129 citations · 370 across the 11 of their papers we have counts for
12 papers
Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations
Daniel Rothchild, Andrew S. Rosen, Eric Taw +3
We present an investigation into diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calcu…
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David Patterson, Joseph Gonzalez, Urs Hölzle +7
Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x…
C5T5: Controllable Generation of Organic Molecules with Transformers
Daniel Rothchild, Alex Tamkin, Julie Yu +2
Methods for designing organic materials with desired properties have high potential impact across fields such as medicine, renewable energy, petrochemical engineering, and agricult…
Carbon Emissions and Large Neural Network Training
David Patterson, Joseph Gonzalez, Quoc Le +6
The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact a…
The Impact of Observing Strategy on Cosmological Constraints with LSST
Michelle Lochner, Dan Scolnic, Husni Almoubayyed +24
The generation-defining Vera C. Rubin Observatory will make state-of-the-art measurements of both the static and transient universe through its Legacy Survey for Space and Time (LS…
FetchSGD: Communication-Efficient Federated Learning with Sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah +5
Existing approaches to federated learning suffer from a communication bottleneck as well as convergence issues due to sparse client participation. In this paper we introduce a nove…