Federated learning has emerged as a new paradigm of collaborative machine learning; however, it has also faced several challenges such as non-independent and identically distributed(IID) data and high communication cost.
In the seeding phase, the network is updated using all the samples to collect a seed of clean samples.
Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much consideration of the communication scalability or the diurnal property relied on clients' local time variety.
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data.
Compared with existing batch selection methods, the results showed that Recency Bias reduced the test error by up to 20. 97% in a fixed wall-clock training time.
In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.
Learning a good distance measure for distance-based classification in time series leads to significant performance improvement in many tasks.
Owing to the extremely high expressive power of deep neural networks, their side effect is to totally memorize training data even when the labels are extremely noisy.
Ranked #3 on Learning with noisy labels on ANIMAL
Neural networks can converge faster with help from a smarter batch selection strategy.
The recent adoption of recurrent neural networks (RNNs) for session modeling has yielded substantial performance gains compared to previous approaches.