25 citations · 55 across the 8 of their papers we have counts for
9 papers
Computational search for materials having a giant anomalous Hall effect in the pyrochlore and spinel crystal structures
Sean Sullivan, Seungjun Lee, Nathan J. Szymanski +4
Ferromagnetic pyrochlore and spinel materials with topological flat bands are of interest for their potential to exhibit a giant anomalous Hall effect (AHE). In this work, we prese…
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations
Nikil Roashan Selvam, Amil Merchant, Stefano Ermon
In diffusion models, samples are generated through an iterative refinement process, requiring hundreds of sequential model evaluations. Several recent methods have introduced appro…
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…
Scalable Diffusion for Materials Generation
Sherry Yang, KwangHwan Cho, Amil Merchant +4
Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advanc…
Predicting emergence of crystals from amorphous matter with deep learning
Muratahan Aykol, Amil Merchant, Simon Batzner +2
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synth…
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…