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Found 8 papers, 3 papers with code
Communication-Efficient Federated Learning with Sketching
A key insight in the design of FedSketchedSGD is that, because the Count Sketch is linear, momentum and error accumulation can both be carried out within the sketch.
Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations
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 calculations.
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x.
C5T5: Controllable Generation of Organic Molecules with Transformers
Methods for designing organic materials with desired properties have high potential impact across fields such as medicine, renewable energy, petrochemical engineering, and agriculture.
Carbon Emissions and Large Neural Network Training
To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models, and we are collaborating with MLPerf developers to include energy usage during training and inference in this industry standard benchmark.
FetchSGD: Communication-Efficient Federated Learning with Sketching
A key insight in the design of FetchSGD is that, because the Count Sketch is linear, momentum and error accumulation can both be carried out within the sketch.
SqueezeWave: Extremely Lightweight Vocoders for On-device Speech Synthesis
Automatic speech synthesis is a challenging task that is becoming increasingly important as edge devices begin to interact with users through speech.
Sound Audio and Speech Processing
Communication-efficient distributed SGD with Sketching
Large-scale distributed training of neural networks is often limited by network bandwidth, wherein the communication time overwhelms the local computation time.
