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Found 4 papers, 1 papers with code
A Character-Word Compositional Neural Language Model for Finnish
Inspired by recent research, we explore ways to model the highly morphological Finnish language at the level of characters while maintaining the performance of word-level models.
Gaussian Process Kernels for Popular State-Space Time Series Models
In this paper we investigate a link between state- space models and Gaussian Processes (GP) for time series modeling and forecasting.
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data.
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series
Although unidirectional RNNs have recently been trained successfully to model such time series, inference in the negative time direction is non-trivial.
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