id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.11046 | Knowledge-enhanced Transformer for Multivariate Long Sequence
Time-series Forecasting | [
"cs.LG",
"cs.AI"
] | Multivariate Long Sequence Time-series Forecasting (LSTF) has been a critical task across various real-world applications. Recent advancements focus on the application of transformer architectures attributable to their ability to capture temporal patterns effectively over extended periods. However, these approaches oft... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11048 | Generating medical screening questionnaires through analysis of social
media data | [
"cs.LG",
"cs.CY"
] | Screening questionnaires are used in medicine as a diagnostic aid. Creating them is a long and expensive process, which could potentially be improved through analysis of social media posts related to symptoms and behaviors prior to diagnosis. Here we show a preliminary investigation into the feasibility of generating s... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 1,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11053 | SRA-MCTS: Self-driven Reasoning Augmentation with Monte Carlo Tree
Search for Code Generation | [
"cs.CL",
"cs.AI"
] | Large language models demonstrate exceptional performance in simple code generation tasks but still face challenges in tackling complex problems. These challenges may stem from insufficient reasoning and problem decomposition capabilities. To address this issue, we propose a reasoning-augmented data generation process,... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11055 | FastDraft: How to Train Your Draft | [
"cs.CL"
] | Speculative Decoding has gained popularity as an effective technique for accelerating the auto-regressive inference process of Large Language Models (LLMs). However, Speculative Decoding entirely relies on the availability of efficient draft models, which are often lacking for many existing language models due to a str... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11057 | Reinforcing Competitive Multi-Agents for Playing So Long Sucker | [
"cs.AI"
] | This paper examines the use of classical deep reinforcement learning (DRL) algorithms, DQN, DDQN, and Dueling DQN, in the strategy game So Long Sucker (SLS), a diplomacy-driven game defined by coalition-building and strategic betrayal. SLS poses unique challenges due to its blend of cooperative and adversarial dynamics... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11061 | Beyond Human-Like Processing: Large Language Models Perform Equivalently
on Forward and Backward Scientific Text | [
"cs.CL",
"q-bio.NC"
] | The impressive performance of large language models (LLMs) has led to their consideration as models of human language processing. Instead, we suggest that the success of LLMs arises from the flexibility of the transformer learning architecture. To evaluate this conjecture, we trained LLMs on scientific texts that were ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11066 | TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for
Training-Free Video Large Language Models | [
"cs.CV"
] | Recent advances in multimodal Large Language Models (LLMs) have shown great success in understanding multi-modal contents. For video understanding tasks, training-based video LLMs are difficult to build due to the scarcity of high-quality, curated video-text paired data. In contrast, paired image-text data are much eas... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11069 | Skeleton-Guided Spatial-Temporal Feature Learning for Video-Based
Visible-Infrared Person Re-Identification | [
"cs.CV"
] | Video-based visible-infrared person re-identification (VVI-ReID) is challenging due to significant modality feature discrepancies. Spatial-temporal information in videos is crucial, but the accuracy of spatial-temporal information is often influenced by issues like low quality and occlusions in videos. Existing methods... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11070 | Joint Precoding and AP Selection for Energy Efficient RIS-aided
Cell-Free Massive MIMO Using Multi-agent Reinforcement Learning | [
"cs.IT",
"eess.SP",
"math.IT"
] | Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-generation (6G) networks. In this paper, we investigate the joint precoding and access point (AP) selection for energy efficient RIS-aided CF mM... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11072 | Multilingual Large Language Models: A Systematic Survey | [
"cs.CL"
] | This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across linguistic boundaries, but also represent an important advancement in artificial intelligence. We first discuss the architecture and pre-tra... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11074 | Spectral Subspace Clustering for Attributed Graphs | [
"cs.SI",
"cs.LG"
] | Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domains, especially images and videos. Recently, several studies have demonstrated the great potential of subspace clustering models for partitio... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2411.11079 | Electrostatic Force Regularization for Neural Structured Pruning | [
"cs.CV"
] | The demand for deploying deep convolutional neural networks (DCNNs) on resource-constrained devices for real-time applications remains substantial. However, existing state-of-the-art structured pruning methods often involve intricate implementations, require modifications to the original network architectures, and nece... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11081 | The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case
Study on Media Bias Detection | [
"cs.CL"
] | High annotation costs from hiring or crowdsourcing complicate the creation of large, high-quality datasets needed for training reliable text classifiers. Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality. LLMs have shown p... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11082 | STOP: Spatiotemporal Orthogonal Propagation for Weight-Threshold-Leakage
Synergistic Training of Deep Spiking Neural Networks | [
"cs.NE",
"cs.CV"
] | The prevailing of artificial intelligence-of-things calls for higher energy-efficient edge computing paradigms, such as neuromorphic agents leveraging brain-inspired spiking neural network (SNN) models based on spatiotemporally sparse binary spikes. However, the lack of efficient and high-accuracy deep SNN learning alg... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 1,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11087 | D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical
Classification | [
"cs.CV"
] | The integration of deep learning technologies in medical imaging aims to enhance the efficiency and accuracy of cancer diagnosis, particularly for pancreatic and breast cancers, which present significant diagnostic challenges due to their high mortality rates and complex imaging characteristics. This paper introduces D... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11088 | An Investigation of Offline Reinforcement Learning in Factorisable
Action Spaces | [
"stat.ML",
"cs.LG"
] | Expanding reinforcement learning (RL) to offline domains generates promising prospects, particularly in sectors where data collection poses substantial challenges or risks. Pivotal to the success of transferring RL offline is mitigating overestimation bias in value estimates for state-action pairs absent from data. Whi... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11090 | ForPKG-1.0: A Framework for Constructing Forestry Policy Knowledge Graph
and Application Analysis | [
"cs.IR"
] | A policy knowledge graph can provide decision support for tasks such as project compliance, policy analysis, and intelligent question answering, and can also serve as an external knowledge base to assist the reasoning process of related large language models. Although there have been many related works on knowledge gra... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 1,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11091 | KV-Tandem -- a Modular Approach to Building High-Speed LSM Storage
Engines | [
"cs.DB"
] | We present~\emph{KV-Tandem}, a modular architecture for building LSM-based storage engines on top of simple, non-ordered persistent key-value stores (KVSs). KV-Tandem enables advanced functionalities such as range queries and snapshot reads, while maintaining the native KVS performance for random reads and writes. Its ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 1,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11093 | Dynamic Dimensioning of Frequency Containment Reserves: The Case of the
Nordic Grid | [
"eess.SY",
"cs.SY"
] | One of the main responsibilities of a Transmission System Operator (TSO) operating an electric grid is to maintain a designated frequency (e.g., 50 Hz in Europe). To achieve this, TSOs have created several products called frequency-supporting ancillary services. The Frequency Containment Reserve (FCR) is one of these a... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11098 | MolParser: End-to-end Visual Recognition of Molecule Structures in the
Wild | [
"cs.CV"
] | In recent decades, chemistry publications and patents have increased rapidly. A significant portion of key information is embedded in molecular structure figures, complicating large-scale literature searches and limiting the application of large language models in fields such as biology, chemistry, and pharmaceuticals.... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11099 | Mitigating Relative Over-Generalization in Multi-Agent Reinforcement
Learning | [
"cs.LG",
"cs.AI",
"stat.ML"
] | In decentralized multi-agent reinforcement learning, agents learning in isolation can lead to relative over-generalization (RO), where optimal joint actions are undervalued in favor of suboptimal ones. This hinders effective coordination in cooperative tasks, as agents tend to choose actions that are individually ratio... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11101 | Different Horses for Different Courses: Comparing Bias Mitigation
Algorithms in ML | [
"cs.LG",
"cs.AI",
"cs.CY"
] | With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often compared against each other to find the best method. These benchmarking efforts tend to use a common setup for evaluation under the assumption that providing a uniform environment ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 1,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11105 | Label Sharing Incremental Learning Framework for Independent Multi-Label
Segmentation Tasks | [
"cs.CV",
"cs.AI"
] | In a setting where segmentation models have to be built for multiple datasets, each with its own corresponding label set, a straightforward way is to learn one model for every dataset and its labels. Alternatively, multi-task architectures with shared encoders and multiple segmentation heads or shared weights with comp... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11108 | Iterative Learning Control for Ramp Metering on Service Station On-ramps | [
"eess.SY",
"cs.SY"
] | Congestion on highways has become a significant social problem due to the increasing number of vehicles, leading to considerable waste of time and pollution. Regulating the outflow from the Service Station can help alleviate this congestion. Notably, traffic flows follow recurring patterns over days and weeks, allowing... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11110 | Retinal Vessel Segmentation via Neuron Programming | [
"eess.IV",
"cs.CV"
] | The accurate segmentation of retinal blood vessels plays a crucial role in the early diagnosis and treatment of various ophthalmic diseases. Designing a network model for this task requires meticulous tuning and extensive experimentation to handle the tiny and intertwined morphology of retinal blood vessels. To tackle ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11116 | DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image
Segmentation | [
"eess.IV",
"cs.CV"
] | Accurately segmenting lesions in ultrasound images is challenging due to the difficulty in distinguishing boundaries between lesions and surrounding tissues. While deep learning has improved segmentation accuracy, there is limited focus on boundary quality and its relationship with body structures. To address this, we ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11119 | Leveraging Bitcoin Mining Machines in Demand-Response Mechanisms to
Mitigate Ramping-Induced Transients | [
"eess.SY",
"cs.SY"
] | We propose an extended demand response program, based on ancillary service for supplying flexible electricity demand. In our proposed scheme, we suggest a broader management model to control the scheduling and power consumption of Bitcoin mining machines. The main aspect that we focus on is suppressing the power rampin... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11132 | Variational Bayesian Bow tie Neural Networks with Shrinkage | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.ME",
"stat.TH"
] | Despite the dominant role of deep models in machine learning, limitations persist, including overconfident predictions, susceptibility to adversarial attacks, and underestimation of variability in predictions. The Bayesian paradigm provides a natural framework to overcome such issues and has become the gold standard fo... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11135 | Oscillation Inversion: Understand the structure of Large Flow Model
through the Lens of Inversion Method | [
"cs.CV"
] | We explore the oscillatory behavior observed in inversion methods applied to large-scale text-to-image diffusion models, with a focus on the "Flux" model. By employing a fixed-point-inspired iterative approach to invert real-world images, we observe that the solution does not achieve convergence, instead oscillating be... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11142 | Emergent Structure in Multi-agent Systems Using Geometric Embeddings | [
"eess.SY",
"cs.RO",
"cs.SY",
"math.GT"
] | This work investigates the self-organization of multi-agent systems into closed trajectories, a common requirement in unmanned aerial vehicle (UAV) surveillance tasks. In such scenarios, smooth, unbiased control signals save energy and mitigate mechanical strain. We propose a decentralized control system architecture t... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11144 | CLMIA: Membership Inference Attacks via Unsupervised Contrastive
Learning | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Since machine learning model is often trained on a limited data set, the model is trained multiple times on the same data sample, which causes the model to memorize most of the training set data. Membership Inference Attacks (MIAs) exploit this feature to determine whether a data sample is used for training a machine l... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11148 | TabDeco: A Comprehensive Contrastive Framework for Decoupled
Representations in Tabular Data | [
"cs.LG",
"cs.AI"
] | Representation learning is a fundamental aspect of modern artificial intelligence, driving substantial improvements across diverse applications. While selfsupervised contrastive learning has led to significant advancements in fields like computer vision and natural language processing, its adaptation to tabular data pr... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11149 | From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis | [
"cs.LG",
"cs.SI"
] | Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets become more common, identifying efficient methods for representing and analyzing them becomes crucial. Th... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2411.11150 | A Comprehensive Survey on Visual Question Answering Datasets and
Algorithms | [
"cs.CV"
] | Visual question answering (VQA) refers to the problem where, given an image and a natural language question about the image, a correct natural language answer has to be generated. A VQA model has to demonstrate both the visual understanding of the image and the semantic understanding of the question, demonstrating reas... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11151 | Person Segmentation and Action Classification for Multi-Channel
Hemisphere Field of View LiDAR Sensors | [
"cs.CV",
"cs.RO"
] | Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11159 | Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy
Through SNR-Weighted Model Aggregation | [
"cs.LG"
] | The increasing demand for data usage in wireless communications requires using wider bands in the spectrum, especially for backhaul links. Yet, allocations in the spectrum for non-communication systems inhibit merging bands to achieve wider bandwidth. To overcome this issue, spectrum-sharing or opportunistic spectrum u... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11161 | MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records | [
"cs.LG",
"cs.AI"
] | The adoption of digital systems in healthcare has resulted in the accumulation of vast electronic health records (EHRs), offering valuable data for machine learning methods to predict patient health outcomes. However, single-visit records of patients are often neglected in the training process due to the lack of annota... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11162 | RPN 2: On Interdependence Function Learning Towards Unifying and
Advancing CNN, RNN, GNN, and Transformer | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.IT",
"math.IT",
"stat.ML"
] | This paper builds upon our previous work on the Reconciled Polynomial Network (RPN). The original RPN model was designed under the assumption of input data independence, presuming the independence among both individual instances within data batches and attributes in each data instance. However, this assumption often pr... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11171 | LL\"aMmlein: Compact and Competitive German-Only Language Models from
Scratch | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We create two German-only decoder models, LL\"aMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to use. The model training involved several key steps, including extensive data preprocessing, the creation of a custom German tokenizer, th... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11174 | Learning the Sherrington-Kirkpatrick Model Even at Low Temperature | [
"cs.LG",
"cs.DS",
"math.ST",
"stat.ML",
"stat.TH"
] | We consider the fundamental problem of learning the parameters of an undirected graphical model or Markov Random Field (MRF) in the setting where the edge weights are chosen at random. For Ising models, we show that a multiplicative-weight update algorithm due to Klivans and Meka learns the parameters in polynomial tim... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11176 | Infinite Width Limits of Self Supervised Neural Networks | [
"cs.LG"
] | The NTK is a widely used tool in the theoretical analysis of deep learning, allowing us to look at supervised deep neural networks through the lenses of kernel regression. Recently, several works have investigated kernel models for self-supervised learning, hypothesizing that these also shed light on the behavior of wi... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11179 | Enhanced Anime Image Generation Using USE-CMHSA-GAN | [
"cs.CV",
"cs.AI"
] | With the growing popularity of ACG (Anime, Comics, and Games) culture, generating high-quality anime character images has become an important research topic. This paper introduces a novel Generative Adversarial Network model, USE-CMHSA-GAN, designed to produce high-quality anime character images. The model builds upon ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11180 | Robust Defense Against Extreme Grid Events Using Dual-Policy
Reinforcement Learning Agents | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Reinforcement learning (RL) agents are powerful tools for managing power grids. They use large amounts of data to inform their actions and receive rewards or penalties as feedback to learn favorable responses for the system. Once trained, these agents can efficiently make decisions that would be too computationally com... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11182 | Improving User Experience in Preference-Based Optimization of Reward
Functions for Assistive Robots | [
"cs.RO",
"cs.AI",
"cs.HC",
"cs.LG"
] | Assistive robots interact with humans and must adapt to different users' preferences to be effective. An easy and effective technique to learn non-expert users' preferences is through rankings of robot behaviors, for example, robot movement trajectories or gestures. Existing techniques focus on generating trajectories ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11185 | Mixing Neural Networks and Exponential Moving Averages for Predicting
Wireless Links Behavior | [
"cs.NI",
"cs.LG"
] | Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, whic... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11188 | AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning
with Transformers | [
"cs.LG"
] | Language models trained on diverse datasets unlock generalization by in-context learning. Reinforcement Learning (RL) policies can achieve a similar effect by meta-learning within the memory of a sequence model. However, meta-RL research primarily focuses on adapting to minor variations of a single task. It is difficul... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11189 | Freqformer: Frequency-Domain Transformer for 3-D Visualization and
Quantification of Human Retinal Circulation | [
"eess.IV",
"cs.CV"
] | We introduce Freqformer, a novel Transformer-based architecture designed for 3-D, high-definition visualization of human retinal circulation from a single scan in commercial optical coherence tomography angiography (OCTA). Freqformer addresses the challenge of limited signal-to-noise ratio in OCTA volume by utilizing a... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11190 | DeepSPV: An Interpretable Deep Learning Pipeline for 3D Spleen Volume
Estimation from 2D Ultrasound Images | [
"eess.IV",
"cs.CV"
] | Splenomegaly, the enlargement of the spleen, is an important clinical indicator for various associated medical conditions, such as sickle cell disease (SCD). Spleen length measured from 2D ultrasound is the most widely used metric for characterising spleen size. However, it is still considered a surrogate measure, and ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11191 | Accelerating Quantum Emitter Characterization with Latent Neural
Ordinary Differential Equations | [
"quant-ph",
"cond-mat.mtrl-sci",
"cs.LG"
] | Deep neural network models can be used to learn complex dynamics from data and reconstruct sparse or noisy signals, thereby accelerating and augmenting experimental measurements. Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferomet... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11192 | Robot Metabolism: Towards machines that can grow by consuming other
machines | [
"cs.RO",
"cs.MA",
"cs.SY",
"eess.SY"
] | Biological lifeforms can heal, grow, adapt, and reproduce -- abilities essential for sustained survival and development. In contrast, robots today are primarily monolithic machines with limited ability to self-repair, physically develop, or incorporate material from their environments. A key challenge to such physical ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 1,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11196 | PickScan: Object discovery and reconstruction from handheld interactions | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG",
"cs.RO"
] | Reconstructing compositional 3D representations of scenes, where each object is represented with its own 3D model, is a highly desirable capability in robotics and augmented reality. However, most existing methods rely heavily on strong appearance priors for object discovery, therefore only working on those classes of ... | {
"Other": 1,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11197 | Stealing Training Graphs from Graph Neural Networks | [
"cs.LG",
"cs.CR"
] | Graph Neural Networks (GNNs) have shown promising results in modeling graphs in various tasks. The training of GNNs, especially on specialized tasks such as bioinformatics, demands extensive expert annotations, which are expensive and usually contain sensitive information of data providers. The trained GNN models are o... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11199 | BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression | [
"cs.CV",
"eess.IV"
] | The advances in immersive technologies and 3D reconstruction have enabled the creation of digital replicas of real-world objects and environments with fine details. These processes generate vast amounts of 3D data, requiring more efficient compression methods to satisfy the memory and bandwidth constraints associated w... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11200 | Countering Backdoor Attacks in Image Recognition: A Survey and
Evaluation of Mitigation Strategies | [
"cs.CR",
"cs.LG"
] | The widespread adoption of deep learning across various industries has introduced substantial challenges, particularly in terms of model explainability and security. The inherent complexity of deep learning models, while contributing to their effectiveness, also renders them susceptible to adversarial attacks. Among th... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11203 | Debiasing Watermarks for Large Language Models via Maximal Coupling | [
"stat.ML",
"cs.CL",
"cs.CR",
"cs.LG",
"stat.ME"
] | Watermarking language models is essential for distinguishing between human and machine-generated text and thus maintaining the integrity and trustworthiness of digital communication. We present a novel green/red list watermarking approach that partitions the token set into ``green'' and ``red'' lists, subtly increasing... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11205 | Examining Platformization in Cultural Production: A Comparative
Computational Analysis of Hit Songs on TikTok and Spotify | [
"cs.SI"
] | The (re)creation and distribution of cultural products such as music are increasingly shaped by digital platforms. This study explores how TikTok and Spotify, situated in different governance and user contexts, could influence digital music production and reception within each platform and between each other. Focusing ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2411.11206 | Capturing Sparks of Abstraction for the ARC Challenge | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Excellent progress has been made recently in solving ARC Challenge problems. However, it seems that new techniques may be required to push beyond 60% accuracy. Even commercial Large Language Models (LLMs) struggle to 'understand' many of the problems (when given the input and output grids), which makes discovering solu... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11211 | Operator Splitting Covariance Steering for Safe Stochastic Nonlinear
Control | [
"cs.RO",
"math.OC"
] | Most robotics applications are typically accompanied with safety restrictions that need to be satisfied with a high degree of confidence even in environments under uncertainty. Controlling the state distribution of a system and enforcing such specifications as distribution constraints is a promising approach for meetin... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11213 | Making Sigmoid-MSE Great Again: Output Reset Challenges Softmax
Cross-Entropy in Neural Network Classification | [
"cs.LG",
"cs.AI",
"stat.ML"
] | This study presents a comparative analysis of two objective functions, Mean Squared Error (MSE) and Softmax Cross-Entropy (SCE) for neural network classification tasks. While SCE combined with softmax activation is the conventional choice for transforming network outputs into class probabilities, we explore an alternat... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11214 | DeforHMR: Vision Transformer with Deformable Cross-Attention for 3D
Human Mesh Recovery | [
"cs.CV"
] | Human Mesh Recovery (HMR) is an important yet challenging problem with applications across various domains including motion capture, augmented reality, and biomechanics. Accurately predicting human pose parameters from a single image remains a challenging 3D computer vision task. In this work, we introduce DeforHMR, a ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11216 | Optimization free control and ground force estimation with momentum
observer for a multimodal legged aerial robot | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Legged-aerial multimodal robots can make the most of both legged and aerial systems. In this paper, we propose a control framework that bypasses heavy onboard computers by using an optimization-free Explicit Reference Governor that incorporates external thruster forces from an attitude controller. Ground reaction force... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11217 | MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs | [
"cs.DC",
"cs.AI",
"cs.LG"
] | Efficient deployment of large language models, particularly Mixture of Experts (MoE), on resource-constrained platforms presents significant challenges, especially in terms of computational efficiency and memory utilization. The MoE architecture, renowned for its ability to increase model capacity without a proportiona... | {
"Other": 1,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11218 | Conjugate Momentum-Based Estimation of External Forces for Bio-Inspired
Morphing Wing Flight | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Dynamic morphing wing flights present significant challenges in accurately estimating external forces due to complex interactions between aerodynamics, rapid wing movements, and external disturbances. Traditional force estimation methods often struggle with unpredictable disturbances like wind gusts or unmodeled impact... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11219 | Relational Contrastive Learning and Masked Image Modeling for Scene Text
Recognition | [
"cs.CV"
] | Context-aware methods have achieved remarkable advancements in supervised scene text recognition by leveraging semantic priors from words. Considering the heterogeneity of text and background in STR, we propose that such contextual priors can be reinterpreted as the relations between textual elements, serving as effect... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11221 | Data Driven Automatic Electrical Machine Preliminary Design with
Artificial Intelligence Expert Guidance | [
"eess.SY",
"cs.LG",
"cs.SY"
] | This paper presents a data-driven electrical machine design (EMD) framework using wound-rotor synchronous generator (WRSG) as a design example. Unlike traditional preliminary EMD processes that heavily rely on expertise, this framework leverages an artificial-intelligence based expert database, to provide preliminary d... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11222 | The Sound of Water: Inferring Physical Properties from Pouring Liquids | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | We study the connection between audio-visual observations and the underlying physics of a mundane yet intriguing everyday activity: pouring liquids. Given only the sound of liquid pouring into a container, our objective is to automatically infer physical properties such as the liquid level, the shape and size of the co... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 1,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11223 | Efficient Transfer Learning for Video-language Foundation Models | [
"cs.CV"
] | Pre-trained vision-language models provide a robust foundation for efficient transfer learning across various downstream tasks. In the field of video action recognition, mainstream approaches often introduce additional parameter modules to capture temporal information. While the increased model capacity brought by thes... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11224 | Don't Be So Positive: Negative Step Sizes in Second-Order Methods | [
"cs.LG",
"math.OC",
"stat.ML"
] | The value of second-order methods lies in the use of curvature information. Yet, this information is costly to extract and once obtained, valuable negative curvature information is often discarded so that the method is globally convergent. This limits the effectiveness of second-order methods in modern machine learning... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11225 | Online Item Cold-Start Recommendation with Popularity-Aware
Meta-Learning | [
"cs.IR"
] | With the rise of e-commerce and short videos, online recommender systems that can capture users' interests and update new items in real-time play an increasingly important role. In both online and offline recommendation, the cold-start problem due to interaction sparsity has been affecting the recommendation effect of ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 1,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11230 | Network-Security Informed Offer-Making of Aggregator with Utility-Owned
Storage Lease Opportunity: Stochastic Stackelberg Game and Distributed
Solution Methods | [
"eess.SY",
"cs.SY"
] | Aggregators of distributed energy resources are increasingly encouraged to participate in wholesale market bidding. However, the delivery of the power they are awarded can result in over-voltage or congestion issues within the distribution network (DN). The opportunity to lease energy storage from the utility that mana... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11231 | BeautyBank: Encoding Facial Makeup in Latent Space | [
"cs.CV"
] | The advancement of makeup transfer, editing, and image encoding has demonstrated their effectiveness and superior quality. However, existing makeup works primarily focus on low-dimensional features such as color distributions and patterns, limiting their versatillity across a wide range of makeup applications. Futhermo... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11233 | Noise Filtering Benchmark for Neuromorphic Satellites Observations | [
"cs.CV",
"cs.LG"
] | Event cameras capture sparse, asynchronous brightness changes which offer high temporal resolution, high dynamic range, low power consumption, and sparse data output. These advantages make them ideal for Space Situational Awareness, particularly in detecting resident space objects moving within a telescope's field of v... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11235 | MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large
Language Models on Human Emotion Analysis | [
"cs.CL",
"cs.AI"
] | Artificial Intelligence (AI) has demonstrated significant capabilities in various fields, and in areas such as human-computer interaction (HCI), embodied intelligence, and the design and animation of virtual digital humans, both practitioners and users are increasingly concerned with AI's ability to understand and expr... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11238 | Reliable Learning of Halfspaces under Gaussian Marginals | [
"cs.LG",
"cs.DS",
"stat.ML"
] | We study the problem of PAC learning halfspaces in the reliable agnostic model of Kalai et al. (2012). The reliable PAC model captures learning scenarios where one type of error is costlier than the others. Our main positive result is a new algorithm for reliable learning of Gaussian halfspaces on $\mathbb{R}^d$ with s... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11240 | Controlling Diversity at Inference: Guiding Diffusion Recommender Models
with Targeted Category Preferences | [
"cs.IR"
] | Diversity control is an important task to alleviate bias amplification and filter bubble problems. The desired degree of diversity may fluctuate based on users' daily moods or business strategies. However, existing methods for controlling diversity often lack flexibility, as diversity is decided during training and can... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 1,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11242 | Mirror Descent on Reproducing Kernel Banach Spaces | [
"cs.LG",
"math.OC",
"stat.ML"
] | Recent advances in machine learning have led to increased interest in reproducing kernel Banach spaces (RKBS) as a more general framework that extends beyond reproducing kernel Hilbert spaces (RKHS). These works have resulted in the formulation of representer theorems under several regularized learning schemes. However... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11247 | ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification | [
"cs.CL",
"cs.AI"
] | In this paper, we propose ZeFaV - a zero-shot based fact-checking verification framework to enhance the performance on fact verification task of large language models by leveraging the in-context learning ability of large language models to extract the relations among the entities within a claim, re-organized the infor... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11249 | EXCON: Extreme Instance-based Contrastive Representation Learning of
Severely Imbalanced Multivariate Time Series for Solar Flare Prediction | [
"cs.LG",
"cs.AI"
] | In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data from solar active regions, recorded by solar imaging observatories, are transformed into multivariate time series to enable solar flare pre... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11252 | DrivingSphere: Building a High-fidelity 4D World for Closed-loop
Simulation | [
"cs.RO",
"cs.CV"
] | Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. However, many existing simulations need to predict waypoints along fixed routes on public datasets or synthetic photorealistic data, \ie, open-lo... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11254 | Semantic or Covariate? A Study on the Intractable Case of
Out-of-Distribution Detection | [
"cs.CV"
] | The primary goal of out-of-distribution (OOD) detection tasks is to identify inputs with semantic shifts, i.e., if samples from novel classes are absent in the in-distribution (ID) dataset used for training, we should reject these OOD samples rather than misclassifying them into existing ID classes. However, we find th... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11256 | Progressive Generalization Risk Reduction for Data-Efficient Causal
Effect Estimation | [
"cs.LG",
"stat.ML"
] | Causal effect estimation (CEE) provides a crucial tool for predicting the unobserved counterfactual outcome for an entity. As CEE relaxes the requirement for ``perfect'' counterfactual samples (e.g., patients with identical attributes and only differ in treatments received) that are impractical to obtain and can instea... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11259 | Graph Retention Networks for Dynamic Graphs | [
"cs.LG"
] | In this work, we propose Graph Retention Network as a unified architecture for deep learning on dynamic graphs. The GRN extends the core computational manner of retention to dynamic graph data as graph retention, which empowers the model with three key computational paradigms that enable training parallelism, $O(1)$ lo... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11260 | Large corpora and large language models: a replicable method for
automating grammatical annotation | [
"cs.CL"
] | Much linguistic research relies on annotated datasets of features extracted from text corpora, but the rapid quantitative growth of these corpora has created practical difficulties for linguists to manually annotate large data samples. In this paper, we present a replicable, supervised method that leverages large langu... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11262 | Cross-Patient Pseudo Bags Generation and Curriculum Contrastive Learning
for Imbalanced Multiclassification of Whole Slide Image | [
"cs.CV",
"cs.AI"
] | Pathology computing has dramatically improved pathologists' workflow and diagnostic decision-making processes. Although computer-aided diagnostic systems have shown considerable value in whole slide image (WSI) analysis, the problem of multi-classification under sample imbalance remains an intractable challenge. To add... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11265 | GROOT: Effective Design of Biological Sequences with Limited
Experimental Data | [
"cs.LG",
"q-bio.QM"
] | Latent space optimization (LSO) is a powerful method for designing discrete, high-dimensional biological sequences that maximize expensive black-box functions, such as wet lab experiments. This is accomplished by learning a latent space from available data and using a surrogate model to guide optimization algorithms to... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11266 | VersaTune: An Efficient Data Composition Framework for Training
Multi-Capability LLMs | [
"cs.CL"
] | Large-scale pretrained models, particularly Large Language Models (LLMs), have exhibited remarkable capabilities in handling multiple tasks across domains due to their emergent properties. These capabilities are further augmented during the Supervised Fine-Tuning (SFT) phase. Despite their potential, existing work main... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11268 | ACE2: Accurately learning subseasonal to decadal atmospheric variability
and forced responses | [
"physics.ao-ph",
"cs.LG"
] | Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator version 2) and its application to reproducing atmospheric variability... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11275 | Effective Predictive Modeling for Emergency Department Visits and
Evaluating Exogenous Variables Impact: Using Explainable Meta-learning
Gradient Boosting | [
"cs.LG",
"cs.NE"
] | Over an extensive duration, administrators and clinicians have endeavoured to predict Emergency Department (ED) visits with precision, aiming to optimise resource distribution. Despite the proliferation of diverse AI-driven models tailored for precise prognostication, this task persists as a formidable challenge, besie... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 1,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11276 | Coupled Integral PINN for conservation law | [
"physics.flu-dyn",
"cs.LG"
] | The Physics-Informed Neural Network (PINN) is an innovative approach to solve a diverse array of partial differential equations (PDEs) leveraging the power of neural networks. This is achieved by minimizing the residual loss associated with the explicit physical information, usually coupled with data derived from initi... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11278 | Towards Open-Vocabulary Audio-Visual Event Localization | [
"cs.CV",
"cs.MM"
] | The Audio-Visual Event Localization (AVEL) task aims to temporally locate and classify video events that are both audible and visible. Most research in this field assumes a closed-set setting, which restricts these models' ability to handle test data containing event categories absent (unseen) during training. Recently... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11282 | Continuous K-space Recovery Network with Image Guidance for Fast MRI
Reconstruction | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Magnetic resonance imaging (MRI) is a crucial tool for clinical diagnosis while facing the challenge of long scanning time. To reduce the acquisition time, fast MRI reconstruction aims to restore high-quality images from the undersampled k-space. Existing methods typically train deep learning models to map the undersam... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11283 | Multi-Hyperbolic Space-based Heterogeneous Graph Attention Network | [
"cs.LG",
"cs.AI"
] | To leverage the complex structures within heterogeneous graphs, recent studies on heterogeneous graph embedding use a hyperbolic space, characterized by a constant negative curvature and exponentially increasing space, which aligns with the structural properties of heterogeneous graphs. However, despite heterogeneous g... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11284 | Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic
and Heterophilic Graphs | [
"cs.LG"
] | Graph Neural Networks (GNNs) have excelled in handling graph-structured data, attracting significant research interest. However, two primary challenges have emerged: interference between topology and attributes distorting node representations, and the low-pass filtering nature of most GNNs leading to the oversight of v... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11285 | Zero-Shot Automatic Annotation and Instance Segmentation using
LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for
Deep Learning Model Development | [
"cs.CV",
"cs.AI"
] | Currently, deep learning-based instance segmentation for various applications (e.g., Agriculture) is predominantly performed using a labor-intensive process involving extensive field data collection using sophisticated sensors, followed by careful manual annotation of images, presenting significant logistical and finan... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11287 | Reducing Label Dependency for Underwater Scene Understanding: A Survey
of Datasets, Techniques and Applications | [
"cs.CV"
] | Underwater surveys provide long-term data for informing management strategies, monitoring coral reef health, and estimating blue carbon stocks. Advances in broad-scale survey methods, such as robotic underwater vehicles, have increased the range of marine surveys but generate large volumes of imagery requiring analysis... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11288 | Neuron: Learning Context-Aware Evolving Representations for Zero-Shot
Skeleton Action Recognition | [
"cs.CV"
] | Zero-shot skeleton action recognition is a non-trivial task that requires robust unseen generalization with prior knowledge from only seen classes and shared semantics. Existing methods typically build the skeleton-semantics interactions by uncontrollable mappings and conspicuous representations, thereby can hardly cap... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11289 | LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large
Language Models | [
"cs.CL",
"cs.AI"
] | Creating high-quality, large-scale datasets for large language models (LLMs) often relies on resource-intensive, GPU-accelerated models for quality filtering, making the process time-consuming and costly. This dependence on GPUs limits accessibility for organizations lacking significant computational infrastructure. To... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11291 | Performance Evaluation of Geospatial Images based on Zarr and Tiff | [
"cs.CV"
] | This evaluate the performance of geospatial image processing using two distinct data storage formats: Zarr and TIFF. Geospatial images, converted to numerous applications like environmental monitoring, urban planning, and disaster management. Traditional Tagged Image File Format is mostly used because it is simple and ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11293 | SADDE: Semi-supervised Anomaly Detection with Dependable Explanations | [
"cs.LG"
] | Semi-supervised learning holds a pivotal position in anomaly detection applications, yet identifying anomaly patterns with a limited number of labeled samples poses a significant challenge. Furthermore, the absence of interpretability poses major obstacles to the practical adoption of semi-supervised frameworks. The ma... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11295 | Transcending Language Boundaries: Harnessing LLMs for Low-Resource
Language Translation | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated remarkable success across a wide range of tasks and domains. However, their performance in low-resource language translation, particularly when translating into these languages, remains underexplored. This gap poses significant challenges, as linguistic barriers hinder the... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.