Search Results for author:
Found 5 papers, 5 papers with code
Few-shot human motion prediction for heterogeneous sensors
Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors.
End-to-End Image-Based Fashion Recommendation
In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models.
Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set.
HIDRA: Head Initialization across Dynamic targets for Robust Architectures
In this paper, we present HIDRA, a meta-learning approach that enables training and evaluating across tasks with any number of target variables.
Chameleon: Learning Model Initializations Across Tasks With Different Schemas
Parametric models, and particularly neural networks, require weight initialization as a starting point for gradient-based optimization.
