High throughput serving of large language models (LLMs) requires batching sufficiently
many requests at a time. However, existing systems struggle because the key-value cache …

Representing and rendering dynamic scenes has been an important but challenging task.
Especially to accurately model complex motions high efficiency is usually hard to guarantee …

Answering visual queries is a complex task that requires both visual processing and
reasoning. End-to-end models, the dominant approach for this task, do not explicitly …

Natural language processing and 2D vision models have attained remarkable proficiency on
many tasks primarily by escalating the scale of training data. However, 3D vision tasks have …

Large-scale diffusion-based generative models have led to breakthroughs in text-
conditioned high-resolution image synthesis. Starting from random noise, such text-to-image …

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

Although deep learning has revolutionized protein structure prediction, almost all
experimentally characterized de novo protein designs have been generated using …

Transformers are slow and memory-hungry on long sequences, since the time and memory
complexity of self-attention are quadratic in sequence length. Approximate attention …

Time series analysis is of immense importance in extensive applications, such as weather
forecasting, anomaly detection, and action recognition. This paper focuses on temporal …

Pretrained large language models (LLMs) are widely used in many sub-fields of natural
language processing (NLP) and generally known as excellent few-shot learners with task …