Imagen de Portada

Spatial Clustering of Citizen Science Data Improves Downstream Species Distribution Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ahmed, Nahian, Roth, Mark, Hallman, Tyler A., Robinson, W. Douglas, Hutchinson, Rebecca A.
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Handling Missing Data in Downstream Tasks With Distribution-Preserving Guarantees
por: Bordoloi, Rahul, et al.
Publicado: (2025)

Forecasting for Swap Regret for All Downstream Agents
por: Roth, Aaron, et al.
Publicado: (2024)

Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
por: Amad, Harry, et al.
Publicado: (2025)

Hybrid Spatial Representations for Species Distribution Modeling
por: Yuan, Shiran, et al.
Publicado: (2024)

An Improved and Generalised Analysis for Spectral Clustering
por: Tyler, George, et al.
Publicado: (2025)

Enhancing Downstream Analysis in Genome Sequencing: Species Classification While Basecalling
por: Kodra, Riselda, et al.
Publicado: (2025)

Memorization in Self-Supervised Learning Improves Downstream Generalization
por: Wang, Wenhao, et al.
Publicado: (2024)

Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance
por: Guarino, Vanessa Emanuela, et al.
Publicado: (2026)

Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses
por: Lu, Jiuyao, et al.
Publicado: (2025)

Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks
por: Rai, Sweta, et al.
Publicado: (2025)

Learning to Poison Large Language Models for Downstream Manipulation
por: Zhou, Xiangyu, et al.
Publicado: (2024)

SlotFM: A Motion Foundation Model with Slot Attention for Diverse Downstream Tasks
por: Park, Junyong, et al.
Publicado: (2025)

CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
por: Abdelwahed, Hager Radi, et al.
Publicado: (2025)

Veridical Data Science for Medical Foundation Models
por: Alaa, Ahmed, et al.
Publicado: (2024)

Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization
por: Nahian, Md Sultan Al, et al.
Publicado: (2024)

Quantifying the Importance of Data Alignment in Downstream Model Performance
por: Chawla, Krrish, et al.
Publicado: (2025)

Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions
por: Nardi, Mirko, et al.
Publicado: (2024)

Mixing Configurations for Downstream Prediction
por: Wang, Juntang, et al.
Publicado: (2025)

Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective
por: Xu, Chengyin, et al.
Publicado: (2025)

Multi-Scale and Multimodal Species Distribution Modeling
por: van Tiel, Nina, et al.
Publicado: (2024)

Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
por: Cheng, Yinan, et al.
Publicado: (2024)

Transferring Knowledge from Large Foundation Models to Small Downstream Models
por: Qiu, Shikai, et al.
Publicado: (2024)

LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
por: Sikorski, Antony, et al.
Publicado: (2025)

Reconciling Model Multiplicity for Downstream Decision Making
por: Du, Ally Yalei, et al.
Publicado: (2024)

Mitigating Downstream Model Risks via Model Provenance
por: Wang, Keyu, et al.
Publicado: (2024)

Smoke and Mirrors in Causal Downstream Tasks
por: Cadei, Riccardo, et al.
Publicado: (2024)

Improving Pediatric Emergency Department Triage with Modality Dropout in Late Fusion Multimodal EHR Models
por: Yang, Tyler, et al.
Publicado: (2026)

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation
por: Kang, Sungmin, et al.
Publicado: (2025)

Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
por: Riabi, Arij, et al.
Publicado: (2021)

Using the SEKF to Transfer NN Models of Dynamical Systems with Limited Data
por: Hammond, Joshua E., et al.
Publicado: (2026)

Attacking Attention of Foundation Models Disrupts Downstream Tasks
por: Silva, Hondamunige Prasanna, et al.
Publicado: (2025)

Vision-Language Model Selection and Reuse for Downstream Adaptation
por: Tan, Hao-Zhe, et al.
Publicado: (2025)

Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on Text
por: Jarca, Andrei, et al.
Publicado: (2025)

Aligning the Evaluation of Probabilistic Predictions with Downstream Value
por: Shahroudi, Novin, et al.
Publicado: (2025)

Harvesting AlphaEarth: Benchmarking the Geospatial Foundation Model for Agricultural Downstream Tasks
por: Ma, Yuchi, et al.
Publicado: (2025)

Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
por: Egami, Naoki, et al.
Publicado: (2023)

Self-Improving Diffusion Models with Synthetic Data
por: Alemohammad, Sina, et al.
Publicado: (2024)

Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection
por: Deshpande, Vijeta, et al.
Publicado: (2026)

Scaling Laws for Downstream Task Performance of Large Language Models
por: Isik, Berivan, et al.
Publicado: (2024)

Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction
por: Liao, Weibin, et al.
Publicado: (2024)