Document Classification

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

Utilizing weak supervision to infer complex objects in autonomous driving data

Understanding Snorkel

Trove: Ontology-driven Weak Supervision for
Medical Entity Classification

Training Complex Models with Multi-Task Weak Supervision

Training Classifiers with Natural Language Explanations

Train and You’ll Miss It: Interactive Model Iteration with Weak Supervision…

Taking Snorkel for a Spin

SwellShark: A Generative Model for Biomedical NER without Labeled Data

State of AI in Cyber

Stanford University: CS229 – Machine Learning

Stanford University: CS 329S: Machine Learning Systems Design

Software 2.0 and Snorkel: Going Beyond Hand-Labeled Data

Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data

Snuba: Automating Weak Supervision to Label Training Data

Snorkel: Rapid Training Data Creation With Weak Supervision

Snorkel: Programming Training Data

Snorkel: Fast Training Set Generation for Information Extraction

Measuring NLP Progress with Sebastian Ruder

Snorkel MeTaL: Weak Supervision for Multi-Task Learning

Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale

Snorkel AI:
Putting Data First in ML Development

Snorkel — A Weak Supervision System

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

Scene Graph Prediction With Limited Labels

Programmatically Building & Managing Training Data

Programmatic Supervision for Machine Learning

Productionizing ML Research With Thomas Wolf

Principles of Good Machine Learning Systems Design

Practical Weak Supervision: Doing More with Less Data

Osprey: Weak Supervision of Imbalanced Extraction Problems without Code

Ontology-driven weak supervision for clinical entity classification in electronic health records

Multi-Resolution Weak Supervision for Sequential Data

Medical Device Surveillance With Electronic Health Records

Making ML Practical With Snorkel

Machine Learning Production Myths

Leveraging Organizational Resources to Adapt Models to New Data Modalities

Learning to Compose Domain-Specific Transformations for Data Augmentation

Learning the Structure of Generative Models without Labeled Data

Learning Dependency Structures for Weak Supervision Models

Interactive Programmatic Labeling for Weak Supervision

Inferring Generative Model Structure with Static Analysis

How To Overcome Practical Challenges for AI in the Public Sector

How to Overcome Practical Challenges for AI in Healthcare

How To Overcome Practical Challenges for AI in Finance

How Machine Learning is Changing Software

Hand labeling is the Past. The Future is #NoLabel AI

Generating Labels for Model Training Using Weak Supervision review

Fonduer: Knowledge Base Construction from Richly Formatted Data

Fast & Three-rious: Speed Up Weak Supervision with Triplet Methods

Debugging AI Applications Pipeline

Data Programming: Creating Large Training Sets, Quickly

Data programming with DDLite: Putting Humans in a Different Part of the Loop

Cross-Modal Data Programming Enables Rapid Medical Machine Learning

Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision

Brown University: CSCI 2952-C – Learning with Limited Labeled Data

Bootstrapping Conversational Agents with Weak Supervision

Advanced Natural Language Processing

A Machine-Compiled Database of Genome-Wide Association Studies

A clinical text classification paradigm using weak supervision and deep representation