Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!Comment: 8 page

Particles, air quality, policy and health

The diversity of ambient particle size and chemical composition considerably complicates pinpointing the specific causal associations between exposure to particles and adverse human health effects, the contribution of different sources to ambient particles at different locations, and the consequent formulation of policy action to most cost-effectively reduce harm caused by airborne particles. Nevertheless, the coupling of increasingly sophisticated measurements and models of particle composition and epidemiology continue to demonstrate associations between particle components and sources (and at lower concentrations) and a wide range of adverse health outcomes. This article reviews the current approaches to source apportionment of ambient particles and the latest evidence for their health effects, and describes the current metrics, policies and legislation for the protection of public health from ambient particles. A particular focus is placed on particles in the ultrafine fraction. The review concludes with an extended evaluation of emerging challenges and future requirements in methods, metrics and policy for understanding and abating adverse health outcomes from ambient particles

Rate-distortion analysis for light field coding and streaming

A theoretical framework to analyze the rate-distortion performance of a light field coding and streaming system is proposed. This framework takes into account the statistical properties of the light field images, the accuracy of the geometry information used in disparity compensation, and the prediction dependency structure or transform used to exploit correlation among views. Using this framework, the effect that various parameters have on compression efficiency is studied. The framework reveals that the efficiency gains from more accurate geometry, increase as correlation between images increases. The coding gains due to prediction suggested by the framework match those observed from experimental results. This framework is also used to study the performance of light field streaming by deriving a view-trajectory-dependent rate-distortion function. Simulation results show that the streaming results depend both the prediction structure and the viewing trajectory. For instance, independent coding of images gives the best streaming performance for certain view trajectories. These and other trends described by the simulation results agree qualitatively with actual experimental streaming results. Key words: light fields, light field coding, light field streaming, rate-distortion theory, statistical signal processin