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Found 9 papers, 4 papers with code
Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts
Ensemble forecasts from numerical weather prediction models show systematic errors that require correction via post-processing.
Grappa -- A Machine Learned Molecular Mechanics Force Field
Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient.
Connectivity Optimized Nested Graph Networks for Crystal Structures
Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry.
HyperInvariances: Amortizing Invariance Learning
Providing invariances in a given learning task conveys a key inductive bias that can lead to sample-efficient learning and good generalisation, if correctly specified.
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
To this end, we explore few-shot learning from the perspective of neural network architecture, as well as a three stage pipeline of network updates under different data supplies, where unsupervised external data is considered for pre-training, base categories are used to simulate few-shot tasks for meta-training, and the scarcely labelled data of an novel task is taken for fine-tuning.
Ranked #2 on
Few-Shot Image Classification
on Meta-Dataset
HoloLens 2 Research Mode as a Tool for Computer Vision Research
Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Second, we demonstrate that the proposed prior encourages a disentangled latent representation which facilitates learning of disentangled representations.
ISA-VAE: Independent Subspace Analysis with Variational Autoencoders
Extensive quantitative and qualitative experiments demonstrate that the proposed prior mitigates the trade-off introduced by modified cost functions like beta-VAE and TCVAE between reconstruction loss and disentanglement.
Variational Inference for Data-Efficient Model Learning in POMDPs
In this paper we propose DELIP, an approach to model learning for POMDPs that utilizes amortized structured variational inference.
