Neural image synthesis has seen enormous advances in recent years, led by innovations in GANs which generate high-resolution, photo-realistic images. However, a major limitation of these methods is that they tend to capture texture statistics of an image with no explicit understanding of geometry. Additionally, GAN-only pipelines are notoriously ha...
We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling. More precisely, to design fair algorithms for as many sensitive variables as possible, including variables we might not be aware of, we assume no prior knowledge of sensitive variables: our algorithms use unsupervised fairness only, meaning...
Published in Drug discovery today
The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to ...
Published in Journal of computer-aided molecular design
Exploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive...
Deep learning allows computers to learn from observations, or else training data. Successful application development requires skills in neural network design, adequate computational resources, and a training data distribution that covers the application do-main. We are currently witnessing an artificial intelligence (AI) outbreak with enough computa...
Maximum-Likelihood Estimation (MLE) is a classic model-fitting method from probability theory. However, it has been argued repeatedly that MLE is inappropriate for synthesis applications, since its priorities are at odds with important principles of human perception, and that, e.g. Generative Adversarial Networks (GANs) are a more appropriate choic...
Published in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The promise of machine learning methods to act as decision support systems for pathologists continues to grow. However, central to their successful adoption must be interpretable implementations so that people can trust and learn from them effectively. Generative modeling, most notable in the form of adversarial generative models, is a naturally in...
Many methods for statistical inference and generative modeling rely on a probability divergence to effectively compare two probability distributions. The Wasserstein distance, which emerges from optimal transport, has been an interesting choice, but suffers from computational and statistical limitations on large-scale settings. Several alternatives...
Published in SN Computer Science
Variational Autoencoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks to efficiently solve the generation problem for high-dimensional data. The key insight of VAEs is to learn the latent distribution of data in such a way that new meaningful sa...
Published in Quantum Machine Intelligence
Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze (Benedetti et al, npj Quantum Inf 5:45, 2019; Hamilton et al, 2018; Zhu et al, 2018), a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer persp...
