We introduce new methods for 1) accelerating and 2) stabilizing training for large language-
vision models. 1) For acceleration, we introduce SwitchBack, a linear layer for int8 quantized …

Foundation models are rapidly being developed for computational pathology applications.
However, it remains an open question which factors are most important for downstream …

Recently diffusion models have emerged as a new powerful generative method for 3D point
cloud generation tasks. However few works study the effect of the architecture of the …

Humans have the ability to adapt the type of information they use, the procedure they
employ, and the amount of time they spend when solving problems. However, most standard …

We present a deep-learning approach for the task of Concurrent Speaker Detection (CSD)
using a modified transformer model. Our model is designed to handle multi-microphone data …

Deploying image target detection algorithms on embedded devices is critical. Previous
studies assumed that fewer model parameters and computations improved the inference …

We introduce Learning from Offline Foundation Features with Tensor Augmentations (LOFF-
TA), an efficient training scheme designed to harness the capabilities of foundation models …

Existing solutions to image editing tasks suffer from several issues. Though achieving
remarkably satisfying generated results, some supervised methods require huge amounts of …

We consider the training of the first layer of vision models and notice the clear relationship
between pixel values and gradient update magnitudes: the gradients arriving at the weights …

Click-Through Rate (CTR) prediction is an important application in online advertising, and
deep learning-based models are developed to maximize CTR prediction. In this study, the …