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Found 20 papers, 4 papers with code
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
In this work, we for the first time, present a video compression-based degradation model to synthesize low-resolution image data in the blind SISR task.
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data.
Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix
Overall, our research highlights the importance of integrating feature and graph information alignment in GSL, as inspired by our derived theoretical result, and showcases the superiority of our approach in handling noisy graph structures through comprehensive experiments on real-world datasets.
Stability and Generalization of lp-Regularized Stochastic Learning for GCN
For a single-layer GCN, we establish an explicit theoretical understanding of GCN with the $\ell_p$-regularized stochastic learning by analyzing the stability of our SGD proximal algorithm.
MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding
In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns.
Stochastic Clustered Federated Learning
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices.
AP: Selective Activation for De-sparsifying Pruned Neural Networks
However, in network pruning, we find that the sparsity introduced by ReLU, which we quantify by a term called dynamic dead neuron rate (DNR), is not beneficial for the pruned network.
Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks
(ii) In addition to the strong theoretical motivation, SILO is empirically optimal in the sense of matching an Oracle, which exhaustively searches for the optimal value of max_lr via grid search.
Towards Better Long-range Time Series Forecasting using Generative Forecasting
In this paper, we propose a new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data.
Improving Mutual Information based Feature Selection by Boosting Unique Relevance
MRwMR-BUR-CLF further improves the classification performance by 3. 8%- 5. 5% (relative to MRwMR), and it also outperforms three popular classifier dependent feature selection methods.
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an honest-but-curious server.
Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring
Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert.
Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks
In this paper, we propose a new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data.
S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks
We explore a new perspective on adapting the learning rate (LR) schedule to improve the performance of the ReLU-based network as it is iteratively pruned.
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
To address this issue, we propose Bayesian Pseudocoresets Exemplar VAE (ByPE-VAE), a new variant of VAE with a prior based on Bayesian pseudocoreset.
Stein Variational Gradient Descent with Multiple Kernel
In practice, we notice that the kernel used in SVGD-based methods has a decisive effect on the empirical performance.
Deep Learning in Latent Space for Video Prediction and Compression
The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain.
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data
Lastly, we conduct an ablation study to demonstrate the effectiveness of the cWGAN-GEP and the ITC algorithm.
Long-range Prediction of Vital Signs Using Generative Boosting via LSTM Networks
Vital signs including heart rate, respiratory rate, body temperature and blood pressure, are critical in the clinical decision making process.
Feature Selection Based on Unique Relevant Information for Health Data
Feature selection, which searches for the most representative features in observed data, is critical for health data analysis.
