{"total":34,"rows":[{"id":"2102.01063","title":"Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition","created":"2021-02-01","cited_by_count":73,"area":null},{"id":"2305.00241","title":"When Deep Learning Meets Polyhedral Theory: A Survey","created":"2023-04-29","cited_by_count":39,"area":null},{"id":"2104.08135","title":"Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums","created":"2021-04-16","cited_by_count":37,"area":null},{"id":"2302.12828","title":"SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries","created":"2023-02-24","cited_by_count":31,"area":null},{"id":"2207.07696","title":"Algorithmic Determination of the Combinatorial Structure of the Linear Regions of ReLU Neural Networks","created":"2022-07-15","cited_by_count":22,"area":null},{"id":"2107.00379","title":"On the Expected Complexity of Maxout Networks","created":"2021-07-01","cited_by_count":15,"area":null},{"id":"2202.11749","title":"Are All Linear Regions Created Equal?","created":"2022-02-23","cited_by_count":13,"area":null},{"id":"2204.08624","title":"Topology and geometry of data manifold in deep learning","created":"2022-04-19","cited_by_count":12,"area":null},{"id":"2203.11355","title":"Origami in N dimensions: How feed-forward networks manufacture linear separability","created":"2022-03-21","cited_by_count":9,"area":null},{"id":"2210.05742","title":"Curved Representation Space of Vision Transformers","created":"2022-10-11","cited_by_count":8,"area":null},{"id":"2103.17174","title":"Using activation histograms to bound the number of affine regions in ReLU feed-forward neural networks","created":"2021-03-31","cited_by_count":7,"area":null},{"id":"2305.09145","title":"Deep ReLU Networks Have Surprisingly Simple Polytopes","created":"2023-05-16","cited_by_count":5,"area":null},{"id":"2306.03406","title":"Deep neural networks architectures from the perspective of manifold learning","created":"2023-06-06","cited_by_count":5,"area":null},{"id":"2101.05490","title":"Neural networks behave as hash encoders: An empirical study","created":"2021-01-14","cited_by_count":3,"area":null},{"id":"2301.05816","title":"Understanding the Spectral Bias of Coordinate Based MLPs Via Training Dynamics","created":"2023-01-14","cited_by_count":3,"area":null},{"id":"2510.18120","title":"Generalization Below the Edge of Stability: The Role of Data Geometry","created":"2025-10-20","cited_by_count":3,"area":null},{"id":"2110.02863","title":"Exploring the Common Principal Subspace of Deep Features in Neural Networks","created":"2021-10-06","cited_by_count":2,"area":null},{"id":"2502.03016","title":"An analysis of optimization problems involving ReLU neural networks","created":"2025-02-05","cited_by_count":2,"area":null},{"id":"2105.07831","title":"How to Explain Neural Networks: an Approximation Perspective","created":"2021-05-17","cited_by_count":1,"area":null},{"id":"2110.13581","title":"Gradient representations in ReLU networks as similarity functions","created":"2021-10-26","cited_by_count":1,"area":null},{"id":"2405.20174","title":"A Computational Tropical Geometry Framework for Neural Networks","created":"2024-05-30","cited_by_count":1,"area":null},{"id":"2502.01360","title":"A Quotient Homology Theory of Representation in Neural Networks","created":"2025-02-03","cited_by_count":1,"area":null},{"id":"2509.15441","title":"Computing Linear Regions in Neural Networks with Skip Connections","created":"2025-09-18","cited_by_count":1,"area":null},{"id":"2605.04946","title":"Training-Time Batch Normalization Reshapes Local Partition Geometry in Piecewise-Affine Networks","created":"2026-05-06","cited_by_count":1,"area":null},{"id":"2605.06218","title":"AffineLens: Capturing the Continuous Piecewise Affine Functions of Neural Networks","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2605.06300","title":"Region Seeding via Pre-Activation Regularization: A Geometric View of Piecewise Affine Neural Networks","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2606.07728","title":"Characterizing the Discrete Geometry of ReLU Networks","created":"2026-06-05","cited_by_count":1,"area":null},{"id":"2302.09949","title":"SpecXAI -- Spectral interpretability of Deep Learning Models","created":"2023-02-20","cited_by_count":0,"area":null},{"id":"2304.14888","title":"The Power of Typed Affine Decision Structures: A Case Study","created":"2023-04-28","cited_by_count":0,"area":null},{"id":"2305.05562","title":"SkelEx and BoundEx: Natural Visualization of ReLU Neural Networks","created":"2023-05-09","cited_by_count":0,"area":null},{"id":"2411.19819","title":"GradAlign for Training-free Model Performance Inference","created":"2024-11-29","cited_by_count":0,"area":null},{"id":"2503.08502","title":"The Space Between: On Folding, Symmetries and Sampling","created":"2025-03-11","cited_by_count":0,"area":null},{"id":"2606.03290","title":"Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.21687","title":"Expressivity Saturation: Reduced Affine Region Usage Under Increasing Task Complexity","created":"2026-06-19","cited_by_count":0,"area":null}],"next_cursor":null}