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Found 8 papers, 2 papers with code
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.
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.
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.
Large-Scale Chemical Language Representations Capture Molecular Structure and Properties
Models based on machine learning can enable accurate and fast molecular property predictions, which is of interest in drug discovery and material design.
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.
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed.
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
Risk Assessment and Statistical Significance in the Age of Foundation Models
Using this framework, we formally develop a risk-aware approach for foundation model selection given guardrails quantified by specified metrics.
