Search Results for author:
Found 9 papers, 3 papers with code
A Manifold Representation of the Key in Vision Transformers
The query, key, and value are often intertwined and generated within those blocks via a single, shared linear transformation.
State Representation Learning Using an Unbalanced Atlas
The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient representations.
It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting
With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future.
Unsupervised Representation Learning in Partially Observable Atari Games
Contrastive methods have performed better than generative models in previous state representation learning research.
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
Various alterations have been proposed to better facilitate time series forecasting, of which this study focused on the Informer, LogSparse Transformer and Autoformer.
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
+2
Deep Reinforcement Learning with Swin Transformers
Transformers are neural network models that utilize multiple layers of self-attention heads and have exhibited enormous potential in natural language processing tasks.
improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise
In this article, we further explore the possibility of replacing priors with noise and sample the noise from a Gaussian distribution to introduce more diversity into this algorithm.
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms.
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a combination of Double Q-learning and Dueling Q-learning.
