Geometric neural diffusion processes
Denoising diffusion models have proven to be a flexible and effective paradigm for
generative modelling. Their recent extension to infinite dimensional Euclidean spaces has …
generative modelling. Their recent extension to infinite dimensional Euclidean spaces has …
NPCL: Neural processes for uncertainty-aware continual learning
Continual learning (CL) aims to train deep neural networks efficiently on streaming data
while limiting the forgetting caused by new tasks. However, learning transferable knowledge …
while limiting the forgetting caused by new tasks. However, learning transferable knowledge …
On permutation-invariant neural networks
Conventional machine learning algorithms have traditionally been designed under the
assumption that input data follows a vector-based format, with an emphasis on vector-centric …
assumption that input data follows a vector-based format, with an emphasis on vector-centric …
Latent bottlenecked attentive neural processes
Neural Processes (NPs) are popular methods in meta-learning that can estimate predictive
uncertainty on target datapoints by conditioning on a context dataset. Previous state-of-the …
uncertainty on target datapoints by conditioning on a context dataset. Previous state-of-the …
Idnp: Interest dynamics modeling using generative neural processes for sequential recommendation
Recent sequential recommendation models rely increasingly on consecutive short-term user-
item interaction sequences to model user interests. These approaches have, however …
item interaction sequences to model user interests. These approaches have, however …
Spectral diffusion processes
Score-based generative modelling (SGM) has proven to be a very effective method for
modelling densities on finite-dimensional spaces. In this work we propose to extend this …
modelling densities on finite-dimensional spaces. In this work we propose to extend this …
[HTML][HTML] A Memory-augmented Conditional Neural Process model for traffic prediction
This paper presents the first neural process-based model for traffic prediction, the Memory-
augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction …
augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction …
Disentangled multi-fidelity deep bayesian active learning
To balance quality and cost, various domain areas of science and engineering run
simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a …
simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a …
Spatial multi-attention conditional neural processes
LL Bao, JS Zhang, CX Zhang - Neural Networks, 2024 - Elsevier
Spatial prediction tasks are challenging when observed samples are sparse and prediction
samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …
samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …
Versatile neural processes for learning implicit neural representations
Representing a signal as a continuous function parameterized by neural network (aka
Implicit Neural Representations, INRs) has attracted increasing attention in recent years …
Implicit Neural Representations, INRs) has attracted increasing attention in recent years …