{"total":23,"rows":[{"id":"2202.00665","title":"Tutorial on amortized optimization","created":"2022-02-01","cited_by_count":83,"area":null},{"id":"2103.16178","title":"Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking","created":"2021-03-30","cited_by_count":47,"area":null},{"id":"2207.08675","title":"Learning differentiable solvers for systems with hard constraints","created":"2022-07-18","cited_by_count":34,"area":null},{"id":"2210.01802","title":"Alternating Differentiation for Optimization Layers","created":"2022-10-03","cited_by_count":22,"area":null},{"id":"2203.14804","title":"Partially Does It: Towards Scene-Level FG-SBIR with Partial Input","created":"2022-03-28","cited_by_count":20,"area":null},{"id":"2402.13077","title":"Mechanistic Neural Networks for Scientific Machine Learning","created":"2024-02-20","cited_by_count":16,"area":null},{"id":"2106.10349","title":"The Perils of Learning Before Optimizing","created":"2021-06-18","cited_by_count":15,"area":null},{"id":"2203.16210","title":"Learning of Global Objective for Network Flow in Multi-Object Tracking","created":"2022-03-30","cited_by_count":11,"area":null},{"id":"2510.08748","title":"Conformal Risk Training: End-to-End Optimization of Conformal Risk Control","created":"2025-10-09","cited_by_count":11,"area":null},{"id":"2208.09424","title":"Hierarchical Compositional Representations for Few-shot Action Recognition","created":"2022-08-19","cited_by_count":10,"area":null},{"id":"2210.02159","title":"Differentiable Mathematical Programming for Object-Centric Representation Learning","created":"2022-10-05","cited_by_count":6,"area":null},{"id":"2311.02940","title":"The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning","created":"2023-11-06","cited_by_count":6,"area":null},{"id":"2109.00741","title":"End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning","created":"2021-09-02","cited_by_count":3,"area":null},{"id":"2510.18215","title":"The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective","created":"2025-10-21","cited_by_count":3,"area":null},{"id":"2303.15414","title":"Learnable Graph Matching: A Practical Paradigm for Data Association","created":"2023-03-27","cited_by_count":2,"area":null},{"id":"2305.01473","title":"Efficient Sensitivity Analysis for Parametric Robust Markov Chains","created":"2023-05-01","cited_by_count":2,"area":null},{"id":"2412.20679","title":"Differentiable Convex Optimization Layers in Neural Architectures: Foundations and Perspectives","created":"2024-12-30","cited_by_count":2,"area":null},{"id":"2205.02329","title":"Second-Order Sensitivity Analysis for Bilevel Optimization","created":"2022-05-04","cited_by_count":1,"area":null},{"id":"2404.10311","title":"Learning and Optimization for Price-based Demand Response of Electric Vehicle Charging","created":"2024-04-16","cited_by_count":1,"area":null},{"id":"2601.16510","title":"Learning to Optimize by Differentiable Programming","created":"2026-01-23","cited_by_count":1,"area":null},{"id":"2406.02348","title":"AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2605.11526","title":"Efficient and provably convergent end-to-end training of deep neural networks with linear constraints","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2609.33828","title":"dOPT: Differentiating Conic Optimization via Geometric Reduction","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}