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nn ( excerpt )
⏲ 41 sec ✓ 16-Aug-2016
RNN: Rare Mystery 002
⏲ 78 sec ✓ 16-Jul-2021
Transformation Networks for Target-Oriented Sentiment Classification
⏲ 11 min 68 sec ✓ 20-Aug-2018
Funnel Webinar_RichRewards_RNN_01
⏲ 29 min 5 sec ✓ 17-May-2023
aRmeNN - Evolution of a SampleRNN Trained on Jungle Drum Breaks
⏲ 6 min 45 sec ✓ 08-Jan-2018
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
⏲ 15 min 8 sec ✓ 20-Aug-2018
[VIS18 Preview] RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Network on Electronic...
⏲ 30 sec ✓ 14-Sep-2018
Neural Kubrick Making of RNN
⏲ 64 sec ✓ 21-Sep-2017
RNN News (2002)
⏲ 1 min 36 sec ✓ 26-Feb-2011
An Interactive Web-Interface for Visualizing the Inner Workings of RNN
⏲ 1 min 45 sec ✓ 31-May-2018
[VIS19 Preview] TSR-TVD: Temporal Super-Resolution for Time-Varying Data Analysis and Visualization (scivis paper)
⏲ 31 sec ✓ 14-Sep-2019
Mouthpiece
⏲ 79 sec ✓ 20-Jun-2022
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_nn-excerpt.webp "installation during my residency @massmoca summer 2016.nnnn shows a physical system that is an embodiment of a neural network in both sound and form. The sounds were rendered using recurrent neural networks (RNN) to synthesize sounds. In this type of machine learning, recursive neural nets literally teach the computer how to produce sounds representative of the machines themselves. As such, this piece focuses on a corpus of field-recorded sounds such as industrial drones, refrigerator hums, cons")
_rnn-rare-mystery-002.webp "RNN: Rare Mystery 002 from rnn")
_transformation-networks-for-target-oriented-sentiment-classification.webp "Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model that achieves new state-of-the-art results on a few benchmarks. Instead of attentio")
_funnel-webinar-richrewards-rnn-01.webp "Funnel Webinar_RichRewards_RNN_01 from rnn")
_armenn-evolution-of-a-samplernn-trained-on-jungle-drum-breaks.webp "Audio generated by a SampleRNN trained on jungle drumbreaks, visualised using sndpeek (http://www.gewang.com/software/sndpeek/), run for around 50000 iterations and sampled every 516. Using Richard Aller's Torch implementation https://github.com/richardassar/SampleRNN_torch")
_the-best-of-both-worlds-combining-recent-advances-in-neural-machine-translation.webp "The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by the more recent Transformer model. Each of these new approaches consists of a fun-damental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures. In this paper, we tease a")
_vis18-preview-retainvis-visual-analytics-with-interpretable-and-interactive-recurrent-neural-network-on-electronic.webp "Authors: Bum Chul Kwon, Min-Je Choi, Joanne Kim, Edward Choi, Young Bin Kim, Soonwook Kwon, Jimeng Sun, Jaegul ChoonnAbstract: In the past decade, we have seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients’ diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often very challenging for users to und")
_neural-kubrick-making-of-rnn.webp "Neural Kubrick Making of RNN from rnn")
_rnn-news-2002.webp "RNN News (2002) from rnn")
_an-interactive-web-interface-for-visualizing-the-inner-workings-of-rnn.webp "0:00 - 1:10 General view: A fixed question-answers pair from SemEval 2017 Task 3 Subtask A dataset is given. A deep learning model is Siamese Bi-LSTM with attention. The objective is to map the correct answers closer to the question than the incorrect ones. A user can iterate through QA-pairs with the navigation buttons.n1. t-SNE plot of the LSTM's output embeddings on the question (blue dot), correct answers (green) and incorrect answers (red). The user can change the perplexity parameter.n2. I")
_vis19-preview-tsr-tvd-temporal-super-resolution-for-time-varying-data-analysis-and-visualization-scivis-paper.webp "Authors: Jun Han, Chaoli WangnnAbstract: We present TSR-TVD, a novel deep learning framework that generates temporal super-resolution (TSR) of time-varying data (TVD) using adversarial learning. TSR-TVD is the first work that applies the recurrent generative network (RGN), a combination of the recurrent neural network (RNN) and generative adversarial network (GAN), to generate temporal high-resolution volume sequences from low-resolution ones. The design of TSR-TVD includes a generator and a dis")
_mouthpiece.webp "Mouthpiece was exhibited at the Collegium Hungaricum Berlin from May 26-29, 2022, as part of the Sound Studies and Sonic Arts Master Exhibition of the Universität der Künste Berlin. nnIt consists of five sculptural elements, each approximately twenty centimetres in height, which act as acoustic filters for a spatial composition. These objects are 3D printed models of a woman’s throat, taken from MRI scans of her vocal tract engaged in the production of five German vowel sounds. Their exterio")
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