Statistical Learning
Convex optimization based on global lower second-order models
The next point in the iteration process is defined as the minimizer of this model.
Convex optimization based on global lower second-order models
The next point in the iteration process is defined as the minimizer of this model.
Scalarization for Multi-Task and Multi-Domain Learning at Scale Amรฉlie Royer, Tijmen Blankevoort, Babak Ehteshami Bejnordi Qualcomm AI Research Amsterdam, The Netherlands
It also enables potential positive knowledge transfer across tasks/domains, leading to improved accuracy and data-efficient training.
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search.
Behavior From the Void: Unsupervised Active Pre-Training
We empirically evaluate APT by exposing task-specific reward after a long unsupervised pre-training phase.
Supplement to Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding 1 Additional Algorithm Details 1.1 Details of the Transformation Function
The support nodes are either positive or negative. For the transformation function, we stack multiple computation blocks as shown in Figure 1. The stacking mechanism helps the function capture comprehensive relationships between nodes such that the performance is boosted. In each computation block, there are mainly two modules. The detailed architecture of the self-attention module is illustrated in Figure 1.
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding Lin Lan
Additionally, we introduce an embedding transformation function that remedies the deficiency of the straightforward use of meta-learning.
Gaussian Gated Linear Networks David Budden Adam H. Marblestone Eren Sezener T or Lattimore Greg Wayne Joel V eness
We propose the Gaussian Gated Linear Network (G-GLN), an extension to the recently proposed GLN family of deep neural networks.
Relative Flatness and Generalization
Flatness of the loss curve is conjectured to be connected to the generalization ability of machine learning models, in particular neural networks.