Search Results for author:
Found 9 papers, 3 papers with code
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Graph Neural Networks (GNNs) are widely applied to graph learning problems such as node classification.
Calibrated Dataset Condensation for Faster Hyperparameter Search
We aim to generate a synthetic validation dataset so that the validation-performance rankings of the models, with different hyperparameters, on the condensed and original datasets are comparable.
A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)
We propose VecKM, a local point cloud geometry encoder that is descriptive and efficient to compute.
WAVES: Benchmarking the Robustness of Image Watermarks
Our evaluation examines two pivotal dimensions: the degree of image quality degradation and the efficacy of watermark detection after attacks.
conv_einsum: A Framework for Representation and Fast Evaluation of Multilinear Operations in Convolutional Tensorial Neural Networks
Modern ConvNets continue to achieve state-of-the-art results over a vast array of vision and image classification tasks, but at the cost of increasing parameters.
Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing
This allows devices to avoid maintaining (i) the fully-sized model and (ii) large amounts of hash tables in local memory for LSH analysis.
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
Furthermore, we provide theoretical results for IID and non-IID settings without any bounded-delay assumption for slow clients which is required by other asynchronous decentralized FL algorithms.
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching
Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge.
Practical and Fast Momentum-Based Power Methods
The power method is a classical algorithm with broad applications in machine learning tasks, including streaming PCA, spectral clustering, and low-rank matrix approximation.
