The following pages link to has companion code repository (P1687):
Displaying 50 items.
- Two-sample Test with Kernel Projected Wasserstein Distance (Q6360449) (← links)
- Robust Data-Driven Discovery of Partial Differential Equations under Uncertainties (Q6360461) (← links)
- User manual for bch, a program for the fast computation of the Baker-Campbell-Hausdorff and similar series (Q6360465) (← links)
- Model-based Prediction and Optimal Control of Pandemics by Non-pharmaceutical Interventions (Q6360469) (← links)
- An efficient method for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placemen (Q6360471) (← links)
- Proximal and Federated Random Reshuffling (Q6360484) (← links)
- Appearance of Random Matrix Theory in Deep Learning (Q6360488) (← links)
- Edge Minimizing the Student Conflict Graph (Q6360489) (← links)
- Learning low-rank latent mesoscale structures in networks (Q6360533) (← links)
- How Framelets Enhance Graph Neural Networks (Q6360535) (← links)
- Exploiting Shared Representations for Personalized Federated Learning (Q6360547) (← links)
- Decentralized Riemannian Gradient Descent on the Stiefel Manifold (Q6360549) (← links)
- Information flows of diverse autoencoders (Q6360618) (← links)
- Annealed Flow Transport Monte Carlo (Q6360639) (← links)
- CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback (Q6360641) (← links)
- WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points (Q6360648) (← links)
- Scaling Up Exact Neural Network Compression by ReLU Stability (Q6360694) (← links)
- HDMI: High-order Deep Multiplex Infomax (Q6360695) (← links)
- An Operator Theoretic Approach for Analyzing Sequence Neural Networks (Q6360697) (← links)
- Topological Graph Neural Networks (Q6360700) (← links)
- MARINA: Faster Non-Convex Distributed Learning with Compression (Q6360702) (← links)
- Local Hyper-Flow Diffusion (Q6360727) (← links)
- A General Descent Aggregation Framework for Gradient-based Bi-level Optimization (Q6360735) (← links)
- Making the most of your day: online learning for optimal allocation of time (Q6360756) (← links)
- Constructing Multiclass Classifiers using Binary Classifiers Under Log-Loss (Q6360769) (← links)
- An Information-Theoretic Justification for Model Pruning (Q6360794) (← links)
- Stochastic Variance Reduction for Variational Inequality Methods (Q6360802) (← links)
- IntSGD: Adaptive Floatless Compression of Stochastic Gradients (Q6360807) (← links)
- cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs (Q6360809) (← links)
- Adversarially Robust Kernel Smoothing (Q6360812) (← links)
- Functional Control of Oscillator Networks (Q6360825) (← links)
- Enhanced Modeling of Contingency Response in Security-constrained Optimal Power Flow (Q6360829) (← links)
- A Safety and Passivity Filter for Robot Teleoperation Systems (Q6360840) (← links)
- Multilevel Monte Carlo learning (Q6360855) (← links)
- Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes (Q6360881) (← links)
- Robust and Differentially Private Mean Estimation (Q6360926) (← links)
- Permutation-Based SGD: Is Random Optimal? (Q6361010) (← links)
- Efficient Riccati recursion for optimal control problems with pure-state equality constraints (Q6361015) (← links)
- Inferring the minimum spanning tree from a sample network (Q6361046) (← links)
- Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis (Q6361048) (← links)
- Fast and Accurate Uncertainty Quantification for the ECG with Random Electrodes Location (Q6361063) (← links)
- Distributed Bootstrap for Simultaneous Inference Under High Dimensionality (Q6361086) (← links)
- Channel Estimation and Data Detection Analysis of Massive MIMO with 1-Bit ADCs (Q6361105) (← links)
- Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables (Q6361140) (← links)
- GIST: Distributed Training for Large-Scale Graph Convolutional Networks (Q6361158) (← links)
- Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators (Q6361172) (← links)
- Dynamical Analysis of the EIP-1559 Ethereum Fee Market (Q6361180) (← links)
- Electromagnetic Model of Reflective Intelligent Surfaces (Q6361206) (← links)
- Representing and computing the B-derivative of an $EC^r$ vector field's $PC^r$ flow (Q6361214) (← links)
- A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization (Q6361215) (← links)
