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Neural Network Optimization

902 directly classified papers

Papers per year

Papers

MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning CVPR 2020
What's Hidden in a Randomly Weighted Neural Network? CVPR 2020
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio CVPR 2020
Sideways: Depth-Parallel Training of Video Models CVPR 2020
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization ICML 2020
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning AAAI 2020
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks AAAI 2020
Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning AAAI 2020
Going Deep: Graph Convolutional Ladder-Shape Networks AAAI 2020
Dynamic Data Selection for Curriculum Learning via Ability Estimation EMNLP 2020
Query-Key Normalization for Transformers EMNLP 2020
Streaming Batch Gradient Tracking for Neural Network Training (Student Abstract) AAAI 2020
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method NIPS 2020
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel NIPS 2020
Curriculum Learning by Dynamic Instance Hardness NIPS 2020
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error NIPS 2020
Neural Architecture Generator Optimization NIPS 2020
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate NIPS 2020
SelectScale: Mining More Patterns from Images via Selective and Soft Dropout IJCAI 2020
Is the Skip Connection Provable to Reform the Neural Network Loss Landscape? IJCAI 2020
Reducing Underflow in Mixed Precision Training by Gradient Scaling IJCAI 2020
Understanding and Exploring the Network with Stochastic Architectures NIPS 2020
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs NIPS 2020
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks NIPS 2020
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization NIPS 2020