His research in Artificial Intelligence and Machine Learning has resulted in over 50 patents and research paper publications, establishing him as a thought leader in the field. Elizabeth Mixson Editor PEX Network We respect your privacy, by clicking ‘Watch On Demand’ you agree to...
The latter was further confirmed by the presence of significant differences between groups in gait timing (double and single support time, step and swing time) and gait regularity (both step and stride) on all three axes (Supplementary Table 1). This could overcome the need to train and ...
Double/Debiased Machine Learning for Dynamic Treatment Effects.Greg LewisVasilis Syrgkanis
Figure1illustrates the schematic of an active-learning algorithm by which an MLP is generated on the fly during the MD/tfMC simulation of graphene growth on a Cu (111) surface. The carbon-growth-on-metal machine-learning potential is henceforth dubbed CGM-MLP. The construction of CGM-MLP begi...
The number of backup workers b has a double effect on the convergence speed. The larger b is, the faster each iteration is, because the PS needs to wait less inputs from the workers. At the same time, the PS aggregates less information, so the model update is noisier and more iteratio...
To address the continuous production status and learn the most suitable actions (scheduling rules) at each rescheduling point, a Dueling Double Deep Q Network (D3QN) is developed to solve this problem. To improve the quality of the model solutions, a MachineRank algorithm (MR) is proposed, ...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - GitHub - double-stand/pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Existing unsupervised representation learning strategies stem from two concepts: self-supervision and pseudo-supervision. Proposed dynamic autoencoder model In this section, we present our double-stage deep clustering model. In Section 4.1, we describe the pretraining phase. Pretraining allows us to ...
and the relative energy difference with respect to the BCC ground statecalculated using our machine-learned spin-lattice potential (MSLP) for iron at non-magnetic (NM), ferromagnetic (FM), single layer antiferromagnetic (SL-AFM), and double layer antiferromagnetic (DL-AFM) states in BCC and FCC...
{V}}}^{l}\)represents a heavy atom and the aromatic, single, double, or triple bonds as the edges. The node features of the ligand graph include atomic number, chirality, degree, and formal charge. In addition to bond type, edge length embedding is also used as scalar edge features. ...