7 citations · 7 across the 2 of their papers we have counts for
7 papers
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning
Anastasia Koloskova, Tao Lin, Sebastian U. Stich
We consider decentralized machine learning over a network where the training data is distributed across agents, each of which can compute stochastic model updates on their loca…
Representation Memorization for Fast Learning New Knowledge without Forgetting
Fei Mi, Tao Lin, Boi Faltings
The ability to quickly learn new knowledge (e.g. new classes or data distributions) is a big step towards human-level intelligence. In this paper, we consider scenarios that requir…
Consensus Control for Decentralized Deep Learning
Lingjing Kong, Tao Lin, Anastasia Koloskova +2
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal…
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich +1
Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of het…
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
Jian Peng, Bo Tang, Hao Jiang +4
Artificial neural networks face the well-known problem of catastrophic forgetting. What's worse, the degradation of previously learned skills becomes more severe as the task sequen…
Multi-variable LSTM neural network for autoregressive exogenous model
Tian Guo, Tao Lin
In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mec…