ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.
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Optimization of hyper-parameters in reinforcement learning (RL) algorithms is a key task, because they determine how the agent will learn its policy by interacting with its environment, and thus what data is gathered. In this work, an approach that uses Bayesian optimization to perform a two-step optimization is propos…
We explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-the-art machine learning algorithms tested failed to learn. We motivate our work from the hypothesis that humans learn such intermediate concepts from other individuals via…
TransFall uses transfer learning to improve activity recognition from mobile sensors.
Model analyzes RFQ markets using stochastic control to optimize dealer performance and inventory.
We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any b…
Recently generative models have focused on combining the advantages of variational autoencoders (VAE) and generative adversarial networks (GAN) for good reconstruction and generative abilities. In this work we introduce a novel hybrid architecture, Implicit Discriminator in Variational Autoencoder (IDVAE), that combine…
Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive. Consider a visual arithmetic task, where the goal is to car…
Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when naively classifying each document via its corresponding language-specific classifier. In order to obtain an…
JIT liquidity providers can sometimes reduce overall market liquidity by crowding out passive LPs.
Bayesian nonparametric models for data with heterogeneous particles.
On December 16, Zynga, the well-known social game developing company went public. This event is following other recent IPOs in the world of social networking companies, such as Groupon, Linkedin or Pandora to cite a few. With a valuation close to 7 billion USD at the time when it went public, Zynga has become the bigge…
Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.
Early time series classification (eTSC) is the problem of classifying a time series after as few measurements as possible with the highest possible accuracy. The most critical issue of any eTSC method is to decide when enough data of a time series has been seen to take a decision: Waiting for more data points usually m…
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games. However, the real-world contains multiple agents, each learning and acting independently to cooperate and compete with other agents, a…
Novel fusion network combines polarization and radiomics features for liver cancer classification.
Hybrid AI and rule-based framework de-identifies medical imaging data.
On December 16th, 2011, Zynga, the well-known social game developing company went public. This event followed other recent IPOs in the world of social networking companies, such as Groupon or Linkedin among others. With a valuation close to 7 billion USD at the time when it went public, Zynga became one of the biggest …