Search Results for author:
Found 8 papers, 4 papers with code
KirchhoffNet: A Circuit Bridging Message Passing and Continuous-Depth Models
Moreover, we justify that irrespective of the number of parameters within a KirchhoffNet, its forward calculation can always be completed within 1/f seconds, with f representing the hardware's clock frequency.
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Data lies at the core of modern deep learning.
FreDo: Frequency Domain-based Long-Term Time Series Forecasting
Finally, we validate that the frequency domain is indeed better by comparing univariate models trained in the frequency v. s.
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
A common assumption in training neural networks via maximum likelihood estimation on time series is that the errors across time steps are uncorrelated.
Conditioned Natural Language Generation using only Unconditioned Language Model: An Exploration
Transformer-based language models have shown to be very powerful for natural language generation (NLG).
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network
Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model.
LAMOL: LAnguage MOdeling for Lifelong Language Learning
We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling.
Ranked #4 on
Continual Learning
on ASC (19 tasks)
Temporal Pattern Attention for Multivariate Time Series Forecasting
To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.
Ranked #2 on
Univariate Time Series Forecasting
on Electricity
