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Neural Network Optimization

3648 directly classified papers

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Papers

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data ICML 2020
Training Neural Networks for and by Interpolation ICML 2020
Stochastic Gradient and Langevin Processes ICML 2020
Improving the Gating Mechanism of Recurrent Neural Networks ICML 2020
Provably Efficient Exploration in Policy Optimization ICML 2020
Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation CVPR 2020
Alleviation of Gradient Exploding in GANs: Fake Can Be Real CVPR 2020
NADS: Neural Architecture Distribution Search for Uncertainty Awareness ICML 2020
Adaptive Interaction Modeling via Graph Operations Search CVPR 2020
Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as Departure from Expectedness SEMEVAL 2020
PsuedoProp at SemEval-2020 Task 11: Propaganda Span Detection Using BERT-CRF and Ensemble Sentence Level Classifier SEMEVAL 2020
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients NIPS 2020
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates NIPS 2020
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization IJCAI 2020
An Improved Analysis of Stochastic Gradient Descent with Momentum NIPS 2020
Escaping the Gravitational Pull of Softmax NIPS 2020
On the Regularization Properties of Structured Dropout CVPR 2020
Regularizing Neural Networks via Minimizing Hyperspherical Energy CVPR 2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them NIPS 2020
Growing Adaptive Multi-hyperplane Machines ICML 2020
Inference Strategies for Machine Translation with Conditional Masking EMNLP 2020
Language Model Prior for Low-Resource Neural Machine Translation EMNLP 2020
Understanding the Difficulty of Training Transformers EMNLP 2020
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks EMNLP 2020
Gradient-based Analysis of NLP Models is Manipulable EMNLP 2020