We present an overview of techniques for quantizing convolutional neural networks for
inference with integer weights and activations. Per-channel quantization of weights and per-layer …

With the advent of deep learning, neural network-based recommendation models have
emerged as an important tool for tackling personalization and recommendation tasks. These …

Large language models have shown their remarkable capabilities as a general interface for
various language-related applications. Motivated by this, we target to build a unified …

Several post-training quantization methods have been applied to large language models (LLMs),
and have been shown to perform well down to 8-bits. We find that these methods break …

Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision
applications. A key component that drives the impressive performance for zero-shot transfer and …

Web usage mining is application of data mining techniques to discover usage patterns from
web data, in order to better serve the needs of web based applications. The user access log …

Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine
learning, but have also grown in parameters and computational complexity, making them …

Diffusion models have emerged as a powerful tool for point cloud generation. A key component
that drives the impressive performance for generating high-quality samples from noise is …

This paper provides an overview of the physical layer of the Forward Link Only (FLO) Air
Interface. The FLO Air Interface is a key component of the MediaFLO system developed by …

This paper addresses the growing need for efficient large language models (LLMs) on mobile
devices, driven by increasing cloud costs and latency concerns. We focus on designing top…