Correcting Forecasts with Multifactor Neural Attention

By Matthew Riemer, Aditya Vempaty, Flávio du Pin Calmon, Fenno F. Terry Heath III, R. Hull, Elham Khabiri. 2016

32 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ignacio Cases
2. Clemens Rosenbaum
3. Miao Liu
4. Matthew Riemer
5. Irina Rish
6. Tim Klinger
7. Tao Lin
8. G. Tesauro

Citations in the corpus

• Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (2018, methodology): “…the dynamics of weight sharing, it bears conceptual similarity to mixtures of experts (Jacobs et al., 1991) style models for lifelong and multi-task learning (Misra et al., 2016; Riemer et al., 2016b; Aljundi et al., 2017; Fernando et al., 2017; Shazeer et al., 2017; Rosenbaum et al., 2018).”
• Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning (2017, background): “Our work is also related to mixtures of experts architectures (Jacobs et al., 1991), (Jordan & Jacobs, 1994) as well as their modern attention based (Riemer et al., 2016) and sparse (Shazeer et al., 2017) variants.”
• Routing Networks and the Challenges of Modular and Compositional Computation (2019, methodology): “The second option, as employed in Rosenbaum et al. [2017], is to use this regularizer to incentivize transfer between different kinds of samples.”
• Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning (2017, neutral): “(Riemer et al., 2016) suggests that a many element gating network can be improved with a sparsity constraint, but this did not work as well as the greedy strategy for our model and experiments.”
• Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs (2017, methodology): “The attention model in Seq-RNNs [3,7] has been studied very recently for time series prediction [28] and classification [29].”
• Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019, background): “First, in RNNs variables differ in predictive power on the target, thereby contributing differently to the prediction (Feng et al., 2018; Riemer et al., 2016).”