In this manuscript, we discuss the capabilities of a deep learning algorithm implemented with Conventional Neural Network concept to characterize hydraulic properties aquifers. The called CNN-HT is designed predict inverse operator tomography using synthetic training dataset in which head data associated pumping tests are linked transmissivity field. This approach relies on an adaptation SegNet...

classification of vegetation according to their species composition is one of the most important tasks in the application of remote sensing in precision agriculture. to prepare an algorithm for such a mandate, there is a need for ground truth. field operation is very costly and time consuming. therefore, some other method must be developed, such as extracting information from the satellite imag...

Humor generation is a very hard problem in the area of computational humor. In this paper, we present a joke generation model based on neural networks. The model can generate a short joke relevant to the topic that the user specifies. Inspired by the architecture of neural machine translation and neural image captioning, we use an encoder for representing user-provided topic information and an ...

In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, our multi-variable LSTM equipped with tensorized hidden states is dev...

Sparse coding represents a signal by a linear combination of only a few atoms of a learned over-complete dictionary. While sparse coding exhibits compelling performance for various machine learning tasks, the process of obtaining sparse code with fixed dictionary is independent for each data point without considering the geometric information and manifold structure of the entire data. We propos...

We propose a novel approach for constructing effective treatment policies when the observed data is biased and lacks counterfactual information. Learning in settings where the observed data does not contain all possible outcomes for all treatments is difficult since the observed data is typically biased due to existing clinical guidelines. This is an important problem in the medical domain as c...

Abstract The thermo-elastic tool center point (TCP) error has been an ongoing research focus, due to its large effect on the workpiece quality. Existing models compute TCP already perform quite well regarding accuracy and speed of computation. However, are often time consuming in their parameterization, expensive apply or error-prone used model inputs. work presented this paper addresses these ...

Programming by Example (PBE) targets at automatically inferring a computer program for accomplishing a certain task from sample input and output. In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems. Our NPBE model has four neur...

Modeling of large-scale research facilities is extremely challenging due to complex physical processes and engineering problems. Here, we adopt a data-driven approach model the longitudinal phase-space diagnostic beamline at photoinector European XFEL with an encoder-decoder neural network model. A deep convolutional (decoder) used build images measured on screen from small feature map generate...