This review discusses a recently developed optimization technique for analyzing the
nonlinear stability of a flow state. It is based on a nonlinear extension of nonmodal analysis …

Reverse time migration (RTM) is a seismic imaging method to map the subsurface reflectivity
using recorded seismic waveforms. The practice in exploration seismology has long …

Unsupervised large-scale vision-language pre-training has shown promising advances on
various downstream tasks. Existing methods often model the cross-modal interaction either …

Large language models have led to state-of-the-art accuracies across several tasks.
However, training these models efficiently is challenging because: a) GPU memory capacity …

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

Stereo depth estimation relies on optimal correspondence matching between pixels on
epipolar lines in the left and right images to infer depth. In this work, we revisit the problem …

Recent developments in multimodal training methodologies, including CLIP and ALIGN,
obviate the necessity for individual data labeling. These approaches utilize pairs of data and …

Scaling up deep neural network capacity has been known as an effective approach to
improving model quality for several different machine learning tasks. In many cases …

We propose a systematic approach to reduce the memory consumption of deep neural
network training. Specifically, we design an algorithm that costs O (sqrt (n)) memory to train …

Many state-of-the-art ML results have been obtained by scaling up the number of
parameters in existing models. However, parameters and activations for such large models …