Publications

Manual segmentation of brain tumor is often time consuming and the performance of the segmentation varies based on the operators experience. This leads to the requisition of a fully automatic method for brain tumor segmentation. In this paper, we propose the usage of the 100 layer Tiramisu …

Cylindrical Algebraic Decomposition (CAD) is an important tool within computational real algebraic geometry, capable of solving many problems for polynomial systems over the reals. It has long been studied by the Symbolic Computation community and has found recent interest in the Satisfiability …

Vast reinforcement learning (RL) research groups, such as DeepMind and OpenAI, have their internal (private) reinforcement learning codebases, which enable quick prototyping and comparing of ideas to many state-of-the-art (SOTA) methods. We argue the five fundamental properties of a sophisticated …

Neural networks are extremely flexible models due to their large number of parameters, which is beneficial for learning, but also highly redundant. This makes it possible to compress neural networks without having a drastic effect on performance. We introduce targeted dropout, a strategy for post …

This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of …

We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex …

Ageing effect on pulse wave velocity (PWV) in a single arterial bifurcation is studied by means of a 0D distributed vascular model. A Gaussian process emulator is trained on a small set of simulations, and PWV is computed at system inlet. The emulator is used to predict PWV for a distribution of …

It is well known that neural networks are susceptible to adversarial perturbations and are also computationally and memory intensive which makes it difficult to deploy them in real-world applications where security and computation are constrained. In this work, we aim to obtain both robust and …

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the …

Following off-road trails is somewhat more complex than following man-made roads. Trails are unstructuredand typically lack standard markers that characterize roadways. Nevertheless, trails can provide an effectiveset of pathways for off-road navigation. Here we approach the problem of trail …

Trails are unstructured and typically lack standard markers that characterize roadways; nevertheless, trailscan provide an effective set of pathways for off-road navigation. Here we approach the problem of trailfollowing by identifying the deviation of the robot from the heading …

One‐dimensional models of the cardiovascular system can capture the physics of pulse waves but involve many parameters. Since these may vary among individuals, patient‐specific models are difficult to construct. Sensitivity analysis can be used to rank model parameters by their effect on outputs and …

Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given …

Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming …

Cerebral vasospasm (CVS) is a life-threatening condition that occurs in a large proportion of those affected by subarachnoid haemorrhage and stroke. CVS manifests itself as the progressive narrowing of intracranial arteries. It is usually diagnosed using Doppler ultrasound, which quantifies blood …

Cerebral vasospasm (CVS) is the progressive narrowing of cerebral arteries following a haemorrhage in the brain. The primary diagnostic method (Transcranial Doppler) quantifies alterations of blood velocities in the affected vessels, but has low sensitivity when the condition affects the peripheral …

Real-time bidding, as one of the most popular mechanisms for selling online ad slots, facilitates advertisers to reach their potential customers. The goal of bidding optimization is to maximize the advertisers' return on investment (ROI) under a certain budget setting. A straightforward solution is …

Clustering is one of the most fundamental unsupervised tasks in machine learning and is elementary in the exploration of high volume data. Recent works propose using deep neural networks for clustering, owing to their ability to learn powerful representations of the data. In this work, we present a …

Following off-road trails is somewhat more complex than following man-made roads. Trails are unstructuredand typically lack standard markers that characterize roadways. Nevertheless, trails can provide an effectiveset of pathways for off-road navigation. Here we approach the problem of trail …

Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we …

Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a …

Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.

Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck, as one needs to store the activations in order to calculate gradients …

In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style …