Optimization & Theory

Improved Knowledge Distillation via Teacher Assistant AAAI 2020 Improving Low Compute Language Modeling with In-Domain Embedding Initialisation EMNLP 2020 Incremental Multi-Domain Learning with Network Latent Tensor Factorization AAAI 2020 Indirect Stochastic Gradient Quantization and Its Application in Distributed Deep Learning AAAI 2020 Inefficiency of K-FAC for Large Batch Size Training AAAI 2020 Infinitely deep neural networks as diffusion processes AISTATS 2020 Information-Theoretic Local Minima Characterization and Regularization ICML 2020 Information-Theoretic Probing with Minimum Description Length EMNLP 2020 Information-Theoretic Understanding of Population Risk Improvement with Model Compression AAAI 2020 In search of robust measures of generalization NeurIPS 2020 Inserting Information Bottlenecks for Attribution in Transformers EMNLP 2020 InstaNAS: Instance-Aware Neural Architecture Search AAAI 2020 Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients NeurIPS 2020 Intelligent Image Collection: Building the Optimal Dataset WACV 2020 Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs NeurIPS 2020 Interpretability and Analysis in Neural NLP ACL 2020 Inter-Region Affinity Distillation for Road Marking Segmentation CVPR 2020 Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks NeurIPS 2020 Is the Skip Connection Provable to Reform the Neural Network Loss Landscape? IJCAI 2020 Iterative Compression of End-to-End ASR Model Using AutoML Interspeech 2020 Joint Texture and Geometry Optimization for RGB-D Reconstruction CVPR 2020 Kernel Based Progressive Distillation for Adder Neural Networks NeurIPS 2020 Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks NeurIPS 2020 Kernel Methods Through the Roof: Handling Billions of Points Efficiently NeurIPS 2020 Knowledge Graph Embedding Compression ACL 2020