{"total":47,"rows":[{"id":"2212.04458","title":"General-Purpose In-Context Learning by Meta-Learning Transformers","created":"2022-12-08","cited_by_count":105,"area":null},{"id":"2207.09640","title":"Test-Time Adaptation via Conjugate Pseudo-labels","created":"2022-07-20","cited_by_count":98,"area":null},{"id":"2201.03916","title":"Automated Reinforcement Learning (AutoRL): A Survey and Open Problems","created":"2022-01-11","cited_by_count":70,"area":null},{"id":"2306.01324","title":"Hyperparameters in Reinforcement Learning and How To Tune Them","created":"2023-06-02","cited_by_count":59,"area":null},{"id":"2107.05544","title":"Meta-learning PINN loss functions","created":"2021-07-12","cited_by_count":51,"area":null},{"id":"2101.03958","title":"Evolving Reinforcement Learning Algorithms","created":"2021-01-08","cited_by_count":49,"area":null},{"id":"2110.03909","title":"Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning","created":"2021-10-08","cited_by_count":49,"area":null},{"id":"2410.23208","title":"Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks","created":"2024-10-30","cited_by_count":45,"area":null},{"id":"2210.06640","title":"Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities","created":"2022-10-13","cited_by_count":38,"area":null},{"id":"2104.01876","title":"MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition","created":"2021-04-05","cited_by_count":27,"area":null},{"id":"2109.10781","title":"Introducing Symmetries to Black Box Meta Reinforcement Learning","created":"2021-09-22","cited_by_count":25,"area":null},{"id":"2107.08170","title":"Megaverse: Simulating Embodied Agents at One Million Experiences per Second","created":"2021-07-17","cited_by_count":20,"area":null},{"id":"2110.03375","title":"Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning","created":"2021-10-07","cited_by_count":18,"area":null},{"id":"2311.18044","title":"Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges","created":"2023-11-29","cited_by_count":18,"area":null},{"id":"2310.02782","title":"Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design","created":"2023-10-04","cited_by_count":16,"area":null},{"id":"2310.07015","title":"Neural Relational Inference with Fast Modular Meta-learning","created":"2023-10-10","cited_by_count":16,"area":null},{"id":"2310.19007","title":"Behavior Alignment via Reward Function Optimization","created":"2023-10-29","cited_by_count":16,"area":null},{"id":"2112.04467","title":"CoMPS: Continual Meta Policy Search","created":"2021-12-08","cited_by_count":15,"area":null},{"id":"2103.00243","title":"Searching for Robustness: Loss Learning for Noisy Classification Tasks","created":"2021-02-27","cited_by_count":13,"area":null},{"id":"2112.15400","title":"A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning","created":"2021-12-31","cited_by_count":12,"area":null},{"id":"2302.01470","title":"Learning to Optimize for Reinforcement Learning","created":"2023-02-03","cited_by_count":12,"area":null},{"id":"2209.08907","title":"Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning","created":"2022-09-19","cited_by_count":9,"area":null},{"id":"2111.00206","title":"One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning","created":"2021-10-30","cited_by_count":5,"area":null},{"id":"2301.13247","title":"Meta-Learning Adaptive Loss Functions","created":"2023-01-30","cited_by_count":4,"area":null},{"id":"2302.03235","title":"Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs","created":"2023-02-07","cited_by_count":4,"area":null},{"id":"2502.03047","title":"Kozax: Flexible and Scalable Genetic Programming in JAX","created":"2025-02-05","cited_by_count":4,"area":null},{"id":"2205.01006","title":"Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding","created":"2022-05-02","cited_by_count":3,"area":null},{"id":"2401.10220","title":"AutoFT: Learning an Objective for Robust Fine-Tuning","created":"2024-01-18","cited_by_count":3,"area":null},{"id":"2106.02745","title":"Neural Auto-Curricula","created":"2021-06-04","cited_by_count":2,"area":null},{"id":"2307.15071","title":"Writer adaptation for offline text recognition: An exploration of neural network-based methods","created":"2023-07-11","cited_by_count":2,"area":null},{"id":"2403.08793","title":"Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks","created":"2024-01-30","cited_by_count":2,"area":null},{"id":"2102.13147","title":"Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning","created":"2021-02-25","cited_by_count":1,"area":null},{"id":"2204.04292","title":"Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability","created":"2022-04-08","cited_by_count":1,"area":null},{"id":"2403.00865","title":"Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning","created":"2024-03-01","cited_by_count":1,"area":null},{"id":"2406.07983","title":"Meta-Learning Neural Procedural Biases","created":"2024-06-12","cited_by_count":1,"area":null},{"id":"2409.00908","title":"EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification","created":"2024-09-02","cited_by_count":1,"area":null},{"id":"2111.10583","title":"Generating meta-learning tasks to evolve parametric loss for classification learning","created":"2021-11-20","cited_by_count":0,"area":null},{"id":"2203.04540","title":"MetaCon: Unified Predictive Segments System with Trillion Concept Meta-Learning","created":"2022-03-09","cited_by_count":0,"area":null},{"id":"2203.07029","title":"SuperCone: Unified User Segmentation over Heterogeneous Experts via Concept Meta-learning","created":"2022-03-09","cited_by_count":0,"area":null},{"id":"2204.02210","title":"Model Based Meta Learning of Critics for Policy Gradients","created":"2022-04-05","cited_by_count":0,"area":null},{"id":"2210.07098","title":"Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations","created":"2022-10-13","cited_by_count":0,"area":null},{"id":"2211.11649","title":"Implicit Training of Energy Model for Structure Prediction","created":"2022-11-21","cited_by_count":0,"area":null},{"id":"2306.10587","title":"Acceleration in Policy Optimization","created":"2023-06-18","cited_by_count":0,"area":null},{"id":"2404.06633","title":"Evolving Loss Functions for Specific Image Augmentation Techniques","created":"2024-04-09","cited_by_count":0,"area":null},{"id":"2501.12115","title":"Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks through Meta-learning","created":"2025-01-21","cited_by_count":0,"area":null},{"id":"2512.15948","title":"Subjective functions","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2607.27953","title":"AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization","created":"2026-07-30","cited_by_count":0,"area":null}],"next_cursor":null}