James Requeima

According to our database1, James Requeima authored at least 15 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
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PhD thesis 
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Links

Online presence:

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Bibliography

2023
Diffusion-Augmented Neural Processes.
CoRR, 2023

Sim2Real for Environmental Neural Processes.
CoRR, 2023

Autoregressive Conditional Neural Processes.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement.
CoRR, 2022

Challenges and Pitfalls of Bayesian Unlearning.
CoRR, 2022

Practical Conditional Neural Processes Via Tractable Dependent Predictions.
CoRR, 2022

Practical Conditional Neural Process Via Tractable Dependent Predictions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Efficient Gaussian Neural Processes for Regression.
CoRR, 2021

The Gaussian Neural Process.
CoRR, 2021

2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

TaskNorm: Rethinking Batch Normalization for Meta-Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Convolutional Conditional Neural Processes.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Gaussian Process Autoregressive Regression Model (GPAR).
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2017
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space.
Proceedings of the 34th International Conference on Machine Learning, 2017


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