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Neural Network Optimization

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Papers

Regularized Context Gates on Transformer for Machine Translation ACL 2020
Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems ACL 2020
A Joint Model for Document Segmentation and Segment Labeling ACL 2020
Fixed-Point Back-Propagation Training CVPR 2020
Designing Network Design Spaces CVPR 2020
TeaForN: Teacher-Forcing with N-grams EMNLP 2020
On the Computational Power of Transformers and Its Implications in Sequence Modeling EMNLP 2020
TurnGPT: a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog EMNLP 2020
Learning Context-free Languages with Nondeterministic Stack RNNs EMNLP 2020
Scheduled DropHead: A Regularization Method for Transformer Models EMNLP 2020
On Second Order Behaviour in Augmented Neural ODEs NIPS 2020
Predicting Training Time Without Training NIPS 2020
Triple descent and the two kinds of overfitting: where & why do they appear? NIPS 2020
Ode to an ODE NIPS 2020
Provably Efficient Neural GTD for Off-Policy Learning NIPS 2020
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement NIPS 2020
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods NIPS 2020
Almost Surely Stable Deep Dynamics NIPS 2020
Theory-Inspired Path-Regularized Differential Network Architecture Search NIPS 2020
A Closer Look at Accuracy vs. Robustness NIPS 2020
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures NIPS 2020
Is normalization indispensable for training deep neural network? NIPS 2020
Evolving Normalization-Activation Layers NIPS 2020
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients NIPS 2020
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks NIPS 2020