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Found 8 papers, 2 papers with code
Predicting Deep Neural Network Generalization with Perturbation Response Curves
However, despite these successes, the recent Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition suggests that there is a need for more robust and efficient measures of network generalization.
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations
Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in drug discovery and material design.
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
Gradient flows are a powerful tool for optimizing functionals in general metric spaces, including the space of probabilities endowed with the Wasserstein metric.
Gi and Pal Scores: Deep Neural Network Generalization Statistics
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks.
Alleviating Noisy Data in Image Captioning with Cooperative Distillation
Image captioning systems have made substantial progress, largely due to the availability of curated datasets like Microsoft COCO or Vizwiz that have accurate descriptions of their corresponding images.
Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO.
Tabular Transformers for Modeling Multivariate Time Series
This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.
Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics
In this work, we propose a method for measuring how well the latent space of deep generative models is able to encode structural and chemical features of molecular datasets by correlating latent space metrics with metrics from the field of topological data analysis (TDA).
