- Auto-Encoding Variational Bayes
- Backdrop: Stochastic Backpropagation
- Backprop Evolution
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Towards Deep Conversational Recommendations
- Deep Recurrent Q-Learning for Partially Observable MDPs
- Deep Residual Learning for Image Recognition
- Discovery of Natural Language Concepts in Individual Units of CNNs
- Deep Learning: An Introduction for Applied Mathematicians
- Deep Learning for Sentiment Analysis: A Survey
- Deep Reinforcement Learning from Human Preferences
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs
- Group Normalization
- Layer Normalization
- The Matrix Calculus You Need For Deep Learning
- Achieving Human Parity in Conversational Speech Recognition
- Artificial Intelligence 101 First World-class Overview of AI For All
- Playing Atari with Deep Reinforcement Learning
- Relational Deep Reinforcement Learning
- geomstats: a Python Package for Riemannian Geometry in Machine Learning
- A Character-Level Approach to the Text Normalization Problem Based on a New Causal Encoder
- Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
dhanizael / data-science-research-papers Goto Github PK
View Code? Open in Web Editor NEWThis project forked from manjunath5496/data-science-research-papers
"It is inevitable that any one who can borrow freely to cover errors of management will borrow rather than correct the errors."― Henry Ford