Amil Merchant

According to our database, Amil Merchant authored at least 8 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Scaling deep learning for materials discovery.

Amil Merchant, Simon L. Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, Ekin Dogus Cubuk

Nat., 2023

Scalable Diffusion for Materials Generation.

Mengjiao Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk

CoRR, 2023

Predicting emergence of crystals from amorphous matter with deep learning.

Muratahan Aykol, Amil Merchant, Simon L. Batzner, Jennifer N. Wei, Ekin Dogus Cubuk

CoRR, 2023

2022

VeLO: Training Versatile Learned Optimizers by Scaling Up.

Luke Metz, James Harrison, C. Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, Jascha Sohl-Dickstein

CoRR, 2022

2021

Learn2Hop: Learned Optimization on Rough Landscapes.

Amil Merchant, Luke Metz, Samuel S. Schoenholz, Ekin D. Cubuk

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Does Data Augmentation Benefit from Split BatchNorms.

Amil Merchant, Barret Zoph, Ekin Dogus Cubuk

CoRR, 2020

What Happens To BERT Embeddings During Fine-tuning?

Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, Ian Tenney

Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2020

2019

Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models.

Alexander Lin, Amil Merchant, Suproteem K. Sarkar, Alexander D'Amour

Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, 2019