Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.Comment: Wei Wen and Kuang-Hung Liu contribute equall

Spartan Daily, October 6, 1953

Volume 42, Issue 10https://scholarworks.sjsu.edu/spartandaily/11916/thumbnail.jp

Adverse Drug Reaction Classification With Deep Neural Networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs

An analysis of The Oxford Guide to practical lexicography (Atkins and Rundell 2008)

Since at least a decade ago, the lexicographic community at large has been demanding that a modern textbook be designed - one that Would place corpora in the centre of the lexicographic enterprise. Written by two of the most respected practising lexicographers, this book has finally arrived, and delivers on very many levels. This review article presents a critical analysis of its features

From Bit Valley to Bitcoin: the NASDAQ Odyssey

Over the past 15 years, NASDAQ, the world’s first all-electronic stock exchange, has actively engaged in efforts to serve the global digital economy by expanding its reach beyond its original domestic U.S. market. They have attempted to create a global 24/7 trading platform, to serve customers in the U.S., Japan, and Europe. These efforts have met with varying degrees of success. More recently, the renamed NASDAQ OMX Group has been experimenting with the disruptive fintech (financial technology) Bitcoin and its underlying technology blockchain to develop robust trading solutions, which drastically reduce transaction and record keeping costs. In this paper we analyze the various approaches taken by NASDAQ in its expansion ventures. We describe the similarities and differences in these undertakings, in order to identify successful strategies for firms who desire to increase the quality of their products while increasing efficiency and reducing the costs of their services. Drawing upon the strategy literature, we also develop theoretical models on how markets operate, and derive a series of propositions about the interplay between technology and markets

Spartan Daily, April 17, 2014

Volume 142, Issue 31https://scholarworks.sjsu.edu/spartandaily/1491/thumbnail.jp