15 citations · 23 across the 3 of their papers we have counts for
5 papers
Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction
Samuel J. Yang, Shutong Li, Subhashini Venugopalan +5
Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such…
VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz, James Harrison, C. Daniel Freeman +8
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the sam…
Learn2Hop: Learned Optimization on Rough Landscapes
Amil Merchant, Luke Metz, Sam Schoenholz +1
Optimization of non-convex loss surfaces containing many local minima remains a critical problem in a variety of domains, including operations research, informatics, and material d…
Does Data Augmentation Benefit from Split BatchNorms
Amil Merchant, Barret Zoph, Ekin Dogus Cubuk
Data augmentation has emerged as a powerful technique for improving the performance of deep neural networks and led to state-of-the-art results in computer vision. However, state-o…
What Happens To BERT Embeddings During Fine-tuning?
Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick +1
While there has been much recent work studying how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these mod…