Road attributes play a pivotal role in digital maps, providing critical information for various routing and planning applications that aim to create a safe and efficient traffic environment. While some road attributes are available in existing map data such as OpenStreetMap [3], these sources may…
Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-scale flexible datasets. Most existing public datasets for GNNs are relatively small, which limits the ability of GNNs to…
Computer science (CS) is special among STEM subjects: it aims at an industry sector that has the most job growth but has a constant shortage in the workforce; it is a relatively young and burgeoning subject in K-12 education that has a shortage of classroom teachers; and it is one of a very few…
We introduce two models for high precision sound event detection leveraging transfer learning. The sound events we detect include “speech”, “music”, and “chime”. Both models consist of a CNN backbone pre-trained using AudioSet for audio classification. To get high precision detection results, the…
Accurate and rich representation of roads in a map is critical for safe and efficient navigation experience. Often, open source road data is incomplete and manually adding roads is labor intensive and consequently expensive. In this paper, we propose RING-Net, an approach for Road INference from…
Classifying trip modalities, i.e. driving, walking, etc., from GPS trajectories is one of the fundamental tasks for urban mobility analytics. It can be used for efficient route planning, human activity recognition, and public transportation design where understanding the time and location of…
We study semi-supervised learning (SSL) for vision transformers (ViT), an underexplored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this problem, we use a SSL pipeline, consisting of first un/self-supervised pre-training, followed by supervised…
Conventionally, Earth system (e.g., weather and climate) forecasting relies on numerical simulation with complex physical models and hence is both expensive in computation and demanding on domain expertise. With the explosive growth of spatiotemporal Earth observation data in the past decade,…
Variational Bayesian posterior inference often requires simplifying approximations such as mean-field parametrisation to ensure tractability. However, prior work has associated the variational mean-field approximation for Bayesian neural networks with underfitting in the case of small datasets or…
The Neural Information Processing Systems (NeurIPS) annual meeting fosters the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their fields.
Built-in radar technology, deep domain adaptation for sleep stage classification, and low-latency incremental sleep tracking enable Halo Rise to deliver a seamless, no-contact way to help customers improve sleep.
Amazon encompasses a large number of discrete businesses such as Retail, Advertising, Fresh, Business (B2B e-commerce), and Prime Video, most of which maintain a presence across its e-commerce website. They produce content for our customers that belong to diverse content types such as merchandising…
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