Concept-aware clustering for decentralized deep learning under temporal shift

Decentralized deep learning requires dealing with non-iid data across clients, which may also change over time due to temporal shifts. While non-iid data has been extensively studied in distributed settings, temporal shifts have received no attention. To the best of our knowledge, we are first with tackling the novel and challenging problem of decentralized learning with non-iid and dynamic data. We propose a novel algorithm that can automatically discover and adapt to the evolving concepts in the network, without any prior knowledge or estimation of the number of concepts. We evaluate our algorithm on standard benchmark datasets and demonstrate that it outperforms previous methods for decentralized learning.Comment: 4 pages, 2 figure

Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

We study prediction of future outcomes with supervised models that use privileged information during learning. The privileged information comprises samples of time series observed between the baseline time of prediction and the future outcome; this information is only available at training time which differs from the traditional supervised learning. Our question is when using this privileged data leads to more sample-efficient learning of models that use only baseline data for predictions at test time. We give an algorithm for this setting and prove that when the time series are drawn from a non-stationary Gaussian-linear dynamical system of fixed horizon, learning with privileged information is more efficient than learning without it. On synthetic data, we test the limits of our algorithm and theory, both when our assumptions hold and when they are violated. On three diverse real-world datasets, we show that our approach is generally preferable to classical learning, particularly when data is scarce. Finally, we relate our estimator to a distillation approach both theoretically and empirically

Few-Shot Bioacoustic Event Detection Using an Event-Length Adapted Ensemble of Prototypical Networks

In this paper we study two major challenges in few-shot bioacoustic event detection: variable event lengths and false-positives. We use prototypical networks where the embedding function is trained using a multi-label sound event detection model instead of using episodic training as the proxy task on the provided training dataset. This is motivated by polyphonic sound events being present in the base training data. We propose a method to choose the embedding function based on the average event length of the few-shot examples and show that this makes the method more robust towards variable event lengths. Further, we show that an ensemble of prototypical neural networks trained on different training and validation splits of time-frequency images with different loudness normalizations reduces false-positives. In addition, we present an analysis on the effect that the studied loudness normalization techniques have on the performance of the prototypical network ensemble. Overall, per-channel energy normalization (PCEN) outperforms the standard log transform for this task. The method uses no data augmentation and no external data. The proposed approach achieves a F-score of 48.0% when evaluated on the hidden test set of the Detection and Classification of Acoustic Scenes and Events (DCASE) task 5

Few-shot bioacoustic event detection using a prototypical network ensemble with adaptive embedding functions

In this report we present our method for the DCASE 2022 challenge on few-shot bioacoustic event detection. We use an ensemble of prototypical neural networks with adaptive embedding functions and show that both ensemble and adaptive embedding functions can be used to improve results from an average F-score of 41.3% to an average F-score of 60.0% on the validation dataset