When Stuck, Flip a Coin : New Algorithms for Large-Scale Tasks

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Quantum computing not only holds the potential to solve long-standing problems in quantum physics, but also to offer speed-ups across a broad spectrum of other fields. Access to a computational space that incorporates quantum effects, such as superposition ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

The increasing demand for computing power and the emergence of heterogeneous computing architectures have driven the exploration of innovative techniques to address current limitations in both the compute and memory subsystems. One such solution is the use ...

Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models c ...

We study the proof theory and algorithms for orthologic, a logical system based on ortholattices, which have shown practical relevance in simplification and normalization of verification conditions. Ortholattices weaken Boolean algebras while having polyno ...

Technology mapping transforms a technology-independent representation into a technology-dependent one given a library of cells. This process is performed by means of local replacements that are extracted by matching sections of the subject graph to library ...

We perform an error analysis of a fully discretised Streamline Upwind Petrov Galerkin Dynamical Low Rank (SUPG-DLR) method for random time-dependent advection-dominated problems. The time integration scheme has a splitting-like nature, allowing for potenti ...

Robots outside of the fenced factories have to deal with continuously changing environment, this requires fast and flexible modes of control. Planning methods or complex learning models can find optimal paths in complex surroundings, but they are computati ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

The optimal pricing of goods, especially when they are new and the innovating firm is a monopolist, must proceed without precise knowledge of the demand curve. This paper provides a pricing method with a relative robustness guarantee by maximizing a perfor ...

When Stuck, Flip a Coin : New Algorithms for Large-Scale Tasks

Chat with Graph Search

Scalable Quantum Algorithms for Noisy Quantum Computers

Gibbs sampling the posterior of neural networks

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Intermediate Address Space: virtual memory optimization of heterogeneous architectures for cache-resident workloads

The complexity of quantum support vector machines

Orthologic with Axioms

In Medio Stat Virtus: Combining Boolean and Pattern Matching

Error estimates for SUPG-stabilised Dynamical Low Rank Approximations

Exact Obstacle Avoidance for Robots in Complex and Dynamic Environments Using Local Modulation

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Monopoly Pricing with Unknown Demand