Search Results for author:
Found 4 papers, 1 papers with code
Do Large Scale Molecular Language Representations Capture Important Structural Information?
Various representation learning methods in a supervised setting, including the features extracted using graph neural nets, have emerged for such tasks.
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.
P2L: Predicting Transfer Learning for Images and Semantic Relations
We use this measure, which we call "Predict To Learn" ("P2L"), in the two very different domains of images and semantic relations, where it predicts, from a set of "source" models, the one model most likely to produce effective transfer for training a given "target" model.
Automatic Labeling of Data for Transfer Learning
Transfer learning uses trained weights from a source model as the initial weightsfor the training of a target dataset.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
