83 citations · 185 across the 8 of their papers we have counts for
8 papers
Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels
Danilo Ribeiro, Omid Abdar, Jack Goetz +4
Frame semantic parsing is an important component of task-oriented dialogue systems. Current models rely on a significant amount training data to successfully identify the intent an…
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik +3
Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that co…
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Pooja Sethi, Denis Savenkov, Forough Arabshahi +6
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task. In this work…
Federated Learning via Synthetic Data
Jack Goetz, Ambuj Tewari
Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data. However the standard method is to transmit mode…
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning
Jack Goetz, Ambuj Tewari
Active learning seeks to build the best possible model with a budget of labelled data by sequentially selecting the next point to label. However the training set is no longer \text…
Active Federated Learning
Jack Goetz, Kshitiz Malik, Duc Bui +3
Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, the…