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Found 8 papers, 6 papers with code
Causal normalizing flows: from theory to practice
In this work, we deepen on the use of normalizing flows for causal reasoning.
Learnable Graph Convolutional Attention Networks
Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features.
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization
We refer to this limitation as modality collapse.
RotoGrad: Gradient Homogenization in Multi-Task Learning
Multi-task learning is being increasingly adopted in applications domains like computer vision and reinforcement learning.
RotoGrad: Gradient Homogenization in Multitask Learning
Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning.
Rotograd: Dynamic Gradient Homogenization for Multitask Learning
GradNorm eases the fitting of all individual tasks by dynamically equalizing the contribution of each task to the overall gradient magnitude.
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.
Lipschitz standardization for multivariate learning
While MTL solutions do not directly apply in the probabilistic setting (as they cannot handle the likelihood constraints) we show that similar ideas may be leveraged during data preprocessing.
