Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

By Farshid Amirabdollahian, Kerstin Dautenhahn, C. Dixon, K. Eder, Michael Fisher, Kheng Lee Koay, E. Magid, Tony Pipe, Maha Salem, J. Saunders and 1 more. 2013

1,812 citations recorded by the source corpus.

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Citations in the corpus

DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019, background): “Because of this unique characteristic of protein structures, it is yet to be fully understood how useful deep architectures such as U-Nets [16] can be for protein contact prediction although some groups have developed contact prediction methods using such architectures [2].”
Towards Characterizing Divergence in Deep Q-Learning (2019, methodology): “…convergence guarantees, although some convergence results (Yang et al., 2019) have been obtained for a closely-related algorithm called Neural-Fitted Q Iteration (Riedmiller, 2005) when used with deep ReLU networks. is the neural tangent kernel (NTK) (Jacot et al., 2018), and αg = θ (cid:48) − θ .”
A Survey on Data Augmentation for Text Classification (2021, unknown): “Data augmentation may help to enhance the amount of data for a certain class in order for balanced class distributions to be present and thus for a classifier to be able to be modeled more robustly [28, 29].”
Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation (2016, methodology): “As mentioned in previous work [15,27], two measures are required to ensure that FV has excellent performance.”
Deep Learning--based Text Classification (2020, unknown): “NLI, also known as recognizing textual entailment (RTE), predicts whether the meaning of one text can be inferred from another.”
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2021, unknown): “They demonstrated high performance for both segmentation and localization, by adding the attention gates to a UNET (Ronneberger et al. (2015)) and a variant of VGG (Simonyan and Zisserman (2014)).”
Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, background): “However, conventional biopsy techniques are painful, time-consuming, and fraught with inaccuracy in sampling [13,14].”
Could a Neuroscientist Understand a Microprocessor? (2016, unknown): “This includes ways of letting the networks paint 479 images [66] and ways of plotting the optimal stimuli for various areas [67].”
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, background): “On the other hand, a few studies such as Cobos et al. (2013) used reference ontology, Bahmani (2012) used both generic and domain ontologies while Vesin et al.”
The rise of artificial intelligence in healthcare applications (2020, background): “cesses of users via a time-stamped sequence of events and in this way allows capturing long-term dependencies [41].”
Privacy-Preserving Bandits (2019, methodology): “We refer the interested reader to the rigorous analysis and proofs provided in the original paper (Gehrke et al., 2012).”
Slime mould algorithm: A new method for stochastic optimization (2020, unknown): “The MAs used for comparison include well-regarded and recent ones: WOA [50], GWO [21], MFO [23], BA [20], SCA [7], FA[51], PSO[18], SSA [52], MVO [3], ALO”
Cost-sensitive decision tree ensembles for effective imbalanced classification (2014, unknown): “Therefore, it is not possible to use support functions [45] and we consequently revert to a weighted voting approach which has been shown to behave better than canonical voting methods [43].”
Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, background): “Furthermore, different approaches have been proposed to explain the behaviour of machine learning approaches post-hoc, these include (Samek and Müller, 2019):”