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Found 10 papers, 1 papers with code
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks
Transfer learning is a popular technique to learn a task using less training data and fewer compute resources.
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models
This paper proposes KD-NAS, the use of Neural Architecture Search (NAS) guided by the Knowledge Distillation process to find the optimal student model for distillation from a teacher, for a given natural language task.
G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning
Transfer learning is a deep-learning technique that ameliorates the problem of learning when human-annotated labels are expensive and limited.
Large Scale Neural Architecture Search with Polyharmonic Splines
Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification.
Visual Objects As Context: Exploiting Visual Objects for Lexical Entailment
We propose a new word representation method derived from visual objects in associated images to tackle the lexical entailment task.
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search
The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset.
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.
Improving Transferability of Deep Neural Networks
This is currently addressed by Transfer Learning where one learns the small data set as a transfer task from a larger source dataset.
IBM Deep Learning Service
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision.
Distributed, Parallel, and Cluster Computing
