{"total":93,"rows":[{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386,"area":"mllm"},{"id":"2107.13586","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","created":"2021-07-28","cited_by_count":2234,"area":null},{"id":"2102.04906","title":"Dynamic Neural Networks: A Survey","created":"2021-02-09","cited_by_count":408,"area":null},{"id":"2206.13499","title":"Prompting Decision Transformer for Few-Shot Policy Generalization","created":"2022-06-27","cited_by_count":146,"area":"offlinerl"},{"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,"area":null},{"id":"2110.13100","title":"Parameter Prediction for Unseen Deep Architectures","created":"2021-10-25","cited_by_count":89,"area":null},{"id":"2302.11529","title":"Modular Deep Learning","created":"2023-02-22","cited_by_count":83,"area":null},{"id":"2107.00358","title":"Cross-domain Few-shot Learning with Task-specific Adapters","created":"2021-07-01","cited_by_count":80,"area":null},{"id":"2209.14733","title":"Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights","created":"2022-09-29","cited_by_count":64,"area":"distill"},{"id":"2103.13582","title":"Learning Dynamic Alignment via Meta-filter for Few-shot Learning","created":"2021-03-25","cited_by_count":41,"area":null},{"id":"2304.02777","title":"MoStGAN-V: Video Generation with Temporal Motion Styles","created":"2023-04-05","cited_by_count":37,"area":null},{"id":"2202.03857","title":"Learning Optical Flow with Adaptive Graph Reasoning","created":"2022-02-08","cited_by_count":33,"area":null},{"id":"2305.17940","title":"Learning Conditional Attributes for Compositional Zero-Shot Learning","created":"2023-05-29","cited_by_count":28,"area":null},{"id":"2303.07502","title":"Meta-learning approaches for few-shot learning: A survey of recent advances","created":"2023-03-13","cited_by_count":25,"area":null},{"id":"2203.13903","title":"Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection","created":"2022-03-25","cited_by_count":23,"area":null},{"id":"2208.08349","title":"Open Long-Tailed Recognition in a Dynamic World","created":"2022-08-17","cited_by_count":21,"area":null},{"id":"2310.18608","title":"Embedding in Recommender Systems: A Survey","created":"2023-10-28","cited_by_count":20,"area":null},{"id":"2501.06773","title":"Pareto Set Learning for Multi-Objective Reinforcement Learning","created":"2025-01-12","cited_by_count":20,"area":"deeprl"},{"id":"2304.04188","title":"HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation","created":"2023-04-09","cited_by_count":19,"area":"distill"},{"id":"2312.08598","title":"MotherNet: Fast Training and Inference via Hyper-Network Transformers","created":"2023-12-14","cited_by_count":19,"area":null},{"id":"2201.06814","title":"Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling","created":"2022-01-18","cited_by_count":18,"area":null},{"id":"2405.10800","title":"Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting","created":"2024-05-17","cited_by_count":18,"area":null},{"id":"2107.00197","title":"Few-Shot Learning with a Strong Teacher","created":"2021-07-01","cited_by_count":16,"area":null},{"id":"2303.08557","title":"Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey","created":"2023-03-15","cited_by_count":14,"area":null},{"id":"2201.09874","title":"CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving","created":"2022-01-24","cited_by_count":13,"area":null},{"id":"2207.02088","title":"SiamMask: A Framework for Fast Online Object Tracking and Segmentation","created":"2022-07-05","cited_by_count":13,"area":null},{"id":"2305.14952","title":"Focus Your Attention (with Adaptive IIR Filters)","created":"2023-05-24","cited_by_count":12,"area":null},{"id":"2311.05241","title":"When Meta-Learning Meets Online and Continual Learning: A Survey","created":"2023-11-09","cited_by_count":12,"area":null},{"id":"2205.04281","title":"Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis","created":"2022-05-09","cited_by_count":11,"area":null},{"id":"2304.06914","title":"SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders","created":"2023-04-14","cited_by_count":11,"area":null},{"id":"2111.00201","title":"A Comparative Review of Recent Few-Shot Object Detection Algorithms","created":"2021-10-30","cited_by_count":10,"area":null},{"id":"2306.16780","title":"Graph Sampling-based Meta-Learning for Molecular Property Prediction","created":"2023-06-29","cited_by_count":10,"area":null},{"id":"2202.10808","title":"Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks","created":"2022-02-22","cited_by_count":9,"area":null},{"id":"2210.00471","title":"OCD: Learning to Overfit with Conditional Diffusion Models","created":"2022-10-02","cited_by_count":9,"area":"diffusion"},{"id":"2203.14205","title":"Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison","created":"2022-03-27","cited_by_count":8,"area":null},{"id":"2110.02529","title":"On the Importance of Firth Bias Reduction in Few-Shot Classification","created":"2021-10-06","cited_by_count":7,"area":null},{"id":"2110.02900","title":"Meta Internal Learning","created":"2021-10-06","cited_by_count":7,"area":null},{"id":"2112.02763","title":"MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples","created":"2021-12-06","cited_by_count":6,"area":null},{"id":"2206.06460","title":"MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning","created":"2022-06-13","cited_by_count":6,"area":null},{"id":"2211.00868","title":"tSF: Transformer-based Semantic Filter for Few-Shot Learning","created":"2022-11-02","cited_by_count":6,"area":null},{"id":"2407.10964","title":"No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations","created":"2024-07-15","cited_by_count":5,"area":null},{"id":"2510.08962","title":"Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation","created":"2025-10-10","cited_by_count":5,"area":null},{"id":"2112.02219","title":"Transferring Unconditional to Conditional GANs with Hyper-Modulation","created":"2021-12-04","cited_by_count":4,"area":null},{"id":"2112.03494","title":"Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning","created":"2021-12-07","cited_by_count":4,"area":null},{"id":"2303.09281","title":"SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning","created":"2023-03-15","cited_by_count":4,"area":null},{"id":"2305.08092","title":"Meta-DM: Applications of Diffusion Models on Few-Shot Learning","created":"2023-05-14","cited_by_count":4,"area":"diffusion"},{"id":"2410.14040","title":"WARPD: World model Assisted Reactive Policy Diffusion","created":"2024-10-17","cited_by_count":4,"area":"wm"},{"id":"2501.09705","title":"Practical Continual Forgetting for Pre-trained Vision Models","created":"2025-01-16","cited_by_count":4,"area":null},{"id":"2103.05107","title":"Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data","created":"2021-02-21","cited_by_count":3,"area":null},{"id":"2104.05860","title":"Contextual HyperNetworks for Novel Feature Adaptation","created":"2021-04-12","cited_by_count":3,"area":null},{"id":"2112.09796","title":"AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data","created":"2021-12-17","cited_by_count":3,"area":null},{"id":"2202.01940","title":"Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks","created":"2022-02-04","cited_by_count":3,"area":null},{"id":"2204.05432","title":"A Simple Approach to Adversarial Robustness in Few-shot Image Classification","created":"2022-04-11","cited_by_count":3,"area":null},{"id":"2206.05194","title":"Learning the Space of Deep Models","created":"2022-06-10","cited_by_count":3,"area":null},{"id":"2208.04600","title":"IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation","created":"2022-08-09","cited_by_count":3,"area":null},{"id":"2406.04779","title":"Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network","created":"2024-06-07","cited_by_count":3,"area":null},{"id":"2408.03208","title":"Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery","created":"2024-08-06","cited_by_count":3,"area":null},{"id":"2412.16777","title":"HyperCLIP: Adapting Vision-Language models with Hypernetworks","created":"2024-12-21","cited_by_count":3,"area":"mllm"},{"id":"2605.31145","title":"FOCUS: Forcing In-Context Object Localization through Visual Support Constraints and Policy Optimization","created":"2026-05-29","cited_by_count":3,"area":null},{"id":"2104.07255","title":"Embedding Adaptation is Still Needed for Few-Shot Learning","created":"2021-04-15","cited_by_count":2,"area":null},{"id":"2104.15049","title":"Updatable Siamese Tracker with Two-stage One-shot Learning","created":"2021-04-30","cited_by_count":2,"area":null},{"id":"2109.07018","title":"Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations","created":"2021-09-14","cited_by_count":2,"area":null},{"id":"2210.01973","title":"Meta-Ensemble Parameter Learning","created":"2022-10-05","cited_by_count":2,"area":"distill"},{"id":"2312.15086","title":"HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings","created":"2023-12-22","cited_by_count":2,"area":null},{"id":"2510.04331","title":"DoRAN: Stabilizing Weight-Decomposed Low-Rank Adaptation via Noise Injection and Auxiliary Networks","created":"2025-10-05","cited_by_count":2,"area":null},{"id":"2510.19105","title":"MetaCluster: Enabling Deep Compression of Kolmogorov-Arnold Network","created":"2025-10-21","cited_by_count":2,"area":null},{"id":"2606.24932","title":"Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation","created":"2026-06-22","cited_by_count":2,"area":null},{"id":"2112.07954","title":"Object Pursuit: Building a Space of Objects via Discriminative Weight Generation","created":"2021-12-15","cited_by_count":1,"area":null},{"id":"2201.02052","title":"A Unified Framework for Attention-Based Few-Shot Object Detection","created":"2022-01-06","cited_by_count":1,"area":null},{"id":"2203.16662","title":"Overcoming challenges in leveraging GANs for few-shot data augmentation","created":"2022-03-30","cited_by_count":1,"area":null},{"id":"2207.14443","title":"A Survey of Learning on Small Data: Generalization, Optimization, and Challenge","created":"2022-07-29","cited_by_count":1,"area":null},{"id":"2210.12402","title":"DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms","created":"2022-10-22","cited_by_count":1,"area":null},{"id":"2210.13923","title":"A Comparative Attention Framework for Better Few-Shot Object Detection on Aerial Images","created":"2022-10-25","cited_by_count":1,"area":null},{"id":"2307.13485","title":"Cos R-CNN for Online Few-shot Object Detection","created":"2023-07-25","cited_by_count":1,"area":null},{"id":"1806.01531","title":"Deep Mixture of Experts via Shallow Embedding","created":"2018-06-05","cited_by_count":0,"area":"moe"},{"id":"2105.03838","title":"HyperHyperNetworks for the Design of Antenna Arrays","created":"2021-05-09","cited_by_count":0,"area":null},{"id":"2107.10504","title":"External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery","created":"2021-07-22","cited_by_count":0,"area":null},{"id":"2110.07053","title":"Robust MIMO Detection using Hypernetworks with Learned Regularizers","created":"2021-10-13","cited_by_count":0,"area":null},{"id":"2112.14016","title":"Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking","created":"2021-12-28","cited_by_count":0,"area":null},{"id":"2201.04851","title":"MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware Meta-learning","created":"2022-01-13","cited_by_count":0,"area":null},{"id":"2201.09202","title":"One-Shot Learning on Attributed Sequences","created":"2022-01-23","cited_by_count":0,"area":null},{"id":"2209.01332","title":"Class-Specific Channel Attention for Few-Shot Learning","created":"2022-09-03","cited_by_count":0,"area":null},{"id":"2305.17871","title":"propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans","created":"2023-05-29","cited_by_count":0,"area":null},{"id":"2401.15834","title":"Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes","created":"2024-01-29","cited_by_count":0,"area":null},{"id":"2405.05981","title":"Scalable physical source-to-field inference with hypernetworks","created":"2024-05-07","cited_by_count":0,"area":null},{"id":"2405.18751","title":"On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2408.08399","title":"Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains","created":"2024-08-15","cited_by_count":0,"area":null},{"id":"2410.05107","title":"Hyper-Representations: Learning from Populations of Neural Networks","created":"2024-10-07","cited_by_count":0,"area":null},{"id":"2501.14271","title":"TLXML: Task-Level Explanation of Meta-Learning via Influence Functions","created":"2025-01-24","cited_by_count":0,"area":null},{"id":"2505.11878","title":"AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery","created":"2025-05-17","cited_by_count":0,"area":null},{"id":"2609.32833","title":"Permutation-Equivariant Flow Matching for Alignment-Free Neural Weight Generation","created":"2026-09-26","cited_by_count":0,"area":"diffusion"},{"id":"2610.00820","title":"On-the-fly Weight Generation: A Hypernetwork Proof of Concept on ARC-1D","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.11117","title":"MCL: Meta Convolution Layer","created":"2026-10-08","cited_by_count":0,"area":null}],"next_cursor":null}