In this paper, we develop a deep learning-based general numerical method coupled with small sample learning (SSL) for solving PDEs. To be more specific, we approximate the solution via a deep feedforward neural network, which is trained to satisfy the PDEs with the initial and boundary ...
The most widely employed approaches to perform sentiment analysis include the Machine Learning/Deep Learning based method, lexicon-based method, and hybrid methodology [27,27,28,28,34,39,43,49]. The lexicon-based methodology makes use of a dictionary of words marked by sentiment to find out th...
In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for ...
This research demonstrated that deep learning method coupled with hyperspectral imaging technique can be used for rapid and nondestructive detecting firmness and SSC in Korla fragrant pear, which would be useful for postharvest fruit quality inspections....
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimagingMartin Priessner, David C. A. Gaboriau, Arlo Sheridan, Tchern Lenn, Carlos Garzon-Coral, Alexander R. Dunn, Jonathan R. Chubb, Aidan M. Tousley, Robbie G. Majzner, Uri ...
computational linguistics; deep learning; natural language understanding; intent detection; mayfly optimization1. Introduction With the development of the task-based dialogue mechanism, natural language understanding (NLU), as a critical element of the task-based dialogue system, has gained more interest ...
The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and ...
Robust deep learning based protein sequence design using ProteinMPNN[code][preprint] This work presents a method for designing a protein sequence that is predicted to fold into a specified conformation, i.e. in a way the reverse of AlphaFold: going from structure to sequence. This is achieved ...
proposed the first deep learn based PSSP method, called DNSS, and it was a deep belief network (DBN) model based on restricted Boltzmann machine (RBM) and trained by contrastive divergence46 in an unsupervised manner. The method used PSSM generated by PSI-BLAST to train deep learning network...
CNN-based • VAE-based • GAN-based • Transformer-based • Bayesian method • Reinforcement Learning • Flow-based • RNN-based • LSTM-based • Autoregressive • Boltzmann machine • Diffusion-based • GNN-based • Score-based 6) Function to Structure Review • LSTM...