Research on decision support applications in healthcare, such as those related to diagnosis,
prediction, treatment planning, etc., has seen strongly growing interest in recent years. This …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

Image retrieval can be formulated as a ranking problem where the goal is to order database
images by decreasing similarity to the query. Recent deep models for image retrieval have …

Recent works have demonstrated that deep learning algorithms were efficient to conduct
security evaluations of embedded systems and had many advantages compared to the other …

Many natural language processing applications use language models to generate text.
These models are typically trained to predict the next word in a sequence, given the …

We present an approach to training neural networks to generate sequences using actor-
critic methods from reinforcement learning (RL). Current log-likelihood training methods are …

With the increasing popularity of machine learning techniques, it has become common to
see prediction algorithms operating within some larger process. However, the criteria by …

Generating adversarial examples is a critical step for evaluating and improving the
robustness of learning machines. So far, most existing methods only work for classification …

We study sequential decision making in environments where rewards are only partially
observed, but can be modeled as a function of observed contexts and the chosen action by …

Few-shot learning refers to understanding new concepts from only a few examples. We
propose an information retrieval-inspired approach for this problem that is motivated by the …