Imagen de Portada

Deep-and-Wide Learning: Enhancing Data-Driven Inference via Synergistic Learning of Inter- and Intra-Data Representations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Islam, Md Tauhidul, Xing, Lei
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Vision-based Deep Learning Analysis of Unordered Biomedical Tabular Datasets via Optimal Spatial Cartography
por: Mostafa, Sakib, et al.
Publicado: (2026)

Knowledge Graph Modulated Deep Learning for Limited-Sample Clinical Data Analysis
por: Xue, Yuwei, et al.
Publicado: (2026)

Toward a universal foundation model for graph-structured data
por: Mostafa, Sakib, et al.
Publicado: (2026)

Graph Transformer-Based Pathway Embedding for Cancer Prognosis
por: Howlader, Koushik, et al.
Publicado: (2026)

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
por: Zhou, Zixia, et al.
Publicado: (2025)

Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes
por: Le, Huy Q., et al.
Publicado: (2025)

Dual-Temporal LSTM with Hybrid Attention for Airline Passenger Load Factor Forecasting: Integrating Intra-Flight and Inter-Flight Booking Dynamics
por: Islam, ASM Nazrul, et al.
Publicado: (2026)

Transformation of Biological Networks into Images via Semantic Cartography for Visual Interpretation and Scalable Deep Analysis
por: Mostafa, Sakib, et al.
Publicado: (2025)

Data-Driven Self-Supervised Graph Representation Learning
por: Samy, Ahmed E., et al.
Publicado: (2024)

Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models
por: Wei, Xiwen, et al.
Publicado: (2025)

Deep Causal Learning: Representation, Discovery and Inference
por: Deng, Zizhen, et al.
Publicado: (2022)

Goal-Conditioned Reinforcement Learning for Data-Driven Maritime Navigation
por: Vaidheeswaran, Vaishnav, et al.
Publicado: (2025)

WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning Models
por: Islam, Md. Khairul, et al.
Publicado: (2024)

Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation
por: Sikder, Md Fahim, et al.
Publicado: (2024)

Less is More: Multimodal Region Representation via Pairwise Inter-view Learning
por: Namgung, Min, et al.
Publicado: (2025)

Wide & Deep Learning for Node Classification
por: Chen, Yancheng, et al.
Publicado: (2025)

Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
por: Jia, Chengxing, et al.
Publicado: (2024)

Explainable Multi-Modal Deep Learning for Automatic Detection of Lung Diseases from Respiratory Audio Signals
por: Saky, S M Asiful Islam, et al.
Publicado: (2025)

Hierarchy Representation of Data in Machine Learnings
por: Yegang, Han, et al.
Publicado: (2023)

Location Agnostic Adaptive Rain Precipitation Prediction using Deep Learning
por: Islam, Md Shazid, et al.
Publicado: (2024)

Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
por: Nguyen, Duong M., et al.
Publicado: (2025)

Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning
por: Huang, Chun-Kai, et al.
Publicado: (2024)

TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency
por: Imteaj, Ahmed, et al.
Publicado: (2024)

Representation-Driven Reinforcement Learning
por: Nabati, Ofir, et al.
Publicado: (2023)

Harmonizing Intra-coherence and Inter-divergence in Ensemble Attacks for Adversarial Transferability
por: Ma, Zhaoyang, et al.
Publicado: (2025)

DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing
por: Li, Conglong, et al.
Publicado: (2022)

Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
por: Yin, Huifeng, et al.
Publicado: (2024)

How does the Performance of the Data-driven Traffic Flow Forecasting Models deteriorate with Increasing Forecasting Horizon? An Extensive Approach Considering Statistical, Machine Learning and Deep Learning Models
por: Sherfenaz, Amanta, et al.
Publicado: (2025)

FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
por: Islam, Md Sirajul, et al.
Publicado: (2024)

Learning Interpretable Rules for Scalable Data Representation and Classification
por: Wang, Zhuo, et al.
Publicado: (2023)

Simulation Priors for Data-Efficient Deep Learning
por: Treven, Lenart, et al.
Publicado: (2025)

Adaptive Data Exploitation in Deep Reinforcement Learning
por: Yuan, Mingqi, et al.
Publicado: (2025)

LLM Embeddings for Deep Learning on Tabular Data
por: Koloski, Boshko, et al.
Publicado: (2025)

Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting
por: Tsoumplekas, Georgios, et al.
Publicado: (2024)

Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
por: Pasand, Ali Saheb, et al.
Publicado: (2026)

Disentangled Representation Learning for Causal Inference with Instruments
por: Cheng, Debo, et al.
Publicado: (2024)

Collapsed Inference for Bayesian Deep Learning
por: Zeng, Zhe, et al.
Publicado: (2023)

Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
por: Liao, Xinting, et al.
Publicado: (2024)

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences
por: Zhang, Xingshen, et al.
Publicado: (2025)

Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
por: Tello, Andres, et al.
Publicado: (2024)