Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Diffusion models currently dominate the field of data-driven image synthesis with their
unparalleled scaling to large datasets. In this paper we identify and rectify several causes for …

In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully
convolutional model based on dilated temporal convolutions over 2D keypoints. We also …

Recent developments in analytical technologies helped in developing applications for real-
time problems faced by industries. These applications are often found to consume more time …

Abstract Normalization layers (eg, Batch Normalization, Layer Normalization) were
introduced to help with optimization difficulties in very deep nets, but they clearly also help …

We propose Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform
quantization scheme for the bell-shaped and long-tailed distribution of weights and …

We hypothesize that due to the greedy nature of learning in multi-modal deep neural
networks, these models tend to rely on just one modality while under-fitting the other …

Normalization layers are a staple in state-of-the-art deep neural network architectures. They
are widely believed to stabilize training, enable higher learning rate, accelerate …

Normalization is known to help the optimization of deep neural networks. Curiously, different
architectures require specialized normalization methods. In this paper, we study what …