{"list":"other","total":71,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"2403.03206","title":"Scaling Rectified Flow Transformers for High-Resolution Image Synthesis","created":"2024-03-05","cited_by_count":4181},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"2209.03003","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","created":"2022-09-07","cited_by_count":3700},{"id":"1502.01852","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","created":"2015-02-06","cited_by_count":3611},{"id":"1803.03635","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","created":"2018-03-09","cited_by_count":2262},{"id":"2006.09661","title":"Implicit Neural Representations with Periodic Activation Functions","created":"2020-06-17","cited_by_count":2238},{"id":"1902.10811","title":"Do ImageNet Classifiers Generalize to ImageNet?","created":"2019-02-13","cited_by_count":1515},{"id":"1609.09106","title":"HyperNetworks","created":"2016-09-27","cited_by_count":1027},{"id":"2209.15571","title":"Building Normalizing Flows with Stochastic Interpolants","created":"2022-09-30","cited_by_count":995},{"id":"2302.00482","title":"Improving and generalizing flow-based generative models with minibatch optimal transport","created":"2023-02-01","cited_by_count":879},{"id":"1909.04630","title":"Meta-Learning with Implicit Gradients","created":"2019-09-10","cited_by_count":525},{"id":"2209.04836","title":"Git Re-Basin: Merging Models modulo Permutation Symmetries","created":"2022-09-11","cited_by_count":471},{"id":"1806.03962","title":"Rotation Equivariant CNNs for Digital Pathology","created":"2018-06-08","cited_by_count":422},{"id":"2309.06380","title":"InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation","created":"2023-09-12","cited_by_count":395},{"id":"2407.15595","title":"Discrete Flow Matching","created":"2024-07-22","cited_by_count":317},{"id":"1810.09502","title":"How to train your MAML","created":"2018-10-22","cited_by_count":309},{"id":"2402.04997","title":"Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design","created":"2024-02-07","cited_by_count":305},{"id":"2304.14772","title":"Multisample Flow Matching: Straightening Flows with Minibatch Couplings","created":"2023-04-28","cited_by_count":278},{"id":"2409.08861","title":"Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control","created":"2024-09-13","cited_by_count":233},{"id":"1306.0543","title":"Predicting Parameters in Deep Learning","created":"2013-06-03","cited_by_count":224},{"id":"2308.06767","title":"A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations","created":"2023-08-13","cited_by_count":202},{"id":"2002.04461","title":"TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics","created":"2020-02-09","cited_by_count":160},{"id":"2306.14892","title":"Supervised Pretraining Can Learn In-Context Reinforcement Learning","created":"2023-06-26","cited_by_count":139},{"id":"2002.09116","title":"Learning Deep Kernels for Non-Parametric Two-Sample Tests","created":"2020-02-21","cited_by_count":130},{"id":"2204.07305","title":"Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference","created":"2022-04-15","cited_by_count":126},{"id":"2212.04458","title":"General-Purpose In-Context Learning by Meta-Learning Transformers","created":"2022-12-08","cited_by_count":105},{"id":"2002.10553","title":"Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks","created":"2020-02-24","cited_by_count":98},{"id":"2210.06662","title":"Action Matching: Learning Stochastic Dynamics from Samples","created":"2022-10-13","cited_by_count":98},{"id":"2106.06345","title":"Proximal Optimal Transport Modeling of Population Dynamics","created":"2021-06-11","cited_by_count":93},{"id":"2110.13100","title":"Parameter Prediction for Unseen Deep Architectures","created":"2021-10-25","cited_by_count":89},{"id":"2405.14780","title":"Metric Flow Matching for Smooth Interpolations on the Data Manifold","created":"2024-05-23","cited_by_count":77},{"id":"2102.12470","title":"On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)","created":"2021-02-24","cited_by_count":73},{"id":"2302.14040","title":"Permutation Equivariant Neural Functionals","created":"2023-02-27","cited_by_count":70},{"id":"2403.12143","title":"Graph Neural Networks for Learning Equivariant Representations of Neural Networks","created":"2024-03-18","cited_by_count":59},{"id":"2412.03487","title":"Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective","created":"2024-12-04","cited_by_count":57},{"id":"2201.04182","title":"HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning","created":"2022-01-11","cited_by_count":55},{"id":"2310.10649","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows","created":"2023-10-16","cited_by_count":46},{"id":"2007.05145","title":"Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples","created":"2020-07-10","cited_by_count":44},{"id":"2210.11348","title":"Hypernetworks in Meta-Reinforcement Learning","created":"2022-10-20","cited_by_count":42},{"id":"2303.01751","title":"Deep Momentum Multi-Marginal Schr\\\"odinger Bridge","created":"2023-03-03","cited_by_count":42},{"id":"2306.06626","title":"On Kinetic Optimal Probability Paths for Generative Models","created":"2023-06-11","cited_by_count":42},{"id":"2411.03688","title":"Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey","created":"2024-11-06","cited_by_count":42},{"id":"2406.09997","title":"Towards Scalable and Versatile Weight Space Learning","created":"2024-06-14","cited_by_count":41},{"id":"2307.16424","title":"MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning","created":"2023-07-31","cited_by_count":40},{"id":"2310.02233","title":"Generalized Schr\\\"odinger Bridge Matching","created":"2023-10-03","cited_by_count":40},{"id":"2209.14764","title":"Model Zoos: A Dataset of Diverse Populations of Neural Network Models","created":"2022-09-29","cited_by_count":39},{"id":"2402.18153","title":"Diffusion-Based Neural Network Weights Generation","created":"2024-02-28","cited_by_count":39},{"id":"2412.17762","title":"The Superposition of Diffusion Models Using the It\\^o Density Estimator","created":"2024-12-23","cited_by_count":36},{"id":"2303.04143","title":"Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?","created":"2023-03-07","cited_by_count":29},{"id":"2408.06277","title":"Multi-marginal Schr\\\"odinger Bridges with Iterative Reference Refinement","created":"2024-08-12","cited_by_count":27},{"id":"2410.07974","title":"Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling","created":"2024-10-10","cited_by_count":27},{"id":"2403.04861","title":"A Survey of Lottery Ticket Hypothesis","created":"2024-03-07","cited_by_count":24},{"id":"2406.12616","title":"Learning diffusion at lightspeed","created":"2024-06-18","cited_by_count":22},{"id":"2410.04207","title":"Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models","created":"2024-10-05","cited_by_count":22},{"id":"2310.10971","title":"Context-Aware Meta-Learning","created":"2023-10-17","cited_by_count":21},{"id":"2212.02742","title":"A Learning Based Hypothesis Test for Harmful Covariate Shift","created":"2022-12-06","cited_by_count":19},{"id":"2205.15745","title":"HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks","created":"2022-05-31","cited_by_count":17},{"id":"2409.11697","title":"Monomial Matrix Group Equivariant Neural Functional Networks","created":"2024-09-18","cited_by_count":17},{"id":"2306.14770","title":"ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion","created":"2023-06-26","cited_by_count":16},{"id":"2406.00288","title":"Neural Optimal Transport with Lagrangian Costs","created":"2024-06-01","cited_by_count":16},{"id":"2501.11587","title":"Recurrent Diffusion for Large-Scale Parameter Generation","created":"2025-01-20","cited_by_count":15},{"id":"2506.07998","title":"Generative Modeling of Weights: Generalization or Memorization?","created":"2025-06-09","cited_by_count":14},{"id":"2406.10676","title":"Variational Analysis in the Wasserstein Space","created":"2024-06-15","cited_by_count":6},{"id":"2504.18072","title":"A Model Zoo on Phase Transitions in Neural Networks","created":"2025-04-25","cited_by_count":3}],"next_cursor":null}