Content

Popular Optimization algorithms

• SGD [Book]
• Momentum [Book]
• RMSProp [Book]
• AdaGrad [Link]
• ADAM [Link]
• AdaBound [Link] [Github]
• ADAMAX [Link]
• NADAM [Link]
• ADAMW [Link]
• AdaLOMO Link
• All optimizers list Awesome-Optimizer

Normalization Methods

• BatchNorm [Link]
• Weight Norm [Link]
• Spectral Norm [Link]
• Cosine Normalization [Link]
• L2 Regularization versus Batch and Weight Normalization Link
• WHY GRADIENT CLIPPING ACCELERATES TRAINING: A THEORETICAL JUSTIFICATION FOR ADAPTIVITY Link

On Convexity and Generalization of Neural Networks

• Convex Neural Networks [Link]
• Breaking the Curse of Dimensionality with Convex Neural Networks [Link]
• UNDERSTANDING DEEP LEARNING REQUIRES RETHINKING GENERALIZATION [Link]
• Optimal Control Via Neural Networks: A Convex Approach. [Link]
• Input Convex Neural Networks [Link]
• A New Concept of Convex based Multiple Neural Networks Structure. [Link
• SGD Converges to Global Minimum in Deep Learning via Star-convex Path [Link]
• A Convergence Theory for Deep Learning via Over-Parameterization Link

Continuation Methods and Curriculum Learning

• Curriculum Learning [Link]
• SOLVING RUBIK’S CUBE WITH A ROBOT HAND Link
• Noisy Activation Function [Link]
• Mollifying Networks [Link]
• Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks Link Talk
• Automated Curriculum Learning for Neural Networks Link
• On The Power of Curriculum Learning in Training Deep Networks Link
• On-line Adaptative Curriculum Learning for GANs Link
• Parameter Continuation with Secant Approximation for Deep Neural Networks and Step-up GAN Link
• HashNet: Deep Learning to Hash by Continuation. [Link]
• Learning Combinations of Activation Functions. [Link]
• Learning and development in neural networks: The importance of starting small (1993) Link
• Flexible shaping: How learning in small steps helps Link
• Curriculum Labeling: Self-paced Pseudo-Labeling for Semi-Supervised Learning Link
• RETHINKING CURRICULUM LEARNING WITH INCREMENTAL LABELS AND ADAPTIVE COMPENSATION Link
• Parameter Continuation Methods for the Optimization of Deep Neural Networks Link
• Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection [Link (https://www.aclweb.org/anthology/W18-6314.pdf)
• Reinforcement Learning based Curriculum Optimization for Neural Machine Translation Link
• EVOLUTIONARY POPULATION CURRICULUM FOR SCALING MULTI-AGENT REINFORCEMENT LEARNING Link
• ENTROPY-SGD: BIASING GRADIENT DESCENT INTO WIDE VALLEYS Link
• NEIGHBOURHOOD DISTILLATION: ON THE BENEFITS OF NON END-TO-END DISTILLATION Link
• LEARNING TO EXECUTE Link
• Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing Link
• Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum Link
• Breaking the Curse of Space Explosion: Towards Effcient NAS with Curriculum Search Link
• Continuation Methods and Curriculum Learning for Learning to Rank Link

On Loss Surfaces and Generalization of Deep Neural Networks

• Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape Link
• Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape Link
• Exact solutions to the nonlinear dynamics of learning in deep linear neural networks Link
• QUALITATIVELY CHARACTERIZING NEURAL NETWORK OPTIMIZATION PROBLEMS[Link]
• The Loss Surfaces of Multilayer Networks [Link]
• Visualizing the Loss Landscape of Neural Nets [Link]
• The Loss Surface Of Deep Linear Networks Viewed Through The Algebraic Geometry Lens [Link]
• How regularization affects the critical points in linear networks.[Link]
• Local minima in training of neural networks [Link]
• Necessary and Sufficient Geometries for Gradient Methods Link
• Fine-grained Optimization of Deep Neural Networks Link
• SCORE-BASED GENERATIVE MODELING THROUGH STOCHASTIC DIFFERENTIAL EQUATIONS Link

Dynamics, Bifurcations and RNNs difficulty to train

• Deep Equilibrium Models Link
• Bifurcations of Recurrent Neural Networks in Gradient Descent Learning [Link]
• On the difficulty of training recurrent neural networks [Link]
• Understanding and Controlling Memory in Recurrent Neural Networks [Link]
• Dynamics and Bifurcation of Neural Networks [Link]
• Context Aware Machine Learning [Link]
• The trade-off between long-term memory and smoothness for recurrent networks [Link]
• Dynamical complexity and computation in recurrent neural networks beyond their fxed point [Link]
• Bifurcations in discrete-time neural networks : controlling complex network behaviour with inputs [Links]
• Interpreting Recurrent Neural Networks Behaviour via Excitable Network Attractors [Link]
• Bifurcation analysis of a neural network model Link
• A Differentiable Physics Engine for Deep Learning in Robotics Link
• Deep learning for universal linear embeddings of nonlinear dynamics Link
• Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations Link
• Analysis of gradient descent learning algorithms for multilayer feedforward neural networks Link
• A dynamical model for the analysis and acceleration of learning in feedforward networks Link
• A bio-inspired bistable recurrent cell allows for long-lasting memory Link
• Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation [Link (https://www.frontiersin.org/articles/10.3389/fncom.2017.00024/full)

Poor Local Minima? and Sharp Minima

• Adding One Neuron Can Eliminate All Bad Local Minima Link
• Deep Learning without Poor Local Minima Link
• Elimination of All Bad Local Minima in Deep Learning Link
• How to escape saddle points efficiently. Link
• Depth with Nonlinearity Creates No Bad Local Minima in ResNets Link
• Sharp Minima Can Generalize For Deep Nets Link
• Asymmetric Valleys: Beyond Sharp and Flat Local Minima Link
• A Reparameterization-Invariant Flatness Measure for Deep Neural Networks Link
• A Simple Weight Decay Can Improve Generalization Link
• Finding Critical and Gradient-Flat Points of Deep Neural Network Loss Functions Link
• The Loss Surface Of Deep Linear Networks Viewed Through The Algebraic Geometry Lens Link
• Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization Link
• Flatness is a False Friend Link
• Are_Saddles_Good_Enough_for_Deep_Learning Link

Initialization of Neural Network

• Deep learning course notes Link
• On the importance of initialization and momentum in deep learning Link
• The Break-Even Point on Optimization Trajectories of Deep Neural Networks Link
• THE EARLY PHASE OF NEURAL NETWORK TRAINING Link
• One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers Link
• PCA-Initialized Deep Neural Networks Applied To Document Image Analysis Link
• Understanding the difficulty of training deep feedforward neural networks Link
• Unitary Evolution of RNNs Link

Momentum in Optimization

• RETHINKING THE HYPERPARAMETERS FOR FINE-TUNING Link
• Momentum Residual Neural Networks Link
• Smooth momentum: improving lipschitzness in gradient descent Link
• Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning link

Batch size Optimiation

• ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMALink
• Revisiting Small Batch Training for Deep Neural Networks Link
• LARGE BATCH TRAINING OF CONVOLUTIONAL NETWORKS Link
• Large Batch Optimization for Deep Learning: Training BERT in 76 minutes Link
• DON’T DECAY THE LEARNING RATE, INCREASE THE BATCH SIZE Link

Degeneracy of Neural Networks

• Exact solutions to the nonlinear dynamics of learning in deep linear neural networks Link
• Avoiding pathologies in very deep networks Link
• Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice Link
• SKIP CONNECTIONS ELIMINATE SINGULARITIES Link
• How degenerate is the parametrization of neural networks with the ReLU activation function? Link
• Theory of Deep Learning III: explaining the non-overfitting puzzle Link
• Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks Link
• Understanding Deep Learning: Expected Spanning Dimension and Controlling the Flexibility of Neural Networks Link
• The Loss Surface Of Deep Linear Networks Viewed Through The Algebraic Geometry Lens Link
• PYHESSIAN: Neural Networks Through the Lens of the Hessian Link

Convergencec Analysis in Deep Learning

• A CONVERGENCE ANALYSIS OF GRADIENT DESCENT FOR DEEP LINEAR NEURAL NETWORKS Link
• A Convergence Theory for Deep Learning via Over-Parameterization Link
• Convergence Analysis of Homotopy-SGD for Non-Convex Optimization Link

Multi-Task Learning with curricula

• Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning. Link
• Learning a Multitask Curriculum for Neural Machine Translation. Link
• Self-paced Curriculum Learning. Link
• Curriculum Learning of Multiple Tasks. Link

Constrained Optimization for Deep Learning

• A Primal-Dual Formulation for Deep Learning with Constraints Link

Reinforcement Learning and Curriculum

• Object-Oriented Curriculum Generation for Reinforcement Learning Link
• Teacher-Student Curriculum Learning Link

Tutorials, Surveys and Blogs

• Curriculum Learning: A Survey Link
• A Comprehensive Survey on Curriculum Learning Link
• https://www.offconvex.org/
• An overview of gradient descent optimization algorithms [Link]
• Review of second-order optimization techniques in artificial neural networks backpropagation Link
• Linear Algebra and data Link
• Why Momentum really works?[Blog]
• Optimization [Book]
• Optimization for deep learning: theory and algorithms Link
• Generalization Error in Deep Learning Link
• Automatic Differentiation in Machine Learning: a Survey Link
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey Link
• Automatic Curriculum Learning For Deep RL: A Short Survey Link
• The Generalization Mystery: Sharp vs Flat Minima Link

Contributing

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