![]() We empirically demonstrate the competitive performance of RCNPs on a large array of tasks naturally containing equivariances. Our proposed method extends the applicability and impact of equivariant neural processes to higher dimensions. In this work, we propose Relational Conditional Neural Processes (RCNPs), an effective approach to incorporate equivariances into any neural process model. More than 100 million people use GitHub to discover, fork, and contribute to over 330 million projects. However, prior attempts to include equivariances in CNPs do not scale effectively beyond two input dimensions. Replication of the 'Conditional Neural Processes' (2018) and 'Neural Processes' (2018) papers by Garnelo et al. Many relevant machine learning tasks, such as spatio-temporal modeling, Bayesian Optimization and continuous control, contain equivariances - for example to translation - which the model can exploit for maximal performance. We demonstrate that this approach outperforms both state-of-the-art gradient-based meta-learning approaches and hypernetwork approaches.Download a PDF of the paper titled Practical Equivariances via Relational Conditional Neural Processes, by Daolang Huang and 7 other authors Download PDF Abstract:Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification. Although synapse formation is an activity-independent event, modification of synapses and synapse elimination requires neural activity. Once axons reach their target areas, activity-dependent mechanisms come into play. CNPs make accurate predictions after observing only a handful of training. These processes are thought of as being independent of neural activity and sensory experience. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. We instead propose a new paradigm that views the large-scale training of neural representations as a part of a partially-observed neural process framework, and leverage neural process algorithms to solve this task. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. We introduce a class of neural latent variable models which we call Neural Processes (NPs), combining the best of both worlds. Existing generalization methods view this as a meta-learning problem and employ gradient-based meta-learning to learn an initialization which is then fine-tuned with test-time optimization, or learn hypernetworks to produce the weights of a neural field. However, given a dataset of signals that we would like to represent, having to optimize a separate neural field for each signal is inefficient, and cannot capitalize on shared information or structures among signals. SDE-Net is either dominated by its drift net with in-distribution (ID) data to achieve good predictive accuracy, or dominated by its diffusion net with out-of-distribution (OOD) data to generate high diffusion for. We empirically show that NDPs are able to. Using a novel attention block we are able to incorporate properties of stochastic processes, such as exchangeability, directly into the NDP's architecture. Compared to discrete representations, neural representations both scale well with increasing resolution, are continuous, and can be many-times differentiable. Existing neural stochastic differential equation models, such as SDE-Net, can quantify the uncertainties of deep neural networks (DNNs) from a dynamical system perspective. We propose Neural Diffusion Processes (NDPs), a novel approach based upon diffusion models, that learns to sample from distributions over functions. ![]() ![]() Neural fields, which represent signals as a function parameterized by a neural network, are a promising alternative to traditional discrete vector or grid-based representations.
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