← Optimization & Theory
Machine Learning › Optimization & Theory ›

Neural Network Optimization

3648 directly classified papers

Papers per year

Papers

Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter NIPS 2017
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs ICML 2017
A Neural Parametric Singing Synthesizer INTERSPEECH 2017
Improving Training of Deep Neural Networks via Singular Value Bounding CVPR 2017
Trainable Greedy Decoding for Neural Machine Translation EMNLP 2017
Pronunciation Learning with RNN-Transducers INTERSPEECH 2017
Structured-Based Curriculum Learning for End-to-End English-Japanese Speech Translation INTERSPEECH 2017
Training Quantized Nets: A Deeper Understanding NIPS 2017
On Improving Acoustic Models for TORGO Dysarthric Speech Database INTERSPEECH 2017
Self-Normalizing Neural Networks NIPS 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning NIPS 2017
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice NIPS 2017
The Reversible Residual Network: Backpropagation Without Storing Activations NIPS 2017
Denoising Recurrent Neural Network for Deep Bidirectional LSTM Based Voice Conversion INTERSPEECH 2017
Why ADAGRAD Fails for Online Topic Modeling EMNLP 2017
SGD Learns the Conjugate Kernel Class of the Network NIPS 2017
Backstitch: Counteracting Finite-Sample Bias via Negative Steps INTERSPEECH 2017
An Efficient Phone N-Gram Forward-Backward Computation Using Dense Matrix Multiplication INTERSPEECH 2017
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks NIPS 2017
Approximate Large-scale Multiple Kernel k-means Using Deep Neural Network IJCAI 2017
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction IJCAI 2017
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds ICML 2017
Neural Optimizer Search with Reinforcement Learning ICML 2017
Probabilistic Line Searches for Stochastic Optimization JMLR 2017
EigenNet: Towards Fast and Structural Learning of Deep Neural Networks IJCAI 2017