Related papers: Enhancing the top signal at Tevatron using Neural …

This work theoretically investigates the performance of a composite neural network. A composite neural network is a rooted directed acyclic graph combining a set of pre-trained and non-instantiated neural network models, where a pre-trained…

I will briefly review the status of higher-order calculations for top-quark observables, comment on the need for improvements, discuss some of the recent theoretical advances, and present a few examples to highlight the role of top-quark…

We review the most recent results on top quark production at the Tevatron including the measurements of the top quark pair ($t \bar t$) production cross section, the forward-backward asymmetry, the spin correlations and the ratio of…

We study the constraints on models of topcolor-assisted technicolor arising from measurements of high-$E_T$ jets and high-mass lepton pairs at the Tevatron collider. Existing data can eliminate models that have appeared in the literature.

Tensor networks (TNs) have been gaining interest as multiway data analysis tools owing to their ability to tackle the curse of dimensionality and to represent tensors as smaller-scale interconnections of their intrinsic features. However,…

Neural networks have seen an explosion of usage and research in the past decade, particularly within the domains of computer vision and natural language processing. However, only recently have advancements in neural networks yielded…

Modularity has been widely studied as a mechanism to improve the capabilities of neural networks through various techniques such as hand-crafted modular architectures and automatic approaches. While these methods have sometimes shown…

Deep neural networks have usually to be compressed and accelerated for their usage in low-power, e.g. mobile, devices. Recently, massively-parallel hardware accelerators were developed that offer high throughput and low latency at low power…

Connections between integration along hypersufaces, Radon transforms, and neural networks are exploited to highlight an integral geometric mathematical interpretation of neural networks. By analyzing the properties of neural networks as…

We review the use of Monte Carlo simulation to model backgrounds to top signal at the Tevatron experiments, CDF and D0, as well as the relevant measurements done by the experiments. We'll concentrate on the modeling of W and Z boson…

Neural networks have been able to achieve groundbreaking accuracy at tasks conventionally considered only doable by humans. Using stochastic gradient descent, optimization in many dimensions is made possible, albeit at a relatively high…

The general strategy, as well as channel-specific details, applied to the measurement of the top quark mass at the Tevatron in Run I are reviewed, and the combination of the results obtained by the CDF and DO collaborations presented. The…

Most of exotic resonances observed in the past decade appear as peak structure near some threshold. These near-threshold phenomena can be interpreted as genuine resonant states or enhanced threshold cusps. Apparently, there is no…

This research uses deep learning to estimate the topology of manifolds represented by sparse, unordered point cloud scenes in 3D. A new labelled dataset was synthesised to train neural networks and evaluate their ability to estimate the…

We report on measurements of the ttbar production cross section at a center-of-mass energy of 1.96 TeV at the D0 experiment during Run II of the Fermilab Tevatron collider. We use candidate events in lepton+jets and dilepton final states.…

Single-lepton plus jets signals from $t\bar t$ production at hadron colliders generally give more spherically symmetrical events than the principal backgrounds from $W$ production. We show that sphericity and aplanarity criteria, applied to…

We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes. We represent shapes using an irregular tetrahedral grid which encodes an occupancy and displacement field. Our formulation enables defining…

Recently, Neural Networks have been proven extremely effective in many natural language processing tasks such as sentiment analysis, question answering, or machine translation. Aiming to exploit such advantages in the Ontology Learning…

Spectrum sensing is of critical importance in any cognitive radio system. When the primary user's signal has uncertain parameters, the likelihood ratio test, which is the theoretically optimal detector, generally has no closed-form…

We derive an exact representation of the topological effect on the dynamics of sequence processing neural networks within signal-to-noise analysis. A new network structure parameter, loopiness coefficient, is introduced to quantitatively…