24 papers · ranked by Valyu relevance
Flavio Ferrarotti, Klaus-Dieter Schewe
We motivate an axiomatic definition of probabilistic algorithms (PAs) by four postulates covering random branching time, abstract states, background, and random bounded exploration. Then, we introduce probabilistic Abstract State Machines (pASMs) and show that they specify PAs. Finally, we prove that every PA…
Philipp Hennig, Michael A. Osborne, Mark Girolami
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation…
Pierre Denis
We present here a new probabilistic inference algorithm that gives exact results in the domain of discrete probability distributions. This algorithm, named the Statues algorithm, calculates the marginal probability distribution on probabilistic models defined as direct acyclic graphs. These models are made up of…
Malte Rørmose Damgaard, Rasmus Pedersen, Thomas Bak
Title: Summary Inspired by the “cognitive hourglass” model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by…
Abdullah M. Algashami, Ali Safaa Sadiq
This research dealt with the problem of scheduling applied to the supercomputer’s execution. The goal is to develop an appreciated algorithm that schedules a group of several programs characterized by their time consuming very high on different supercomputers searching for an efficient assignment of the total running…
Ishita Dasgupta, Eric Schulz, Joshua B. Tenenbaum, Samuel J. Gershman
Bayesian theories of cognition assume that people can integrate probabilities rationally. However, several empirical findings contradict this proposition: human probabilistic inferences are prone to systematic deviations from optimality. Puzzlingly, these deviations sometimes go in opposite directions. Whereas some…
Karthekeyan Chandrasekaran, Richard M. Karp
We study the problem of learning a most biased coin among a set of coins by tossing the coins adaptively. The goal is to minimize the number of tosses until we identify a coin i ∗ whose posterior probability of being most biased is at least 1 − δ for a given δ. Under a particular probabilistic model, we give an optimal…
Jérémie Lumbroso
This article is a historical introduction to data streaming algorithms that was written as a companion piece to the talk "How Philippe Flipped Coins to Count Data", given on December 16th, 2011, in the context of the conference in honor of Philippe Flajolet and Analytic Combinatorics [6]. The narrative was pieced…
Cliff C. Kerr, Salvador Dura-Bernal, Tomasz G. Smolinski, George L. Chadderdon + 2 more
'George L. Chadderdon' 'David P. Wilson' 'Lars Kaderali'] When standard optimization methods fail to find a satisfactory solution for a parameter fitting problem, a tempting recourse is to adjust parameters manually. While tedious, this approach can be surprisingly powerful in terms of achieving optimal or near-optimal…
Tobias Røikjer, Asger Hobolth, Kasper Munch
Phase-type distributions model the time until absorption in continuous or discrete-time Markov chains on a finite state space. The multivariate phase-type distributions have diverse and important applications by modeling rewards accumulated at visited states. However, even moderately-sized state spaces make the…
Jian Li, Tianlin Shi
Given n independent integer-valued random variables X1, X2, ..., Xn and an integer C, we study the fundamental problem of computing the probability that the sum X = X1+X2+...+Xn is at most C. We assume that each random variable Xi is implicitly given by an oracle Oi, which given two input integers n1, n2 returns the…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Paul Masset, Jacob A. Zavatone-Veth, J. Patrick Connor, Venkatesh N. Murthy + 1 more
For animals to navigate an uncertain world, their brains need to estimate uncertainty at the timescales of sensations and actions. Sampling-based algorithms afford a theoretically-grounded framework for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in…
Brandon Alexander Holt, Gabriel A. Kwong
Engineered biocircuits that interface with living systems as plug-and-play constructs may enable new applications for programmable therapies and diagnostics. We create biological bits (bbits) using proteases – a family of pleiotropic, promiscuous enzymes – to construct the biological equivalent of Boolean logic gates…
S. Gerber, L. Pospisil, M. Navandar, I. Horenko
Finding reliable discrete approximations of complex systems is a key prerequisite when applying many of the most popular modeling tools. Common discretization approaches (for example, the very popular K-means clustering) are crucially limited in terms of quality and cost. We introduce a low-cost improved-quality…
Muhammad Sadeequllah, Azhar Rauf, Saif Ur Rehman, Noha Alnazzawi + 1 more
'Bilal Alatas'] Frequent itemset mining (FIM) is a major component in association rule mining, significantly influencing its performance. FIM is a computationally intensive nondeterministic polynomial time (NP)-hard problem. At the core of FIM is the task of computing support of candidate itemsets. This problem becomes…
Malcolm Pradhan, Paul Dagum
We present two Monte Carlo sampling algorithms for probabilistic inference that guarantee polynomial-time convergence for a larger class of network than current sampling algorithms provide. These new methods are variants of the known likelihood weighting algorithm. We use of recent advances in the theory of optimal…
Xiyuan Liu, Liying Wang, Hongyan Yan, Qingjiao Cao + 3 more
'Weiguo Zhao' 'Yongquan Zhou'] To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Authors not listed
We present a unified theoretical framework that classifies and analyzes quantum enhancement strategies for classical algorithms, establishing design paradigms that systematically combine quantum subroutines with classical procedures. The theory identifies four fundamental enhancement mechanisms: quantum search…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…