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Found 5 papers, 3 papers with code
User-LLM: Efficient LLM Contextualization with User Embeddings
Large language models (LLMs) have revolutionized natural language processing.
Massively Scalable Inverse Reinforcement Learning in Google Maps
Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addressed planetary-scale problems with hundreds of millions of states and demonstration trajectories.
Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services
Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors.
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data.
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
In this paper, we present a new approach to time series forecasting.
