitigate these issues, renewable energy can be combined with coal fired power and hydropower sources to stabilize the energy system, with battery storage serving as a backup source to sm... | {
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2411.07070 | On Active Privacy Auditing in Supervised Fine-tuning for White-Box
Language Models | [
"cs.CL",
"cs.AI"
] | The pretraining and fine-tuning approach has become the leading technique for various NLP applications. However, recent studies reveal that fine-tuning data, due to their sensitive nature, domain-specific characteristics, and identifiability, pose significant privacy concerns. To help develop more privacy-resilient fin... | {
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2411.07071 | Universal Response and Emergence of Induction in LLMs | [
"cs.LG",
"cs.AI",
"cs.CL"
] | While induction is considered a key mechanism for in-context learning in LLMs, understanding its precise circuit decomposition beyond toy models remains elusive. Here, we study the emergence of induction behavior within LLMs by probing their response to weak single-token perturbations of the residual stream. We find th... | {
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2411.07072 | An Interpretable X-ray Style Transfer via Trainable Local Laplacian
Filter | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Radiologists have preferred visual impressions or 'styles' of X-ray images that are manually adjusted to their needs to support their diagnostic performance. In this work, we propose an automatic and interpretable X-ray style transfer by introducing a trainable version of the Local Laplacian Filter (LLF). From the shap... | {
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2411.07074 | Increasing Rosacea Awareness Among Population Using Deep Learning and
Statistical Approaches | [
"cs.CV"
] | Approximately 16 million Americans suffer from rosacea according to the National Rosacea Society. To increase rosacea awareness, automatic rosacea detection methods using deep learning and explainable statistical approaches are presented in this paper. The deep learning method applies the ResNet-18 for rosacea detectio... | {
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2411.07075 | Transformer verbatim in-context retrieval across time and scale | [
"cs.CL"
] | To predict upcoming text, language models must in some cases retrieve in-context information verbatim. In this report, we investigated how the ability of language models to retrieve arbitrary in-context nouns developed during training (across time) and as language models trained on the same dataset increase in size (ac... | {
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2411.07076 | StoryTeller: Improving Long Video Description through Global
Audio-Visual Character Identification | [
"cs.CV",
"cs.AI"
] | Existing large vision-language models (LVLMs) are largely limited to processing short, seconds-long videos and struggle with generating coherent descriptions for extended video spanning minutes or more. Long video description introduces new challenges, such as plot-level consistency across descriptions. To address thes... | {
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2411.07079 | Robust Nonprehensile Object Transportation with Uncertain Inertial
Parameters | [
"cs.RO"
] | We consider the nonprehensile object transportation task known as the waiter's problem - in which a robot must move an object balanced on a tray from one location to another - when the balanced object has uncertain inertial parameters. In contrast to existing approaches that completely ignore uncertainty in the inertia... | {
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2411.07086 | To Train or Not to Train: Balancing Efficiency and Training Cost in Deep
Reinforcement Learning for Mobile Edge Computing | [
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | Artificial Intelligence (AI) is a key component of 6G networks, as it enables communication and computing services to adapt to end users' requirements and demand patterns. The management of Mobile Edge Computing (MEC) is a meaningful example of AI application: computational resources available at the network edge need ... | {
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2411.07087 | OCMDP: Observation-Constrained Markov Decision Process | [
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | In many practical applications, decision-making processes must balance the costs of acquiring information with the benefits it provides. Traditional control systems often assume full observability, an unrealistic assumption when observations are expensive. We tackle the challenge of simultaneously learning observation ... | {
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2411.07088 | Eavesdropping on Goal-Oriented Communication: Timing Attacks and
Countermeasures | [
"eess.SY",
"cs.CR",
"cs.IT",
"cs.MA",
"cs.SY",
"math.IT"
] | Goal-oriented communication is a new paradigm that considers the meaning of transmitted information to optimize communication. One possible application is the remote monitoring of a process under communication costs: scheduling updates based on goal-oriented considerations can significantly reduce transmission frequenc... | {
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2411.07089 | Towards Characterizing Cyber Networks with Large Language Models | [
"cs.AI",
"cs.CR",
"cs.LG"
] | Threat hunting analyzes large, noisy, high-dimensional data to find sparse adversarial behavior. We believe adversarial activities, however they are disguised, are extremely difficult to completely obscure in high dimensional space. In this paper, we employ these latent features of cyber data to find anomalies via a pr... | {
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2411.07094 | Differentially-Private Collaborative Online Personalized Mean Estimation | [
"cs.LG",
"cs.IT",
"math.IT"
] | We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy a... | {
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2411.07096 | Extreme Rotation Estimation in the Wild | [
"cs.CV"
] | We present a technique and benchmark dataset for estimating the relative 3D orientation between a pair of Internet images captured in an extreme setting, where the images have limited or non-overlapping field of views. Prior work targeting extreme rotation estimation assume constrained 3D environments and emulate persp... | {
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} |
2411.07097 | Arctique: An artificial histopathological dataset unifying realism and
controllability for uncertainty quantification | [
"cs.CV"
] | Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limits their systematic comparison and evaluation: Current UQ methods are typically tested either on overly simplistic toy datasets or on complex... | {
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} |
2411.07098 | A Multi-Agent Approach for REST API Testing with Semantic Graphs and
LLM-Driven Inputs | [
"cs.SE",
"cs.AI"
] | As modern web services increasingly rely on REST APIs, their thorough testing has become crucial. Furthermore, the advent of REST API documentation languages, such as the OpenAPI Specification, has led to the emergence of many black-box REST API testing tools. However, these tools often focus on individual test element... | {
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} |
2411.07099 | Bounded Rationality Equilibrium Learning in Mean Field Games | [
"cs.GT",
"cs.AI",
"cs.LG",
"cs.MA"
] | Mean field games (MFGs) tractably model behavior in large agent populations. The literature on learning MFG equilibria typically focuses on finding Nash equilibria (NE), which assume perfectly rational agents and are hence implausible in many realistic situations. To overcome these limitations, we incorporate bounded r... | {
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} |
2411.07102 | Effectively Leveraging Momentum Terms in Stochastic Line Search
Frameworks for Fast Optimization of Finite-Sum Problems | [
"math.OC",
"cs.LG"
] | In this work, we address unconstrained finite-sum optimization problems, with particular focus on instances originating in large scale deep learning scenarios. Our main interest lies in the exploration of the relationship between recent line search approaches for stochastic optimization in the overparametrized regime a... | {
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} |
2411.07104 | Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal
Pushing | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA"
] | Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their usefulness in demanding real-world applications such as search and rescue, construction, industrial automation, and room organization. This pa... | {
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} |
2411.07107 | Training Neural Networks as Recognizers of Formal Languages | [
"cs.CL",
"cs.LG"
] | Characterizing the computational power of neural network architectures in terms of formal language theory remains a crucial line of research, as it describes lower and upper bounds on the reasoning capabilities of modern AI. However, when empirically testing these bounds, existing work often leaves a discrepancy betwee... | {
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} |
2411.07111 | Building a Taiwanese Mandarin Spoken Language Model: A First Attempt | [
"cs.CL",
"cs.SD",
"eess.AS"
] | This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations. Our end-to-end model incorporates a decoder-only transformer architecture and aims to achieve seaml... | {
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} |
2411.07114 | TinyML Security: Exploring Vulnerabilities in Resource-Constrained
Machine Learning Systems | [
"cs.CR",
"cs.LG"
] | Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted by RAM and CPU capabilities two to three orders of magnitude smaller than conventional systems, make... | {
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} |
2411.07118 | ConvMixFormer- A Resource-efficient Convolution Mixer for
Transformer-based Dynamic Hand Gesture Recognition | [
"cs.CV",
"cs.HC",
"cs.LG"
] | Transformer models have demonstrated remarkable success in many domains such as natural language processing (NLP) and computer vision. With the growing interest in transformer-based architectures, they are now utilized for gesture recognition. So, we also explore and devise a novel ConvMixFormer architecture for dynami... | {
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} |
2411.07120 | Efficient Adaptive Optimization via Subset-Norm and Subspace-Momentum:
Fast, Memory-Reduced Training with Convergence Guarantees | [
"cs.LG",
"cs.NE",
"math.OC"
] | We introduce two complementary techniques for efficient adaptive optimization that reduce memory requirements while accelerating training of large-scale neural networks. The first technique, Subset-Norm adaptive step size, generalizes AdaGrad-Norm and AdaGrad(-Coordinate) by reducing the second moment term's memory foo... | {
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} |
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