Imagen de Portada

Feature Space Topology Control via Hopkins Loss

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Vaaras, Einari, Airaksinen, Manu
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

PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse
por: Vaaras, Einari, et al.
Publicado: (2024)

Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation
por: Vaaras, Einari, et al.
Publicado: (2026)

Investigating Affect Mining Techniques for Annotation Sample Selection in the Creation of Finnish Affective Speech Corpus
por: Lahtinen, Kalle, et al.
Publicado: (2025)

Topology-aware Reinforcement Feature Space Reconstruction for Graph Data
por: Ying, Wangyang, et al.
Publicado: (2024)

Visualizing Loss Functions as Topological Landscape Profiles
por: Geniesse, Caleb, et al.
Publicado: (2024)

Evaluating Loss Landscapes from a Topology Perspective
por: Xie, Tiankai, et al.
Publicado: (2024)

TopoPrune: Robust Data Pruning via Unified Latent Space Topology
por: Roy, Arjun, et al.
Publicado: (2026)

Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis
por: Chen, Jiaqing, et al.
Publicado: (2026)

TangledFeatures: Robust Feature Selection in Highly Correlated Spaces
por: Sunny, Allen Daniel
Publicado: (2025)

Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions
por: Hou, Qinhan, et al.
Publicado: (2026)

Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method
por: Tao, Qinghua, et al.
Publicado: (2024)

Angular Steering: Behavior Control via Rotation in Activation Space
por: Vu, Hieu M., et al.
Publicado: (2025)

Federated Imputation under Heterogeneous Feature Spaces
por: Hocine, Imane, et al.
Publicado: (2026)

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces
por: Geissler, Daniel, et al.
Publicado: (2024)

Disentangled (Un)Controllable Features
por: Kooi, Jacob E., et al.
Publicado: (2022)

Preserving Vector Space Properties in Dimensionality Reduction: A Relationship Preserving Loss Framework
por: Weinwurm, Eddi, et al.
Publicado: (2025)

ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
por: Zhu, Yuanshao, et al.
Publicado: (2024)

Gradient Aligned Regression via Pairwise Losses
por: Zhu, Dixian, et al.
Publicado: (2024)

Decoupled Split Learning via Auxiliary Loss
por: Zihad, Anower, et al.
Publicado: (2026)

PatchCTG: Patch Cardiotocography Transformer for Antepartum Fetal Health Monitoring
por: Khan, M. Jaleed, et al.
Publicado: (2024)

Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
por: Barannikov, Serguei, et al.
Publicado: (2020)

Interpretable Policy Distillation for Power Grid Topology Control
por: Dmitruka, Aleksandra, et al.
Publicado: (2026)

Barycentric Neural Networks and Length-Weighted Persistent Entropy Loss: A Green Geometric and Topological Framework for Function Approximation
por: Toscano-Duran, Victor, et al.
Publicado: (2025)

Topological Deep Learning with State-Space Models: A Mamba Approach for Simplicial Complexes
por: Montagna, Marco, et al.
Publicado: (2024)

Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
por: Choi, Jaemoo, et al.
Publicado: (2026)

packetLSTM: Dynamic LSTM Framework for Streaming Data with Varying Feature Space
por: Agarwal, Rohit, et al.
Publicado: (2024)

Rethinking the Harmonic Loss via Non-Euclidean Distance Layers
por: Miller-Golub, Maxwell, et al.
Publicado: (2026)

Early-Warning Signals of Grokking via Loss-Landscape Geometry
por: Xu, Yongzhong
Publicado: (2026)

SynergyKGC: Reconciling Topological Heterogeneity in Knowledge Graph Completion via Topology-Aware Synergy
por: Zou, Xuecheng, et al.
Publicado: (2026)

Feature Identification via the Empirical NTK
por: Lin, Jennifer
Publicado: (2025)

Scaling Attention via Feature Sparsity
por: Xie, Yan, et al.
Publicado: (2026)

Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders
por: Lan, Michael, et al.
Publicado: (2024)

CCMamba: Topologically-Informed Selective State-Space Networks on Combinatorial Complexes for Higher-Order Graph Learning
por: Chen, Jiawen, et al.
Publicado: (2026)

Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
por: Huang, Chen, et al.
Publicado: (2024)

Not All Latent Spaces Are Flat: Hyperbolic Concept Control
por: Briglia, Maria Rosaria, et al.
Publicado: (2026)

Holdout-Loss-Based Data Selection for LLM Finetuning via In-Context Learning
por: Zhang, Ling, et al.
Publicado: (2025)

Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective
por: Phan, Hoang, et al.
Publicado: (2022)

Controlling Topological Defects in Polar Fluids via Reinforcement Learning
por: Singh, Abhinav, et al.
Publicado: (2025)

CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting
por: Zha, Yaohua, et al.
Publicado: (2026)

GT-Space: Enhancing Heterogeneous Collaborative Perception with Ground Truth Feature Space
por: Wang, Wentao, et al.
Publicado: (2026)