Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

Both non-local (NL) operation and sparse representation are crucial for Single Image Super-
Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non …

There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …

Large Transformer models routinely achieve state-of-the-art results on a number of tasks but
training these models can be prohibitively costly, especially on long sequences. We …

Federated learning over distributed multi-party data is an emerging paradigm that iteratively
aggregates updates from a group of devices to train a globally shared model. Relying on a …

Transformer models have advanced the state of the art in many Natural Language
Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended …

Quantization based techniques are the current state-of-the-art for scaling maximum inner
product search to massive databases. Traditional approaches to quantization aim to …

Machine learning (ML) over distributed multi-party data is required for a variety of domains.
Existing approaches, such as federated learning, collect the outputs computed by a group of …

Deep neural networks (DNNs) enable innovative applications of machine learning like
image recognition, machine translation, or malware detection. However, deep learning is …

Recent advances in efficient Transformers have exploited either the sparsity or low-rank
properties of attention matrices to reduce the computational and memory bottlenecks of …