id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.20916 | Low-Light Image Enhancement via Generative Perceptual Priors | [
"cs.CV"
] | Although significant progress has been made in enhancing visibility, retrieving texture details, and mitigating noise in Low-Light (LL) images, the challenge persists in applying current Low-Light Image Enhancement (LLIE) methods to real-world scenarios, primarily due to the diverse illumination conditions encountered.... | {
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2412.20918 | Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes | [
"stat.ML",
"cs.LG"
] | Deep neural networks (DNNs) are often constructed under the closed-world assumption, which may fail to generalize to the out-of-distribution (OOD) data. This leads to DNNs producing overconfident wrong predictions and can result in disastrous consequences in safety-critical applications. Existing OOD detection methods ... | {
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2412.20920 | Channel Charting-assisted Non-orthogonal Pilot Allocation for Uplink
XL-MIMO Transmission | [
"cs.IT",
"eess.SP",
"math.IT"
] | Extremely large-scale multiple-input multiple-output (XL-MIMO) is critical to future wireless networks. The substantial increase in the number of base station (BS) antennas introduces near-field propagation effects in the wireless channels, complicating channel parameter estimation and increasing pilot overhead. Channe... | {
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2412.20924 | HisynSeg: Weakly-Supervised Histopathological Image Segmentation via
Image-Mixing Synthesis and Consistency Regularization | [
"cs.CV",
"cs.AI"
] | Tissue semantic segmentation is one of the key tasks in computational pathology. To avoid the expensive and laborious acquisition of pixel-level annotations, a wide range of studies attempt to adopt the class activation map (CAM), a weakly-supervised learning scheme, to achieve pixel-level tissue segmentation. However,... | {
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2412.20925 | Active Learning with Variational Quantum Circuits for Quantum Process
Tomography | [
"quant-ph",
"cs.LG"
] | Quantum process tomography (QPT), used for reconstruction of an unknown quantum process from measurement data, is a fundamental tool for the diagnostic and full characterization of quantum systems. It relies on querying a set of quantum states as input to the quantum process. Previous works commonly use a straightforwa... | {
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2412.20927 | Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering | [
"cs.CV"
] | Multimodal large language models (MLLMs), such as GPT-4o, Gemini, LLaVA, and Flamingo, have made significant progress in integrating visual and textual modalities, excelling in tasks like visual question answering (VQA), image captioning, and content retrieval. They can generate coherent and contextually relevant descr... | {
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2412.20934 | Optimal Diffusion Processes | [
"math.PR",
"cs.IT",
"math.IT"
] | Of stochastic differential equations, diffusion processes have been adopted in numerous applications, as more relevant and flexible models. This paper studies diffusion processes in a different setting, where for a given stationary distribution and average variance, it seeks the diffusion process with optimal convergen... | {
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2412.20936 | Influence Maximization in Temporal Networks with Persistent and Reactive
Behaviors | [
"cs.SI",
"physics.comp-ph"
] | Influence maximization in temporal social networks presents unique challenges due to the dynamic interactions that evolve over time. Traditional diffusion models often fall short in capturing the real-world complexities of active-inactive transitions among nodes, obscuring the true behavior of influence spread. In dyna... | {
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2412.20942 | Ontology-grounded Automatic Knowledge Graph Construction by LLM under
Wikidata schema | [
"cs.AI",
"cs.IR"
] | We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base. An ontology is authored by generating Competency Questions (CQ) on knowledge base to discover knowledge scope, extracting relations from CQs, and attempt to replace equivalent relations ... | {
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2412.20943 | Cluster-Based Time-Variant Channel Characterization and Modeling for
5G-Railways | [
"cs.IT",
"math.IT"
] | With the development of high-speed railways, 5G for Railways (5G-R) is gradually replacing Global System for the Mobile Communications for Railway (GSM-R) worldwide to meet increasing demands. The large bandwidth, array antennas, and non-stationarity caused by high mobility has made 5G-R channel characterization more c... | {
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2412.20946 | Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO | [
"cs.LG"
] | This work addresses the challenge of optimal energy management in microgrids through a collaborative and privacy-preserving framework. We propose the FedTRPO methodology, which integrates Federated Learning (FL) and Trust Region Policy Optimization (TRPO) to manage distributed energy resources (DERs) efficiently. Using... | {
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2412.20953 | GASLITEing the Retrieval: Exploring Vulnerabilities in Dense
Embedding-based Search | [
"cs.CR",
"cs.CL"
] | Dense embedding-based text retrieval$\unicode{x2013}$retrieval of relevant passages from corpora via deep learning encodings$\unicode{x2013}$has emerged as a powerful method attaining state-of-the-art search results and popularizing the use of Retrieval Augmented Generation (RAG). Still, like other search methods, embe... | {
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2412.20960 | Rise of Generative Artificial Intelligence in Science | [
"cs.CY",
"cs.AI",
"cs.IR"
] | Generative Artificial Intelligence (GenAI, generative AI) has rapidly become available as a tool in scientific research. To explore the use of generative AI in science, we conduct an empirical analysis using OpenAlex. Analyzing GenAI publications and other AI publications from 2017 to 2023, we profile growth patterns, ... | {
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2412.20962 | Conservation-informed Graph Learning for Spatiotemporal Dynamics
Prediction | [
"cs.LG",
"cs.AI"
] | Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep learning models often lack interpretability, fail to obey intrinsic physics, and struggle to cope with the various domains. While geometry-based... | {
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2412.20964 | Hierarchical Banzhaf Interaction for General Video-Language
Representation Learning | [
"cs.CV"
] | Multimodal representation learning, with contrastive learning, plays an important role in the artificial intelligence domain. As an important subfield, video-language representation learning focuses on learning representations using global semantic interactions between pre-defined video-text pairs. However, to enhance ... | {
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2412.20965 | Practical Implementation and Experimental Validation of an Optimal
Control based Eco-Driving System | [
"eess.SY",
"cs.SY"
] | The main goal of Eco-Driving (ED) is to maximize energy efficiency. This study evaluates the energy gains of an ED system for an electric vehicle, obtained from a predictive optimal controller, in a real-world traffic scenario. To this end, a Visual driver Advisory System (VAS) in the form of a personal tablet is used ... | {
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2412.20974 | FPGA-based Acceleration of Neural Network for Image Classification using
Vitis AI | [
"cs.CV",
"eess.IV"
] | In recent years, Convolutional Neural Networks (CNNs) have been widely adopted in computer vision. Complex CNN architecture running on CPU or GPU has either insufficient throughput or prohibitive power consumption. Hence, there is a need to have dedicated hardware to accelerate the computation workload to solve these l... | {
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2412.20976 | Hierarchical Pose Estimation and Mapping with Multi-Scale Neural Feature
Fields | [
"cs.RO"
] | Robotic applications require a comprehensive understanding of the scene. In recent years, neural fields-based approaches that parameterize the entire environment have become popular. These approaches are promising due to their continuous nature and their ability to learn scene priors. However, the use of neural fields ... | {
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2412.20977 | UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI | [
"cs.AI",
"cs.CV",
"cs.RO"
] | We introduce UnrealZoo, a rich collection of photo-realistic 3D virtual worlds built on Unreal Engine, designed to reflect the complexity and variability of the open worlds. Additionally, we offer a variety of playable entities for embodied AI agents. Based on UnrealCV, we provide a suite of easy-to-use Python APIs and... | {
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2412.20980 | Efficient Parallel Genetic Algorithm for Perturbed Substructure
Optimization in Complex Network | [
"cs.NE",
"cs.SI"
] | Evolutionary computing, particularly genetic algorithm (GA), is a combinatorial optimization method inspired by natural selection and the transmission of genetic information, which is widely used to identify optimal solutions to complex problems through simulated programming and iteration. Due to its strong adaptabilit... | {
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2412.20984 | AlignAb: Pareto-Optimal Energy Alignment for Designing Nature-Like
Antibodies | [
"cs.LG"
] | We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design. We first pre-train a language model using millions of antibody sequence data. Then, we employ the learned representations to guide the training of a diffusion model for joint optimization over bot... | {
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2412.20987 | RobustBlack: Challenging Black-Box Adversarial Attacks on
State-of-the-Art Defenses | [
"cs.LG"
] | Although adversarial robustness has been extensively studied in white-box settings, recent advances in black-box attacks (including transfer- and query-based approaches) are primarily benchmarked against weak defenses, leaving a significant gap in the evaluation of their effectiveness against more recent and moderate r... | {
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2412.20992 | Verified Lifting of Deep learning Operators | [
"cs.LG",
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"stat.ML"
] | Deep learning operators are fundamental components of modern deep learning frameworks. With the growing demand for customized operators, it has become increasingly common for developers to create their own. However, designing and implementing operators is complex and error-prone, due to hardware-specific optimizations ... | {
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2412.20993 | Efficiently Serving LLM Reasoning Programs with Certaindex | [
"cs.LG",
"cs.CL"
] | The rapid evolution of large language models (LLMs) has unlocked their capabilities in advanced reasoning tasks like mathematical problem-solving, code generation, and legal analysis. Central to this progress are inference-time reasoning algorithms, which refine outputs by exploring multiple solution paths, at the cost... | {
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2412.20995 | KARPA: A Training-free Method of Adapting Knowledge Graph as References
for Large Language Model's Reasoning Path Aggregation | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) demonstrate exceptional performance across a variety of tasks, yet they are often affected by hallucinations and the timeliness of knowledge. Leveraging knowledge graphs (KGs) as external knowledge sources has emerged as a viable solution, but existing methods for LLM-based knowledge graph ... | {
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2412.20996 | Plug-and-Play Training Framework for Preference Optimization | [
"cs.CL"
] | Recently, preference optimization methods such as DPO have significantly enhanced large language models (LLMs) in wide tasks including dialogue and question-answering. However, current methods fail to account for the varying difficulty levels of training samples during preference optimization, leading to mediocre perfo... | {
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2412.20998 | T-DOM: A Taxonomy for Robotic Manipulation of Deformable Objects | [
"cs.RO"
] | Robotic grasp and manipulation taxonomies, inspired by observing human manipulation strategies, can provide key guidance for tasks ranging from robotic gripper design to the development of manipulation algorithms. The existing grasp and manipulation taxonomies, however, often assume object rigidity, which limits their ... | {
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2412.21001 | LEASE: Offline Preference-based Reinforcement Learning with High Sample
Efficiency | [
"cs.LG",
"cs.AI"
] | Offline preference-based reinforcement learning (PbRL) provides an effective way to overcome the challenges of designing reward and the high costs of online interaction. However, since labeling preference needs real-time human feedback, acquiring sufficient preference labels is challenging. To solve this, this paper pr... | {
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2412.21004 | Weber-Fechner Law in Temporal Difference learning derived from Control
as Inference | [
"cs.LG",
"cs.RO"
] | This paper investigates a novel nonlinear update rule based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in the standard RL states that the TD error is linearly proportional to the degree of updates, treating all rewards equally without no bias. On the other hand, the recent biolog... | {
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2412.21006 | Verbosity-Aware Rationale Reduction: Effective Reduction of Redundant
Rationale via Principled Criteria | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) rely on generating extensive intermediate reasoning units (e.g., tokens, sentences) to enhance final answer quality across a wide range of complex tasks. While generating multiple reasoning paths or iteratively refining rationales proves effective for improving performance, these approaches... | {
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2412.21009 | Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline | [
"cs.CV",
"cs.IR",
"cs.MM"
] | Recent advancements in deep learning have significantly enhanced content-based retrieval methods, notably through models like CLIP that map images and texts into a shared embedding space. However, these methods often struggle with domain-specific entities and long-tail concepts absent from their training data, particul... | {
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2412.21015 | MapQaTor: A System for Efficient Annotation of Map Query Datasets | [
"cs.CL",
"cs.HC"
] | Mapping and navigation services like Google Maps, Apple Maps, Openstreet Maps, are essential for accessing various location-based data, yet they often struggle to handle natural language geospatial queries. Recent advancements in Large Language Models (LLMs) show promise in question answering (QA), but creating reliabl... | {
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2412.21022 | Text Classification: Neural Networks VS Machine Learning Models VS
Pre-trained Models | [
"cs.LG"
] | Text classification is a very common task nowadays and there are many efficient methods and algorithms that we can employ to accomplish it. Transformers have revolutionized the field of deep learning, particularly in Natural Language Processing (NLP) and have rapidly expanded to other domains such as computer vision, t... | {
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2412.21023 | EdgeRAG: Online-Indexed RAG for Edge Devices | [
"cs.LG"
] | Deploying Retrieval Augmented Generation (RAG) on resource-constrained edge devices is challenging due to limited memory and processing power. In this work, we propose EdgeRAG which addresses the memory constraint by pruning embeddings within clusters and generating embeddings on-demand during retrieval. To avoid the l... | {
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2412.21030 | Improving Location-based Thermal Emission Side-Channel Analysis Using
Iterative Transfer Learning | [
"cs.LG",
"cs.CR"
] | This paper proposes the use of iterative transfer learning applied to deep learning models for side-channel attacks. Currently, most of the side-channel attack methods train a model for each individual byte, without considering the correlation between bytes. However, since the models' parameters for attacking different... | {
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2412.21033 | Plancraft: an evaluation dataset for planning with LLM agents | [
"cs.CL",
"cs.AI"
] | We present Plancraft, a multi-modal evaluation dataset for LLM agents. Plancraft has both a text-only and multi-modal interface, based on the Minecraft crafting GUI. We include the Minecraft Wiki to evaluate tool use and Retrieval Augmented Generation (RAG), as well as an oracle planner and oracle RAG information extra... | {
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2412.21035 | Machine Learning Optimal Ordering in Global Routing Problems in
Semiconductors | [
"cs.LG",
"cs.DM"
] | In this work, we propose a new method for ordering nets during the process of layer assignment in global routing problems. The global routing problems that we focus on in this work are based on routing problems that occur in the design of substrates in multilayered semiconductor packages. The proposed new method is bas... | {
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2412.21036 | GePBench: Evaluating Fundamental Geometric Perception for Multimodal
Large Language Models | [
"cs.CL"
] | Multimodal large language models (MLLMs) have made significant progress in integrating visual and linguistic understanding. Existing benchmarks typically focus on high-level semantic capabilities, such as scene understanding and visual reasoning, but often overlook a crucial, foundational ability: geometric perception.... | {
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2412.21037 | TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow
Matching and Clap-Ranked Preference Optimization | [
"cs.SD",
"cs.AI",
"cs.CL",
"eess.AS"
] | We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models lies in the difficulty of creating preference pairs, as TTA lacks structured mechanisms ... | {
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2412.21042 | Visual Style Prompt Learning Using Diffusion Models for Blind Face
Restoration | [
"cs.CV",
"cs.MM"
] | Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements ... | {
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2412.21044 | E2EDiff: Direct Mapping from Noise to Data for Enhanced Diffusion Models | [
"cs.CV"
] | Diffusion models have emerged as a powerful framework for generative modeling, achieving state-of-the-art performance across various tasks. However, they face several inherent limitations, including a training-sampling gap, information leakage in the progressive noising process, and the inability to incorporate advance... | {
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2412.21046 | Mind the truncation gap: challenges of learning on dynamic graphs with
recurrent architectures | [
"cs.LG"
] | Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage the scale and complexity of these graph datasets, machine learning (ML) approaches have become essential. However, CTDGs pose challenges fo... | {
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2412.21049 | Learning Epidemiological Dynamics via the Finite Expression Method | [
"cs.LG",
"cs.NA",
"math.NA"
] | Modeling and forecasting the spread of infectious diseases is essential for effective public health decision-making. Traditional epidemiological models rely on expert-defined frameworks to describe complex dynamics, while neural networks, despite their predictive power, often lack interpretability due to their ``black-... | {
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2412.21051 | Toward Intelligent and Secure Cloud: Large Language Model Empowered
Proactive Defense | [
"cs.CR",
"cs.AI",
"cs.NI"
] | The rapid evolution of cloud computing technologies and the increasing number of cloud applications have provided a large number of benefits in daily lives. However, the diversity and complexity of different components pose a significant challenge to cloud security, especially when dealing with sophisticated and advanc... | {
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2412.21052 | Towards Effective Discrimination Testing for Generative AI | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Generative AI (GenAI) models present new challenges in regulating against discriminatory behavior. In this paper, we argue that GenAI fairness research still has not met these challenges; instead, a significant gap remains between existing bias assessment methods and regulatory goals. This leads to ineffective regulati... | {
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2412.21059 | VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning
for Image and Video Generation | [
"cs.CV"
] | We present a general strategy to aligning visual generation models -- both image and video generation -- with human preference. To start with, we build VisionReward -- a fine-grained and multi-dimensional reward model. We decompose human preferences in images and videos into multiple dimensions, each represented by a s... | {
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2412.21061 | BridgePure: Revealing the Fragility of Black-box Data Protection | [
"cs.LG"
] | Availability attacks, or unlearnable examples, are defensive techniques that allow data owners to modify their datasets in ways that prevent unauthorized machine learning models from learning effectively while maintaining the data's intended functionality. It has led to the release of popular black-box tools for users ... | {
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2412.21063 | Varformer: Adapting VAR's Generative Prior for Image Restoration | [
"cs.CV"
] | Generative models trained on extensive high-quality datasets effectively capture the structural and statistical properties of clean images, rendering them powerful priors for transforming degraded features into clean ones in image restoration. VAR, a novel image generative paradigm, surpasses diffusion models in genera... | {
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2412.21065 | Efficient Multi-Task Inferencing with a Shared Backbone and Lightweight
Task-Specific Adapters for Automatic Scoring | [
"cs.CL"
] | The integration of Artificial Intelligence (AI) in education requires scalable and efficient frameworks that balance performance, adaptability, and cost. This paper addresses these needs by proposing a shared backbone model architecture enhanced with lightweight LoRA adapters for task-specific fine-tuning, targeting th... | {
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2412.21069 | Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed
Resource Bidding | [
"eess.SY",
"cs.LG",
"cs.NI",
"cs.SY"
] | Mobile edge computing (MEC) has empowered mobile devices (MDs) in supporting artificial intelligence (AI) applications through collaborative efforts with proximal MEC servers. Unfortunately, despite the great promise of device-edge cooperative AI inference, data privacy becomes an increasing concern. In this paper, we ... | {
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2412.21071 | Investigating layer-selective transfer learning of QAOA parameters for
Max-Cut problem | [
"quant-ph",
"cond-mat.dis-nn",
"cs.LG"
] | Quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successful for solving combinatorial optimization problems (COPs). It has been observed that the optimal variational parameters obtained from one instanc... | {
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2412.21072 | Enhanced coarsening of charge density waves induced by electron
correlation: Machine-learning enabled large-scale dynamical simulations | [
"cond-mat.str-el",
"cond-mat.stat-mech",
"cs.LG"
] | The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate interplay between quasiparticles and emergent order-parameter fields could lead to unusual coarsening dynamics that is beyond the standard the... | {
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2412.21079 | Edicho: Consistent Image Editing in the Wild | [
"cs.CV"
] | As a verified need, consistent editing across in-the-wild images remains a technical challenge arising from various unmanageable factors, like object poses, lighting conditions, and photography environments. Edicho steps in with a training-free solution based on diffusion models, featuring a fundamental design principl... | {
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2412.21080 | Vinci: A Real-time Embodied Smart Assistant based on Egocentric
Vision-Language Model | [
"cs.CV"
] | We introduce Vinci, a real-time embodied smart assistant built upon an egocentric vision-language model. Designed for deployment on portable devices such as smartphones and wearable cameras, Vinci operates in an "always on" mode, continuously observing the environment to deliver seamless interaction and assistance. Use... | {
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2412.21082 | Quantum Diffusion Model for Quark and Gluon Jet Generation | [
"quant-ph",
"cs.LG",
"hep-ph"
] | Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing techniques in order to mitigate computational challenges and enhance generative performa... | {
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2412.21084 | On the Generalizability of Machine Learning-based Ransomware Detection
in Block Storage | [
"cs.CR",
"cs.LG"
] | Ransomware represents a pervasive threat, traditionally countered at the operating system, file-system, or network levels. However, these approaches often introduce significant overhead and remain susceptible to circumvention by attackers. Recent research activity started looking into the detection of ransomware by obs... | {
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2412.21088 | Advances in Multi-agent Reinforcement Learning: Persistent Autonomy and
Robot Learning Lab Report 2024 | [
"cs.MA"
] | Multi-Agent Reinforcement Learning (MARL) approaches have emerged as popular solutions to address the general challenges of cooperation in multi-agent environments, where the success of achieving shared or individual goals critically depends on the coordination and collaboration between agents. However, existing cooper... | {
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2412.21095 | Lyapunov-Based Deep Neural Networks for Adaptive Control of Stochastic
Nonlinear Systems | [
"eess.SY",
"cs.SY"
] | Controlling nonlinear stochastic dynamical systems involves substantial challenges when the dynamics contain unknown and unstructured nonlinear state-dependent terms. For such complex systems, deep neural networks can serve as powerful black box approximators for the unknown drift and diffusion processes. Recent develo... | {
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2412.21102 | Exploring and Controlling Diversity in LLM-Agent Conversation | [
"cs.CL",
"cs.AI"
] | Diversity is a critical aspect of multi-agent communication. In this paper, we focus on controlling and exploring diversity in the context of open-domain multi-agent conversations, particularly for world simulation applications. We propose Adaptive Prompt Pruning (APP), a novel method that dynamically adjusts the conte... | {
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2412.21104 | On Parallel External-Memory Bidirectional Search | [
"cs.AI"
] | Parallelization and External Memory (PEM) techniques have significantly enhanced the capabilities of search algorithms when solving large-scale problems. Previous research on PEM has primarily centered on unidirectional algorithms, with only one publication on bidirectional PEM that focuses on the meet-in-the-middle (M... | {
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2412.21117 | Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D
Scene Generation | [
"cs.CV"
] | In this work, we introduce Prometheus, a 3D-aware latent diffusion model for text-to-3D generation at both object and scene levels in seconds. We formulate 3D scene generation as multi-view, feed-forward, pixel-aligned 3D Gaussian generation within the latent diffusion paradigm. To ensure generalizability, we build our... | {
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2412.21118 | Efficient Approximate Degenerate Ordered Statistics Decoding for Quantum
Codes via Reliable Subset Reduction | [
"quant-ph",
"cs.IT",
"math.IT"
] | Efficient decoding of quantum codes is crucial for achieving high-performance quantum error correction. In this paper, we introduce the concept of approximate degenerate decoding and integrate it with ordered statistics decoding (OSD). Previously, we proposed a reliability metric that leverages both hard and soft decis... | {
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2412.21124 | Adaptive Batch Size Schedules for Distributed Training of Language
Models with Data and Model Parallelism | [
"cs.LG",
"math.OC",
"stat.ML"
] | An appropriate choice of batch sizes in large-scale model training is crucial, yet it involves an intrinsic yet inevitable dilemma: large-batch training improves training efficiency in terms of memory utilization, while generalization performance often deteriorates due to small amounts of gradient noise. Despite this d... | {
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2412.21127 | What Makes for a Good Stereoscopic Image? | [
"cs.CV"
] | With rapid advancements in virtual reality (VR) headsets, effectively measuring stereoscopic quality of experience (SQoE) has become essential for delivering immersive and comfortable 3D experiences. However, most existing stereo metrics focus on isolated aspects of the viewing experience such as visual discomfort or i... | {
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2412.21132 | DeepF-fNet: a physics-informed neural network for vibration isolation
optimization | [
"physics.comp-ph",
"cs.LG",
"eess.SP"
] | Structural optimization is essential for designing safe, efficient, and durable components with minimal material usage. Traditional methods for vibration control often rely on active systems to mitigate unpredictable vibrations, which may lead to resonance and potential structural failure. However, these methods face s... | {
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2412.21139 | Training Software Engineering Agents and Verifiers with SWE-Gym | [
"cs.SE",
"cs.CL"
] | We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task instances, each comprising a codebase with an executable runtime environment, unit tests, and a task specified in natural language. We use SWE-Gym to train language model ba... | {
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2412.21140 | Facilitating large language model Russian adaptation with Learned
Embedding Propagation | [
"cs.CL",
"cs.AI"
] | Rapid advancements of large language model (LLM) technologies led to the introduction of powerful open-source instruction-tuned LLMs that have the same text generation quality as the state-of-the-art counterparts such as GPT-4. While the emergence of such models accelerates the adoption of LLM technologies in sensitive... | {
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2412.21149 | Functional Risk Minimization | [
"cs.LG"
] | The field of Machine Learning has changed significantly since the 1970s. However, its most basic principle, Empirical Risk Minimization (ERM), remains unchanged. We propose Functional Risk Minimization~(FRM), a general framework where losses compare functions rather than outputs. This results in better performance in s... | {
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2412.21151 | PyG-SSL: A Graph Self-Supervised Learning Toolkit | [
"cs.LG",
"cs.AI"
] | Graph Self-Supervised Learning (SSL) has emerged as a pivotal area of research in recent years. By engaging in pretext tasks to learn the intricate topological structures and properties of graphs using unlabeled data, these graph SSL models achieve enhanced performance, improved generalization, and heightened robustnes... | {
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2412.21154 | Aviary: training language agents on challenging scientific tasks | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and experimentation. Language agents are promising for automating intellectual tasks in science because they can interact with tools via natural lang... | {
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2412.21156 | Unified dimensionality reduction techniques in chronic liver disease
detection | [
"cs.LG"
] | Globally, chronic liver disease continues to be a major health concern that requires precise predictive models for prompt detection and treatment. Using the Indian Liver Patient Dataset (ILPD) from the University of California at Irvine's UCI Machine Learning Repository, a number of machine learning algorithms are inve... | {
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2412.21161 | Open RAN-Enabled Deep Learning-Assisted Mobility Management for
Connected Vehicles | [
"cs.NI",
"cs.AI"
] | Connected Vehicles (CVs) can leverage the unique features of 5G and future 6G/NextG networks to enhance Intelligent Transportation System (ITS) services. However, even with advancements in cellular network generations, CV applications may experience communication interruptions in high-mobility scenarios due to frequent... | {
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2412.21164 | Adversarial Attack and Defense for LoRa Device Identification and
Authentication via Deep Learning | [
"cs.NI",
"cs.AI",
"cs.CR",
"cs.LG",
"eess.SP"
] | LoRa provides long-range, energy-efficient communications in Internet of Things (IoT) applications that rely on Low-Power Wide-Area Network (LPWAN) capabilities. Despite these merits, concerns persist regarding the security of LoRa networks, especially in situations where device identification and authentication are im... | {
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2412.21171 | Quantum Error Correction near the Coding Theoretical Bound | [
"quant-ph",
"cs.IT",
"math.IT"
] | Recent advancements in quantum computing have led to the realization of systems comprising tens of reliable logical qubits, constructed from thousands of noisy physical qubits. However, many of the critical applications that quantum computers aim to solve require quantum computations involving millions or more logical ... | {
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2412.21178 | Two-component spatiotemporal template for activation-inhibition of
speech in ECoG | [
"q-bio.NC",
"cs.CL",
"cs.LG",
"eess.AS",
"eess.SP"
] | I compute the average trial-by-trial power of band-limited speech activity across epochs of multi-channel high-density electrocorticography (ECoG) recorded from multiple subjects during a consonant-vowel speaking task. I show that previously seen anti-correlations of average beta frequency activity (12-35 Hz) to high-f... | {
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2412.21180 | STITCHER: Real-Time Trajectory Planning with Motion Primitive Search | [
"cs.RO"
] | Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy state or actuator constraints. Most modern trajectory planning techniques rely on numerical optimization because high-quality, expressive tr... | {
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2412.21181 | Causal Hangover Effects | [
"econ.EM",
"cs.IT",
"math.IT",
"stat.AP"
] | It's not unreasonable to think that in-game sporting performance can be affected partly by what takes place off the court. We can't observe what happens between games directly. Instead, we proxy for the possibility of athletes partying by looking at play following games in party cities. We are interested to see if team... | {
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2412.21187 | Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs | [
"cs.CL"
] | The remarkable performance of models like the OpenAI o1 can be attributed to their ability to emulate human-like long-time thinking during inference. These models employ extended chain-of-thought (CoT) processes, exploring multiple strategies to enhance problem-solving capabilities. However, a critical question remains... | {
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2412.21188 | Sparse chaos in cortical circuits | [
"q-bio.NC",
"cond-mat.dis-nn",
"cs.LG",
"nlin.CD"
] | Nerve impulses, the currency of information flow in the brain, are generated by an instability of the neuronal membrane potential dynamics. Neuronal circuits exhibit collective chaos that appears essential for learning, memory, sensory processing, and motor control. However, the factors controlling the nature and inten... | {
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2412.21197 | A Large-Scale Study on Video Action Dataset Condensation | [
"cs.CV"
] | Dataset condensation has made significant progress in the image domain. Unlike images, videos possess an additional temporal dimension, which harbors considerable redundant information, making condensation even more crucial. However, video dataset condensation still remains an underexplored area. We aim to bridge this ... | {
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2412.21199 | HumanEval Pro and MBPP Pro: Evaluating Large Language Models on
Self-invoking Code Generation | [
"cs.SE",
"cs.CL"
] | We introduce self-invoking code generation, a new task designed to evaluate the progressive reasoning and problem-solving capabilities of LLMs. In this task, models are presented with a base problem and a related, more complex problem. They must solve the base problem and then utilize its solution to address the more c... | {
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2412.21200 | Distributed Mixture-of-Agents for Edge Inference with Large Language
Models | [
"cs.IT",
"cs.CL",
"cs.DC",
"cs.LG",
"cs.NI",
"math.IT"
] | Mixture-of-Agents (MoA) has recently been proposed as a method to enhance performance of large language models (LLMs), enabling multiple individual LLMs to work together for collaborative inference. This collaborative approach results in improved responses to user prompts compared to relying on a single LLM. In this pa... | {
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2412.21203 | SoS Certificates for Sparse Singular Values and Their Applications:
Robust Statistics, Subspace Distortion, and More | [
"cs.DS",
"cs.LG"
] | We study $\textit{sparse singular value certificates}$ for random rectangular matrices. If $M$ is an $n \times d$ matrix with independent Gaussian entries, we give a new family of polynomial-time algorithms which can certify upper bounds on the maximum of $\|M u\|$, where $u$ is a unit vector with at most $\eta n$ nonz... | {
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2412.21205 | Action-Agnostic Point-Level Supervision for Temporal Action Detection | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We propose action-agnostic point-level (AAPL) supervision for temporal action detection to achieve accurate action instance detection with a lightly annotated dataset. In the proposed scheme, a small portion of video frames is sampled in an unsupervised manner and presented to human annotators, who then label the frame... | {
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2412.21206 | PERSE: Personalized 3D Generative Avatars from A Single Portrait | [
"cs.CV"
] | We present PERSE, a method for building an animatable personalized generative avatar from a reference portrait. Our avatar model enables facial attribute editing in a continuous and disentangled latent space to control each facial attribute, while preserving the individual's identity. To achieve this, our method begins... | {
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2501.00001 | Mathematical modelling of flow and adsorption in a gas chromatograph | [
"cs.CE",
"physics.chem-ph"
] | In this paper, a mathematical model is developed to describe the evolution of the concentration of compounds through a gas chromatography column. The model couples mass balances and kinetic equations for all components. Both single and multiple-component cases are considered with constant or variable velocity. Non-dime... | {
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2501.00003 | Machine learning models for Si nanoparticle growth in nonthermal plasma | [
"physics.comp-ph",
"cs.LG"
] | Nanoparticles (NPs) formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant challenges due to the non-equilibrium nature of NTPs, making them computationally expensive to describe. In this work, we address the challenges ... | {
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2501.00004 | NewsHomepages: Homepage Layouts Capture Information Prioritization
Decisions | [
"cs.IR",
"cs.AI",
"cs.CL"
] | Information prioritization plays an important role in how humans perceive and understand the world. Homepage layouts serve as a tangible proxy for this prioritization. In this work, we present NewsHomepages, a large dataset of over 3,000 new website homepages (including local, national and topic-specific outlets) captu... | {
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2501.00009 | Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G
gNB | [
"eess.SP",
"cs.AI"
] | High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairm... | {
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} |
2501.00013 | Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody
Design and Specificity Optimization | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Antibodies are Y-shaped proteins that protect the host by binding to specific antigens, and their binding is mainly determined by the Complementary Determining Regions (CDRs) in the antibody. Despite the great progress made in CDR design, existing computational methods still encounter several challenges: 1) poor capabi... | {
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"cs.SI": 0,
"cs.SY": 0
} |
2501.00015 | Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions | [
"physics.comp-ph",
"cs.LG",
"stat.CO",
"stat.ML"
] | (Pseudo)random sampling, a costly yet widely used method in (probabilistic) machine learning and Markov Chain Monte Carlo algorithms, remains unfeasible on a truly large scale due to unmet computational requirements. We introduce an energy-efficient algorithm for uniform Float16 sampling, utilizing a room-temperature s... | {
"Other": 0,
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"cs.SY": 0
} |
2501.00016 | Predicting Crack Nucleation and Propagation in Brittle Materials Using
Deep Operator Networks with Diverse Trunk Architectures | [
"physics.comp-ph",
"cs.AI"
] | Phase-field modeling reformulates fracture problems as energy minimization problems and enables a comprehensive characterization of the fracture process, including crack nucleation, propagation, merging, and branching, without relying on ad-hoc assumptions. However, the numerical solution of phase-field fracture proble... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.00020 | Magnetic Field Data Calibration with Transformer Model Using Physical
Constraints: A Scalable Method for Satellite Missions, Illustrated by
Tianwen-1 | [
"physics.space-ph",
"astro-ph.EP",
"astro-ph.IM",
"cs.LG"
] | This study introduces a novel approach that integrates the magnetic field data correction from the Tianwen-1 Mars mission with a neural network architecture constrained by physical principles derived from Maxwell's equation equations. By employing a Transformer based model capable of efficiently handling sequential dat... | {
"Other": 0,
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"cs.NE": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.00021 | Did we miss P In CAP? Partial Progress Conjecture under Asynchrony | [
"cs.DC",
"cs.DB"
] | Each application developer desires to provide its users with consistent results and an always-available system despite failures. Boldly, the CALM theorem disagrees. It states that it is hard to design a system that is both consistent and available under network partitions; select at most two out of these three properti... | {
"Other": 1,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.00029 | A Breadth-First Catalog of Text Processing, Speech Processing and
Multimodal Research in South Asian Languages | [
"cs.CL",
"cs.IR",
"cs.LG"
] | We review the recent literature (January 2022- October 2024) in South Asian languages on text-based language processing, multimodal models, and speech processing, and provide a spotlight analysis focused on 21 low-resource South Asian languages, namely Saraiki, Assamese, Balochi, Bhojpuri, Bodo, Burmese, Chhattisgarhi,... | {
"Other": 0,
"cs.AI": 0,
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"cs.RO": 0,
"cs.SD": 0,
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"cs.SY": 0
} |
2501.00030 | Underutilization of Syntactic Processing by Chinese Learners of English
in Comprehending English Sentences, Evidenced from Adapted Garden-Path
Ambiguity Experiment | [
"cs.CL"
] | Many studies have revealed that sentence comprehension relies more on semantic processing than on syntactic processing. However, previous studies have predominantly emphasized the preference for semantic processing, focusing on the semantic perspective. In contrast, this current study highlights the under-utilization o... | {
"Other": 0,
"cs.AI": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.00031 | Distilling Large Language Models for Efficient Clinical Information
Extraction | [
"cs.CL"
] | Large language models (LLMs) excel at clinical information extraction but their computational demands limit practical deployment. Knowledge distillation--the process of transferring knowledge from larger to smaller models--offers a potential solution. We evaluate the performance of distilled BERT models, which are appr... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.00032 | Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on
Arm CPUs | [
"cs.LG",
"cs.AI",
"cs.AR",
"cs.CL"
] | Large language models (LLMs) have transformed the way we think about language understanding and generation, enthralling both researchers and developers. However, deploying LLMs for inference has been a significant challenge due to their unprecedented size and resource requirements. While quantizing model weights to sub... | {
"Other": 1,
"cs.AI": 1,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.00034 | Time Series Feature Redundancy Paradox: An Empirical Study Based on
Mortgage Default Prediction | [
"q-fin.ST",
"cs.AI",
"cs.LG"
] | With the widespread application of machine learning in financial risk management, conventional wisdom suggests that longer training periods and more feature variables contribute to improved model performance. This paper, focusing on mortgage default prediction, empirically discovers a phenomenon that contradicts tradit... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.00036 | Crime Hotspot Analysis and Mapping Using Geospatial Technology in Dessie
City, Ethiopia | [
"physics.soc-ph",
"cs.IR"
] | Over the past few decades, crime and delinquency rates have increased drastically in many countries; nevertheless, it is important to note that crime trends can differ significantly by geographic region. This study's primary goal was to use geographic technology to map and analyze Dessie City's crime patterns. To inves... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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