Neural Computation

By Mark J van Rossum. 1989

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Citations in the corpus

Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure (2017, neutral): “As we argue in the full paper, neither theoretical results [Siegelmann and Sontag, 1995], nor handcrafted examples [Gers and Schmidhuber, 2001; Rodriguez, 2001], nor large-scale NLP applications [Sutskever et al.”
Sparse coding with an overcomplete basis set: a strategy employed by V1? (1997, background): “Surely, there will be statistical dependencies among the elements of the single-stage model, and it would be desirable to have these modeled by a second and third stage in a hierarchical fashion (Dayan et al., 1995; Lewicki & Sejnowski, 1996).”
Adversarial Vulnerability of Neural Networks Increases With Input Dimension (2018, methodology): “Just as Tikhonov regularization is equivalent to training with random noise [Bis95], we show that our proposed regularization-scheme is equivalent at first order to training with adversarial noise, as proposed by [GSS14].”
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017, methodology): “Recently, long short-term memory units (LSTM) [Hochreiter and Schmidhuber, 1997] and the gated recurrent unit (GRU) [Cho et al.”
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019, background): “There has been a rich body of work [Belkin and Niyogi, 2003; Tang et al., 2015; Qiu et al., 2018] on node-level embeddings that turn each node in a graph into a vector preserving node-node proximity (similarity/distance).”
Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification (2020, methodology): “To better classify the nodes’ labels in the graph, based on the Laplacian smoothing assumption [Li et al., 2018; Xu et al., 2018b], the message passing models were proposed to aggregate the information from its connected neighbors in the graph, acquiring enough Training Inference Model Feature Label”
Recurrent Neural Network for Text Classification with Multi-Task Learning (2016, background): “Long short-term memory network (LSTM) was proposed by [Hochreiter and Schmidhuber, 1997] to specifically address this issue of learning long-term dependencies.”
Explicit Knowledge-based Reasoning for Visual Question Answering (2015, methodology): “, 2016] is based on directly connecting a convolutional neural network (CNN) to perform the image analysis, and a Long Short-Term Memory (LSTM) [Hochreiter and Schmidhuber, 1997] network to process the question and answer text.”
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks (2018, methodology): “We also perform experiments on the language modeling tasks to test our proposed algorithm on Long ShortTerm Memory (LSTM) network [Hochreiter and Schmidhuber, 1997], where adaptive gradient methods such as Adam are currently the mainstream optimizers for these tasks.”
Bilateral Multi-Perspective Matching for Natural Language Sentences (2017, methodology): “into a Long Short-Term Memory Network (LSTM) [Hochreiter and Schmidhuber, 1997], where the character embeddings are randomly initialized and learned jointly with other network parameters from NLSM tasks.”
Knowledge Aware Semantic Concept Expansion for Image-Text Matching (2019, methodology): “In our two-modality joint embedding network, we used LSTM with 1024 hidden units to encode text of images and a VGG19 [Simonyan and Zisserman, 2015] pre-trained by ImageNet as our image feature extractor.”
Open-Ended Long-form Video Question Answering via Adaptive Hierarchical Reinforced Networks (2018, background): “Since both the video and answer are sequential data with variant length, it it natural to choose the variant recurrent neural network called long-short term memory network (LSTM) [Hochreiter and Schmidhuber, 1997] to learn their feature representations by”
Bypassing the Kohn-Sham equations with machine learning (2016, neutral): “There has also been a recent spike of interest in applying machine learning (ML) methods in the physical sciences[7–11].”
Deep Neural Networks for High Dimension, Low Sample Size Data (2017, methodology): “We compare the proposed DNP method with three representative feature selection algorithms, including l1-penalized logistic regression (LogR-l1), gradient boosted feature selection (GBFS) [Xu et al., 2014], and HSIC-Lasso [Yamada et al., 2014].”