Search Results for author:
Found 11 papers, 2 papers with code
Maintaining Adversarial Robustness in Continuous Learning
Adversarial robustness is essential for security and reliability of machine learning systems.
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations
Decentralized learning has emerged as an alternative method to the popular parameter-server framework which suffers from high communication burden, single-point failure and scalability issues due to the need of a central server.
Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating
MMFS, which possesses action recognition and action quality assessment, captures RGB, skeleton, and is collected the score of actions from 11671 clips with 256 categories including spatial and temporal labels.
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular parameter server framework.
Identifying Unique Causal Network from Nonstationary Time Series
HCE algorithm measures the strength of causality by using nearest-neighbors entropy estimator, which works well on nonstationary time series.
Network Topology Inference based on Timing Meta-Data
If the data timings and ACK timings of two nodes -- say node 1 and node 2, respectively -- are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1).
Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution
By integrating sufficient information from the front of the video to build the hidden state needed for the initially recurrent unit to help restore the earlier frames, the information prebuilt network balances the input information difference at different time steps.
Critical Learning Periods in Federated Learning
In this work, we show that the final test accuracy of FL is dramatically affected by the early phase of the training process, i. e., FL exhibits critical learning periods, in which small gradient errors can have irrecoverable impact on the final test accuracy.
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
In this paradigm, each worker maintains a local estimate of the optimal parameter vector, and iteratively updates it by waiting and averaging all estimates obtained from its neighbors, and then corrects it on the basis of its local dataset.
Visual Analytics of Anomalous User Behaviors: A Survey
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
Heterogeneous Strategy Particle Swarm Optimization
Our extensive numerical experiments show that HSPSO algorithm is able to obtain satisfactory solutions, outperforming both PSO and the fully informed PSO.
