{"total":283,"rows":[{"id":"2208.01066","title":"What Can Transformers Learn In-Context? A Case Study of Simple Function Classes","created":"2022-08-01","cited_by_count":715,"area":null},{"id":"2112.10510","title":"Transformers Can Do Bayesian Inference","created":"2021-12-20","cited_by_count":360,"area":null},{"id":"2201.12204","title":"From data to functa: Your data point is a function and you can treat it like one","created":"2022-01-28","cited_by_count":187,"area":null},{"id":"2207.04179","title":"Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling","created":"2022-07-09","cited_by_count":127,"area":null},{"id":"2106.02584","title":"Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning","created":"2021-06-04","cited_by_count":113,"area":null},{"id":"2306.11719","title":"Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision","created":"2023-06-20","cited_by_count":112,"area":"diffusion"},{"id":"2206.07275","title":"CARD: Classification and Regression Diffusion Models","created":"2022-06-15","cited_by_count":103,"area":"diffusion"},{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"area":null},{"id":"2102.04776","title":"Generative Models as Distributions of Functions","created":"2021-02-09","cited_by_count":89,"area":null},{"id":"2107.06882","title":"Conservative Objective Models for Effective Offline Model-Based Optimization","created":"2021-07-14","cited_by_count":88,"area":null},{"id":"2302.07400","title":"Score-based Diffusion Models in Function Space","created":"2023-02-14","cited_by_count":82,"area":"diffusion"},{"id":"2106.11944","title":"MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images","created":"2021-06-22","cited_by_count":78,"area":null},{"id":"2307.14338","title":"TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023","created":"2023-07-26","cited_by_count":74,"area":null},{"id":"2209.14855","title":"Continuous PDE Dynamics Forecasting with Implicit Neural Representations","created":"2022-09-29","cited_by_count":73,"area":null},{"id":"2302.11002","title":"Learning Physical Models that Can Respect Conservation Laws","created":"2023-02-21","cited_by_count":59,"area":null},{"id":"2103.12413","title":"Neural ODE Processes","created":"2021-03-23","cited_by_count":57,"area":null},{"id":"2202.01889","title":"Generalizing to New Physical Systems via Context-Informed Dynamics Model","created":"2022-02-01","cited_by_count":50,"area":null},{"id":"2206.03992","title":"Neural Diffusion Processes","created":"2022-06-08","cited_by_count":50,"area":null},{"id":"2212.00886","title":"Diffusion Generative Models in Infinite Dimensions","created":"2022-12-01","cited_by_count":49,"area":"diffusion"},{"id":"2506.07918","title":"CausalPFN: Amortized Causal Effect Estimation via In-Context Learning","created":"2025-06-09","cited_by_count":49,"area":null},{"id":"2211.02590","title":"Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion","created":"2022-11-04","cited_by_count":39,"area":null},{"id":"2102.07970","title":"Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation","created":"2021-02-16","cited_by_count":38,"area":null},{"id":"2106.01908","title":"You Never Cluster Alone","created":"2021-06-03","cited_by_count":38,"area":null},{"id":"2205.15463","title":"Few-Shot Diffusion Models","created":"2022-05-30","cited_by_count":38,"area":"diffusion"},{"id":"2412.01786","title":"Gradient-Free Generation for Hard-Constrained Systems","created":"2024-12-02","cited_by_count":38,"area":"diffusion"},{"id":"2504.16109","title":"Representation Learning for Tabular Data: A Comprehensive Survey","created":"2025-04-17","cited_by_count":36,"area":null},{"id":"2403.04317","title":"Online Adaptation of Language Models with a Memory of Amortized Contexts","created":"2024-03-07","cited_by_count":35,"area":null},{"id":"2410.05952","title":"Active Evaluation Acquisition for Efficient LLM Benchmarking","created":"2024-10-08","cited_by_count":35,"area":null},{"id":"2101.03606","title":"The Gaussian Neural Process","created":"2021-01-10","cited_by_count":34,"area":null},{"id":"2101.00531","title":"Context-Aware Safe Reinforcement Learning for Non-Stationary Environments","created":"2021-01-02","cited_by_count":33,"area":null},{"id":"2303.14468","title":"Autoregressive Conditional Neural Processes","created":"2023-03-25","cited_by_count":33,"area":null},{"id":"2305.15930","title":"End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes","created":"2023-05-25","cited_by_count":32,"area":null},{"id":"2402.17135","title":"Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings","created":"2024-02-27","cited_by_count":31,"area":"deeprl"},{"id":"2101.07950","title":"Convolutional conditional neural processes for local climate downscaling","created":"2021-01-20","cited_by_count":29,"area":null},{"id":"2109.01876","title":"Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting","created":"2021-09-04","cited_by_count":29,"area":null},{"id":"2110.11346","title":"Data-Driven Offline Optimization For Architecting Hardware Accelerators","created":"2021-10-20","cited_by_count":29,"area":null},{"id":"2303.00165","title":"Diffusion Probabilistic Fields","created":"2023-03-01","cited_by_count":28,"area":"diffusion"},{"id":"2302.05927","title":"Transfer Learning for Bayesian Optimization: A Survey","created":"2023-02-12","cited_by_count":27,"area":null},{"id":"2403.02683","title":"Learning to Defer to a Population: A Meta-Learning Approach","created":"2024-03-05","cited_by_count":27,"area":null},{"id":"2102.03340","title":"NRTSI: Non-Recurrent Time Series Imputation","created":"2021-02-05","cited_by_count":25,"area":null},{"id":"2102.12037","title":"Conditional Image Generation by Conditioning Variational Auto-Encoders","created":"2021-02-24","cited_by_count":25,"area":null},{"id":"2203.08775","title":"Practical Conditional Neural Processes Via Tractable Dependent Predictions","created":"2022-03-16","cited_by_count":25,"area":null},{"id":"2205.11404","title":"Variable-Input Deep Operator Networks","created":"2022-05-23","cited_by_count":25,"area":null},{"id":"2209.14125","title":"Spectral Diffusion Processes","created":"2022-09-28","cited_by_count":25,"area":"diffusion"},{"id":"2106.03542","title":"How Tight Can PAC-Bayes be in the Small Data Regime?","created":"2021-06-07","cited_by_count":24,"area":null},{"id":"2111.05326","title":"The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning","created":"2021-11-09","cited_by_count":23,"area":null},{"id":"2209.00517","title":"The Neural Process Family: Survey, Applications and Perspectives","created":"2022-09-01","cited_by_count":23,"area":null},{"id":"2410.15320","title":"Amortized Probabilistic Conditioning for Optimization, Simulation and Inference","created":"2024-10-20","cited_by_count":23,"area":null},{"id":"2302.10351","title":"Variational Autoencoding Neural Operators","created":"2023-02-20","cited_by_count":22,"area":null},{"id":"2310.19961","title":"ExPT: Synthetic Pretraining for Few-Shot Experimental Design","created":"2023-10-30","cited_by_count":22,"area":null},{"id":"2412.16577","title":"A Meta-Learning Approach to Bayesian Causal Discovery","created":"2024-12-21","cited_by_count":22,"area":null},{"id":"2106.13711","title":"Multimodal Emergent Fake News Detection via Meta Neural Process Networks","created":"2021-06-22","cited_by_count":21,"area":null},{"id":"2501.11039","title":"Model Predictive Task Sampling for Efficient and Robust Adaptation","created":"2025-01-19","cited_by_count":21,"area":null},{"id":"2406.07457","title":"Estimating the Hallucination Rate of Generative AI","created":"2024-06-11","cited_by_count":20,"area":null},{"id":"2304.08183","title":"Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion","created":"2023-04-17","cited_by_count":19,"area":null},{"id":"2305.18719","title":"Graph Neural Processes for Spatio-Temporal Extrapolation","created":"2023-05-30","cited_by_count":19,"area":null},{"id":"2410.02525","title":"Contextual Document Embeddings","created":"2024-10-03","cited_by_count":19,"area":null},{"id":"2203.04905","title":"What Matters For Meta-Learning Vision Regression Tasks?","created":"2022-03-09","cited_by_count":18,"area":null},{"id":"2102.08291","title":"Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models","created":"2021-02-16","cited_by_count":17,"area":null},{"id":"2301.12023","title":"Meta Temporal Point Processes","created":"2023-01-27","cited_by_count":17,"area":null},{"id":"2102.08759","title":"Group Equivariant Conditional Neural Processes","created":"2021-02-17","cited_by_count":16,"area":null},{"id":"2103.13372","title":"Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition","created":"2021-03-24","cited_by_count":16,"area":null},{"id":"2106.03904","title":"When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting","created":"2021-06-07","cited_by_count":16,"area":null},{"id":"2106.08630","title":"HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-Learning","created":"2021-06-16","cited_by_count":16,"area":null},{"id":"2204.05449","title":"Neural Processes with Stochastic Attention: Paying more attention to the context dataset","created":"2022-04-11","cited_by_count":16,"area":null},{"id":"2205.15795","title":"A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud","created":"2022-05-31","cited_by_count":16,"area":null},{"id":"2304.09431","title":"Martingale Posterior Neural Processes","created":"2023-04-19","cited_by_count":16,"area":null},{"id":"2111.05323","title":"Variational Multi-Task Learning with Gumbel-Softmax Priors","created":"2021-11-09","cited_by_count":15,"area":null},{"id":"2206.04872","title":"Multi-fidelity Hierarchical Neural Processes","created":"2022-06-10","cited_by_count":15,"area":null},{"id":"2403.10642","title":"Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs","created":"2024-03-15","cited_by_count":15,"area":null},{"id":"2502.06601","title":"Amortized In-Context Bayesian Posterior Estimation","created":"2025-02-10","cited_by_count":15,"area":null},{"id":"2310.19272","title":"NPCL: Neural Processes for Uncertainty-Aware Continual Learning","created":"2023-10-30","cited_by_count":14,"area":null},{"id":"2501.04126","title":"Stochastic Process Learning via Operator Flow Matching","created":"2025-01-07","cited_by_count":14,"area":"diffusion"},{"id":"2106.12135","title":"Deep Gaussian Processes: A Survey","created":"2021-06-21","cited_by_count":13,"area":null},{"id":"2211.08568","title":"Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs","created":"2022-11-15","cited_by_count":13,"area":null},{"id":"2306.10915","title":"Practical Equivariances via Relational Conditional Neural Processes","created":"2023-06-19","cited_by_count":13,"area":null},{"id":"2402.07594","title":"Zero-shot Imputation with Foundation Inference Models for Dynamical Systems","created":"2024-02-12","cited_by_count":13,"area":null},{"id":"2402.18846","title":"Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling","created":"2024-02-29","cited_by_count":13,"area":null},{"id":"2405.19162","title":"Does learning the right latent variables necessarily improve in-context learning?","created":"2024-05-29","cited_by_count":13,"area":null},{"id":"2406.12409","title":"Translation Equivariant Transformer Neural Processes","created":"2024-06-18","cited_by_count":13,"area":null},{"id":"2407.19523","title":"Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation","created":"2024-07-28","cited_by_count":13,"area":null},{"id":"2502.02463","title":"Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation","created":"2025-02-04","cited_by_count":13,"area":null},{"id":"2106.10052","title":"On Contrastive Representations of Stochastic Processes","created":"2021-06-18","cited_by_count":12,"area":null},{"id":"2104.02487","title":"Neural Process for Black-Box Model Optimization Under Bayesian Framework","created":"2021-04-03","cited_by_count":11,"area":null},{"id":"2203.03978","title":"Contrastive Conditional Neural Processes","created":"2022-03-08","cited_by_count":11,"area":null},{"id":"2211.07206","title":"Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice","created":"2022-11-14","cited_by_count":11,"area":null},{"id":"2301.08883","title":"Versatile Neural Processes for Learning Implicit Neural Representations","created":"2023-01-21","cited_by_count":11,"area":null},{"id":"2304.01518","title":"Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation","created":"2023-04-04","cited_by_count":11,"area":null},{"id":"2305.04392","title":"Disentangled Multi-Fidelity Deep Bayesian Active Learning","created":"2023-05-07","cited_by_count":11,"area":null},{"id":"2305.15586","title":"Manifold Diffusion Fields","created":"2023-05-24","cited_by_count":11,"area":"diffusion"},{"id":"2212.00131","title":"Evidential Conditional Neural Processes","created":"2022-11-30","cited_by_count":10,"area":null},{"id":"2307.05431","title":"Geometric Neural Diffusion Processes","created":"2023-07-11","cited_by_count":10,"area":"diffusion"},{"id":"2403.17410","title":"On permutation-invariant neural networks","created":"2024-03-26","cited_by_count":10,"area":null},{"id":"2505.21974","title":"BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL","created":"2025-05-28","cited_by_count":10,"area":null},{"id":"2110.13623","title":"Contrastive Neural Processes for Self-Supervised Learning","created":"2021-10-24","cited_by_count":9,"area":null},{"id":"2202.06557","title":"Reinforcement Learning in Presence of Discrete Markovian Context Evolution","created":"2022-02-14","cited_by_count":9,"area":null},{"id":"2203.12768","title":"Multidimensional Belief Quantification for Label-Efficient Meta-Learning","created":"2022-03-23","cited_by_count":9,"area":null},{"id":"2305.14567","title":"Memory Efficient Neural Processes via Constant Memory Attention Block","created":"2023-05-23","cited_by_count":9,"area":null},{"id":"2506.07003","title":"End-to-End Probabilistic Framework for Learning with Hard Constraints","created":"2025-06-08","cited_by_count":9,"area":null},{"id":"2106.01216","title":"Evidential Turing Processes","created":"2021-06-02","cited_by_count":8,"area":null}],"next_cursor":"100"}