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Found 8 papers, 1 papers with code
Rethinking E-Commerce Search
Instead of converting unstructured data (web pages, customer reviews, etc) to structured data, we instead convert structured data (product inventory, catalogs, taxonomies, etc) into textual data, which can be easily integrated into the text corpus that trains LLMs.
Mitigating Pooling Bias in E-commerce Search via False Negative Estimation
Efficient and accurate product relevance assessment is critical for user experiences and business success.
An Embedding-Based Grocery Search Model at Instacart
To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method.
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point
In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of datatypes developed for production cloud-scale inferencing on custom hardware.
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
We evaluate our proposed method for various machine learning tasks including object detection on MS-COCO 2014 dataset, multiple object tracking problem on MOT-Challenge dataset, and human activity classification on UCF 101 dataset.
Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms
The lossless or lossy encoding of the feature space is proposed to enhance the maximum input rate supported by the edge platform and/or reduce the energy of the edge platform.
NeuroTrainer: An Intelligent Memory Module for Deep Learning Training
This paper presents, NeuroTrainer, an intelligent memory module with in-memory accelerators that forms the building block of a scalable architecture for energy efficient training for deep neural networks.
Hardware Architecture
Cascade Adversarial Machine Learning Regularized with a Unified Embedding
Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks.
