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Found 9 papers, 8 papers with code
Effect of caffeine and energy drinks on sleep quality and brain activity in college students
This study investigates the impact of caffeine and energy drink consumption on sleep quality and brain activity among college students.
Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy
Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner.
Improving existing segmentators performance with zero-shot segmentators
We release with this paper the open-source implementation of our method.
Ranked #4 on
Camouflaged Object Segmentation
on CAMO
(MAE metric, using extra
training data)
Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition
Our consideration of Re-ID and action recognition as a multi-task problem results in a multi-branch 2D CNN architecture that outperforms prior work in the field (rank-1 (mAP) – MARS: 93. 21%(87. 23%), LPW: 79. 60%) without any reliance 3D convolutions or multi-stream networks architectures as found in other contemporary work.
Cosmic Background Removal with Deep Neural Networks in SBND
In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded.
Semantic Segmentation
Data Analysis, Statistics and Probability
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models training in reasonable time as well as distributing the storage requirements.
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules.
Machine Learning of Accurate Energy-conserving Molecular Force Fields
Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories.
