Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking. However, automated pupil detection in real-world scenarios has proven to be an intricate challenge due to fast illumination changes, pupil occlusion, non centered and off-axis eye recording, and physiological eye characteristics. In this paper, we propose and evaluate a method ba...

Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques can make use of the increasing amounts of structural data that are becoming publicly available. Convolutional neural network (CNN) scoring functions...

In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a ‘mapping’ between noisy speech spectra and clean speech spectra via supervised learning. Specifically, we propose using ful...

The present paper discusses the use of deep learning techniques, in particular a convolutional neural network, which is trained to identify, in an image, a surgical cutting tool located on a plane. Initially a database is established regarding the tool with different rotations and after this, the base structure of the convolutional network for its training is determined. It is possible to obtai...

The goal of the present study is to explore the application of deep convolutional network features to emotion recognition. Results indicate that they perform similarly to recently published models at a best recognition rate of 94.4%, and do so with a single still image rather than a video stream. An implementation of an affective feedback game is also described, where a classifier using these f...

In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning and linguistic structures, we propose a tree-based convolutional neural network model which exploit various long-distance relationships betwee...

We show that a deep convolutional network with an architecture inspired by the models used in image recognition can yield accuracy similar to a long-short term memory (LSTM) network, which achieves the state-of-the-art performance on the standard Switchboard automatic speech recognition task. Moreover, we demonstrate that merging the knowledge in the CNN and LSTM models via model compression fu...

We describe an attention-based convolutional neural network for the English semantic textual similarity (STS) task in the SemEval2016 competition (Agirre et al., 2016). We develop an attention-based input interaction layer and incorporate it into our multiperspective convolutional neural network (He et al., 2015), using the PARAGRAM-PHRASE word embeddings (Wieting et al., 2016) trained on parap...