The following pages link to has companion code repository (P1687):
Displaying 50 items.
- Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning (Q6318680) (← links)
- Shape optimization through proper orthogonal decomposition with interpolation and dynamic mode decomposition enhanced by active subspaces (Q6318694) (← links)
- Approximating Orthogonal Matrices with Effective Givens Factorization (Q6318741) (← links)
- A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems (Q6318772) (← links)
- Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning (Q6318817) (← links)
- On the Automatic Parameter Selection for Permutation Entropy (Q6318831) (← links)
- Moment-based Estimation of Mixtures of Regression Models (Q6318836) (← links)
- Inexact Newton Methods for Stochastic Nonconvex Optimization with Applications to Neural Network Training (Q6318870) (← links)
- Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables (Q6318878) (← links)
- Functorial Question Answering (Q6318925) (← links)
- Randomization of Approximate Bilinear Computation for Matrix Multiplication (Q6318932) (← links)
- Adaptively Truncating Backpropagation Through Time to Control Gradient Bias (Q6318938) (← links)
- Trajectory Optimization on Manifolds: A Theoretically-Guaranteed Embedded Sequential Convex Programming Approach (Q6318975) (← links)
- Locally Differentially Private Frequency Estimation with Consistency (Q6319063) (← links)
- Compression with Flows via Local Bits-Back Coding (Q6319088) (← links)
- Deep Energy-Based Modeling of Discrete-Time Physics (Q6319106) (← links)
- Distributionally Robust Formulation and Model Selection for the Graphical Lasso (Q6319145) (← links)
- MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders (Q6319147) (← links)
- Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms (Q6319156) (← links)
- Fine-grained Optimization of Deep Neural Networks (Q6319157) (← links)
- MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling (Q6319215) (← links)
- Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces (Q6319220) (← links)
- Revisiting Graph Neural Networks: All We Have is Low-Pass Filters (Q6319231) (← links)
- Acceleration of SVRG and Katyusha X by Inexact Preconditioning (Q6319248) (← links)
- Network Density of States (Q6319254) (← links)
- Efficient Reduction in Shape Parameter Space Dimension for Ship Propeller Blade Design (Q6319261) (← links)
- Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis (Q6319275) (← links)
- On Recurrent Neural Networks for Sequence-based Processing in Communications (Q6319280) (← links)
- Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates (Q6319283) (← links)
- Momentum-Based Variance Reduction in Non-Convex SGD (Q6319287) (← links)
- Optimal nonparametric change point detection and localization (Q6319288) (← links)
- Semi-Parametric Efficient Policy Learning with Continuous Actions (Q6319303) (← links)
- Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks (Q6319315) (← links)
- Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator (Q6319323) (← links)
- Minimax Rates of Estimating Approximate Differential Privacy (Q6319325) (← links)
- Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension (Q6319330) (← links)
- A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks (Q6319338) (← links)
- Variational Bayes under Model Misspecification (Q6319395) (← links)
- Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback (Q6319405) (← links)
- Graph Filtration Learning (Q6319414) (← links)
- A Rate-Distortion Framework for Explaining Neural Network Decisions (Q6319421) (← links)
- Practical and Consistent Estimation of f-Divergences (Q6319425) (← links)
- An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums (Q6319461) (← links)
- ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls (Q6319469) (← links)
- Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates (Q6319495) (← links)
- Why gradient clipping accelerates training: A theoretical justification for adaptivity (Q6319511) (← links)
- Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem (Q6319512) (← links)
- Competitive Gradient Descent (Q6319536) (← links)
- Optimal approximation for unconstrained non-submodular minimization (Q6319543) (← links)
- A Topology Layer for Machine Learning (Q6319554) (← links)
