36 citations · 50 across the 5 of their papers we have counts for
5 papers
BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga +8
Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks. However, these models have hundreds of billions of parameter…
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining
Jacob Portes, Alex Trott, Sam Havens +6
Although BERT-style encoder models are heavily used in NLP research, many researchers do not pretrain their own BERTs from scratch due to the high cost of training. In the past hal…
Representation range needs for 16-bit neural network training
Valentina Popescu, Abhinav Venigalla, Di Wu +1
Deep learning has grown rapidly thanks to its state-of-the-art performance across a wide range of real-world applications. While neural networks have been trained using IEEE-754 bi…
Adaptive Braking for Mitigating Gradient Delay
Abhinav Venigalla, Atli Kosson, Vitaliy Chiley +1
Neural network training is commonly accelerated by using multiple synchronized workers to compute gradient updates in parallel. Asynchronous methods remove synchronization overhead…
Pipelined Backpropagation at Scale: Training Large Models without Batches
Atli Kosson, Vitaliy Chiley, Abhinav Venigalla +2
New hardware can substantially increase the speed and efficiency of deep neural network training. To guide the development of future hardware architectures, it is pertinent to expl…