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Found 15 papers, 4 papers with code
Multimodal fusion via cortical network inspired losses
Information integration from different modalities is an active area of research.
Neural Dependency Coding inspired Multimodal Fusion
Information integration from different modalities is an active area of research.
Adversarial Stein Training for Graph Energy Models
Learning distributions over graph-structured data is a challenging task with many applications in biology and chemistry.
Sibling Regression for Generalized Linear Models
Field observations form the basis of many scientific studies, especially in ecological and social sciences.
High-Confidence Off-Policy (or Counterfactual) Variance Estimation
Many sequential decision-making systems leverage data collected using prior policies to propose a new policy.
Three-quarter Sibling Regression for Denoising Observational Data
However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes.
Bosonic Random Walk Networks for Graph Learning
The development of Graph Neural Networks (GNNs) has led to great progress in machine learning on graph-structured data.
Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint
It is believed that by processing augmented inputs in tandem with the original ones, the model learns a more robust set of features which are shared between the original and augmented counterparts.
Optimizing for the Future in Non-Stationary MDPs
Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process is stationary.
Differential Equation Units: Learning Functional Forms of Activation Functions from Data
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure.
Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure.
Posterior Attention Models for Sequence to Sequence Learning
Modern neural architectures critically rely on attention for mapping structured inputs to sequences.
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning
In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning.
Generalizing Across Domains via Cross-Gradient Training
We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains.
Ranked #58 on
Domain Generalization
on PACS
Labeled Memory Networks for Online Model Adaptation
In this paper, we establish their potential in online adapting a batch trained neural network to domain-relevant labeled data at deployment time.
