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Found 7 papers, 3 papers with code
On the Statistical and Information Theoretical Characteristics of DNN Representations
It has been common to argue or imply that a regularizer can be used to alter a statistical property of a hidden layer's representation and thus improve generalization or performance of deep networks.
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets.
Towards a Rigorous Analysis of Mutual Information in Contrastive Learning
Contrastive learning has emerged as a cornerstone in recent achievements of unsupervised representation learning.
DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion
To improve the diversity of the whole-body object construction, we develop an iterative method that stochastically combines multiple objects observed from the real world into a single object.
Short-term Traffic Prediction with Deep Neural Networks: A Survey
2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and meta-learning networks.
DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting
In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship, for designing basic graph elements as the fundamental building blocks.
Statistical Characteristics of Deep Representations: An Empirical Investigation
In this study, the effects of eight representation regularization methods are investigated, including two newly developed rank regularizers (RR).
