Sorry, you need to enable JavaScript to visit this website.

We address the problem of adding new classes to an existing classifier without hurting the original classes, when no access is allowed to any sample from the original classes. This problem arises frequently since models are often shared without their training data, due to privacy and data ownership concerns. We propose an easy-to-use approach that modifies the original classifier by retraining a suitable subset of layers using a linearly-tuned, knowledge-distillation regularization.

In this paper, we address the problem of bird audio detec-
tion and propose a new convolutional neural network archi-
tecture together with a divergence based information channel
weighing strategy in order to achieve improved state-of-the-
art performance and faster convergence. The effectiveness of
the methodology is shown on the Bird Audio Detection Chal-
lenge 2018 (Detection and Classification of Acoustic Scenes
and Events Challenge, Task 3) development data set.

Diversity plays a vital role in many text generating applications. In recent years, Conditional Variational Auto Encoders (CVAE) have shown promising performances for this task. However, they often encounter the so called KL-Vanishing problem. Previous works mitigated such problem by heuristic methods such as strengthening the encoder or weakening the decoder while optimizing the CVAE objective function. Nevertheless, the optimizing direction of these methods are implicit and it is hard to find an appropriate degree to which these methods should be applied.

The generalized command response (GCR) model represents intonation as a
superposition of muscle responses to spike command signals. We have previously
shown that the spikes can be predicted by a two-stage system, consisting of a recurrent neural network and a post-processing procedure, but the responses themselves were fixed dictionary atoms. We propose an end-to-end
neural architecture that replaces the dictionary atoms with trainable
second-order recurrent elements analogous to recursive filters. We demonstrate

1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts.

Speech-related Brain Computer Interfaces (BCI) aim primarily at finding an alternative vocal communication pathway for
people with speaking disabilities. As a step towards full decoding of imagined speech from active thoughts, we present a
BCI system for subject-independent classification of phonological categories exploiting a novel deep learning based

Road traffic forecasting systems are in scenarios where sensor or system failure occur. In those scenarios, it is known that missing values negatively affect estimation accuracy although it is being often underestimate in current deep neural network approaches. Our assumption is that traffic data can be generated from a latent space. Thus, we propose an online unsupervised data imputation method based on learning the data distribution using a variational autoencoder (VAE).