Search Results for author: George E. Dahl

Found 20 papers, 6 papers with code

AI system for fetal ultrasound in low-resource settings

no code implementations18 Mar 2022 Ryan G. Gomes, Bellington Vwalika, Chace Lee, Angelica Willis, Marcin Sieniek, Joan T. Price, Christina Chen, Margaret P. Kasaro, James A. Taylor, Elizabeth M. Stringer, Scott Mayer McKinney, Ntazana Sindano, George E. Dahl, William Goodnight III, Justin Gilmer, Benjamin H. Chi, Charles Lau, Terry Spitz, T Saensuksopa, Kris Liu, Jonny Wong, Rory Pilgrim, Akib Uddin, Greg Corrado, Lily Peng, Katherine Chou, Daniel Tse, Jeffrey S. A. Stringer, Shravya Shetty

Using a simplified sweep protocol with real-time AI feedback on sweep quality, we have demonstrated the generalization of model performance to minimally trained novice ultrasound operators using low cost ultrasound devices with on-device AI integration.

Predicting the utility of search spaces for black-box optimization:a simple, budget-aware approach

no code implementations15 Dec 2021 Setareh Ariafar, Justin Gilmer, Zack Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl

For example, when tuning hyperparameters for machine learning pipelines on a new problem given a limited budget, one must strike a balance between excluding potentially promising regions and keeping the search space small enough to be tractable.

Pre-training helps Bayesian optimization too

2 code implementations16 Sep 2021 Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

Contrary to a common belief that BO is suited to optimizing black-box functions, it actually requires domain knowledge on characteristics of those functions to deploy BO successfully.

What Will it Take to Fix Benchmarking in Natural Language Understanding?

no code implementations NAACL 2021 Samuel R. Bowman, George E. Dahl

Evaluation for many natural language understanding (NLU) tasks is broken: Unreliable and biased systems score so highly on standard benchmarks that there is little room for researchers who develop better systems to demonstrate their improvements.

Natural Language Understanding

On Empirical Comparisons of Optimizers for Deep Learning

no code implementations11 Oct 2019 Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl

In particular, we find that the popular adaptive gradient methods never underperform momentum or gradient descent.

Faster Neural Network Training with Data Echoing

1 code implementation12 Jul 2019 Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl

In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training.

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

1 code implementation NeurIPS 2019 Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger Grosse

Increasing the batch size is a popular way to speed up neural network training, but beyond some critical batch size, larger batch sizes yield diminishing returns.

Measuring the Effects of Data Parallelism on Neural Network Training

no code implementations8 Nov 2018 Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl

Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes.

The Importance of Generation Order in Language Modeling

no code implementations EMNLP 2018 Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George E. Dahl

Neural language models are a critical component of state-of-the-art systems for machine translation, summarization, audio transcription, and other tasks.

Language Modelling Machine Translation +1

Motivating the Rules of the Game for Adversarial Example Research

no code implementations18 Jul 2018 Justin Gilmer, Ryan P. Adams, Ian Goodfellow, David Andersen, George E. Dahl

Advances in machine learning have led to broad deployment of systems with impressive performance on important problems.

Embedding Text in Hyperbolic Spaces

no code implementations WS 2018 Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl

Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data.

Sentence Embeddings

Parallel Architecture and Hyperparameter Search via Successive Halving and Classification

1 code implementation25 May 2018 Manoj Kumar, George E. Dahl, Vijay Vasudevan, Mohammad Norouzi

We present a simple and powerful algorithm for parallel black box optimization called Successive Halving and Classification (SHAC).

Classification General Classification

Large scale distributed neural network training through online distillation

no code implementations ICLR 2018 Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton

Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made.

Language Modelling

Machine learning prediction errors better than DFT accuracy

no code implementations J. Chem. Theory Comput. 2017 Felix A. Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S. Schoenholz, George E. Dahl, Oriol Vinyals, Steven Kearnes, Patrick F. Riley, O. Anatole von Lilienfeld

We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules.

Drug Discovery Formation Energy

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes

no code implementations9 Aug 2014 Ryan Prescott Adams, George E. Dahl, Iain Murray

Probabilistic matrix factorization (PMF) is a powerful method for modeling data associ- ated with pairwise relationships, Finding use in collaborative Filtering, computational bi- ology, and document analysis, among other areas.

Collaborative Filtering Gaussian Processes

Multi-task Neural Networks for QSAR Predictions

no code implementations4 Jun 2014 George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov

Although artificial neural networks have occasionally been used for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in the past, the literature has of late been dominated by other machine learning techniques such as random forests.

Improvements to deep convolutional neural networks for LVCSR

no code implementations5 Sep 2013 Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed, George E. Dahl, George Saon, Hagen Soltau, Tomas Beran, Aleksandr Y. Aravkin, Bhuvana Ramabhadran

We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline.

Speech Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.