conftrace_
← Deep Learning
Deep Learning ›

Optimization & Theory

6,829 papers (27 classified directly here)

Subtopics

Model Compression (1,785) Optimization (1,664) Efficient Computing (1,579) Theory (1,082) Neural Network Optimization (903) Evaluation (509) Loss Functions (123) Interpretability (105) Stochastic Methods (97) Regularization (77) Generalization (24) Sparse Optimization (7)

Papers per year

8
50
642
1173
'10
'15
'20
'25

Papers (including subtopics)

O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers NIPS 2020
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping NIPS 2020
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity NIPS 2020
Rational neural networks NIPS 2020
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities NIPS 2020
Kernel Methods Through the Roof: Handling Billions of Points Efficiently NIPS 2020
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate NIPS 2020
AdaTune: Adaptive Tensor Program Compilation Made Efficient NIPS 2020
STEER : Simple Temporal Regularization For Neural ODE NIPS 2020
Coresets via Bilevel Optimization for Continual Learning and Streaming NIPS 2020
Understanding and Exploring the Network with Stochastic Architectures NIPS 2020
Adversarial Training is a Form of Data-dependent Operator Norm Regularization NIPS 2020
Finite Versus Infinite Neural Networks: an Empirical Study NIPS 2020
Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors NIPS 2020
Efficient Algorithms for Device Placement of DNN Graph Operators NIPS 2020
Calibrating CNNs for Lifelong Learning NIPS 2020
Sparse Weight Activation Training NIPS 2020
On the linearity of large non-linear models: when and why the tangent kernel is constant NIPS 2020
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations NIPS 2020
Explicit Regularisation in Gaussian Noise Injections NIPS 2020
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs NIPS 2020
Lipschitz-Certifiable Training with a Tight Outer Bound NIPS 2020
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection NIPS 2020
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks NIPS 2020
Directional convergence and alignment in deep learning NIPS 2020