File list

Jump to navigation Jump to search

This special page shows all uploaded files.

File list
First pagePrevious pageNext pageLast page
Date Name Thumbnail Size User Description Versions
23:59, 10 February 2019 2003 TowardsaTheoryofNaturalLanguage Fig4.png (file) 57 KB Omoreira Figure4: PRECISE System Architecture.In: Popescu et al. (2003). 1
23:19, 10 February 2019 2005 NaLIXAnInteractiveNaturalLangua Fig1.png (file) 84 KB Omoreira Figure 1: Architecture of NaLIX. In: Li et al. (2005). 1
08:06, 10 February 2019 2008 NaturalLanguageDatabaseInterfac Fig1.png (file) 64 KB Omoreira Figure 1: Architectural Design of NLDBI-CBMS. In: Garcia et al. (2008) 1
06:48, 10 February 2019 2014 ConstructingAnInteractiveNatura Fig2.png (file) 42 KB Omoreira Figure 2: System Architecture. In: Li & Jagadish (2014). 1
23:42, 20 January 2019 2016 ScalingMemoryAugmentedNeuralNet Fig5.png (file) 49 KB Omoreira Figure 5: A schematic of the memory efficient backpropagation through time. Each circle represents an instance of the SAM core at a given time step. The grey box marks the dense memory. Each core holds a reference to the single [[instan... 1
20:57, 20 January 2019 2016 DynamicNeuralTuringMachinewithS Fig1.png (file) 57 KB Omoreira Figure 1: A graphical illustration of the proposed dynamic neural Turing machine with the recurrent-controller. The controller receives the fact as a continuous vector encoded by a recurrent neural network, computes the [[re... 1
23:43, 13 January 2019 2014 NeuralTuringMachines Fig1.png (file) 26 KB Omoreira '''Figure 1: Neural Turing Machine Architecture.''' During each update cycle, the controller network receives inputs from an external environment and emits outputs in response. It also reads to and writes fr... 1
07:22, 13 January 2019 2009 LearningClassifierSystemsACompl Fig1.png (file) 52 KB Omoreira Figure 1: Field tree—foundations of the LCS community. In: Urbanowicz & Moore, (2009) 1
14:46, 6 January 2019 2018 RethinkingMachineLearningDevelo Fig2.png (file) 44 KB Omoreira <i>Figure 2: Proposed ML development and deployment approach. The shaded blocks are vendor-specific parts</i> In: Lai & Suda, (2018) 1
14:41, 6 January 2019 2018 RethinkingMachineLearningDevelo Fig1.png (file) 46 KB Omoreira <i>Figure 1: Current ML development and deployment approach. The shaded blocks are vendor-specific parts.</i> In: Lai & Suda, (2018) 1
00:21, 31 December 2018 2015 TeachingMachinestoReadandCompre Fig1.png (file) 77 KB Omoreira Figure 1: Document and query embedding models. In: Hermann et al., (2015). 1
23:04, 30 December 2018 2016 LSTMbasedDeepLearningModelsforN Fig3.png (file) 30 KB Omoreira Figure 3: QA-LSTM with attention. In: Tan et al., (2016) 1
22:46, 30 December 2018 2016 LSTMbasedDeepLearningModelsforN Fig2.png (file) 29 KB Omoreira Figure 2: QA-LSTM/CNN.In: Tan et al., 2016 1
22:01, 30 December 2018 2016 LSTMbasedDeepLearningModelsforN Fig1.png (file) 22 KB Omoreira Figure 1: Basic Model: QA-LSTM. In: Tan et al., 2016 1
08:49, 30 December 2018 2015 DeepQuestionAnsweringforProtein Fig2.png (file) 119 KB Omoreira Figure 2.</B> Deep QA. In standard QA, answers are extracted from some retrieved documents. In Deep QA, curated data are exploited to build a supervised classification model, which is then used to generate answers. In:[[... 1
23:28, 29 December 2018 2015 DeepQuestionAnsweringforProtein Fig3.png (file) 190 KB Omoreira <B>Figure 3.</B> Overall workflow of the EAGLi platform. The input is a question formulated in natural language, the output is a set of candidate answers extracted from a set of retrieved MEDLINE abstracts. In: [[2015_De... 1
17:55, 16 December 2018 2016 EIEEfficientInferenceEngineonCo Fig1.png (file) 48 KB Omoreira Figure 1. Efficient inference engine that works on the compressed deep neural network model for machine learning applications. In: Han et al. (2016) 1
16:50, 16 December 2018 2016 DeepCompressionCompressingDeepN Fig1.png (file) 74 KB Omoreira Figure 1: The three stage compression pipeline: pruning, quantization and Huffman coding. Pruning reduces the number of... 1
11:03, 16 December 2018 2017 MemoryEfficientImplementationof Fig3.png (file) 105 KB Omoreira Figure 3: DenseNet layer forward pass: original implementation (left) and efficient implementation (right). Solid boxes correspond to tensors all... 1
10:05, 16 December 2018 2017 DenselyConnectedConvolutionalNe Fig1.png (file) 67 KB Omoreira Figure 1: A 5-layer dense block with a growth rate of <math>\kappa = 4</math>. Each layer takes all preceding feature-maps as input. In: Huang et al. (2017) 1
16:34, 8 December 2018 2006 SoundandEfficientInferencewithP Algorithm1.png (file) 43 KB Omoreira Algorihtm 1 &copyright; Poon & Domingos, 2006 1
00:03, 26 November 2018 2018 ProvidingASimpleQuestionAnsweri Fig1.png (file) 32 KB Omoreira Figure 1: Overall data flow for our Question ⇒ Question system. In: Larson et al. (2018) 1
08:25, 25 November 2018 2002 MaximumCommonSubgraphIsomorphis Fig1b.png (file) 16 KB Omoreira <P>Figure 1. b) Maximum common edge subgraph.. In: Raymond & Willett (2002). 1
08:24, 25 November 2018 2002 MaximumCommonSubgraphIsomorphis Fig1a.png (file) 15 KB Omoreira <P>Figure 1. a) Maximum common induced subgraph. In: Raymond & Willett (2002). 1
07:33, 25 November 2018 2002 MaximumCommonSubgraphIsomorphis Fig2b.png (file) 17 KB Omoreira Figure 2. b) Disconnected MCES. In: Raymond & Willett (2002) 1
07:33, 25 November 2018 2002 MaximumCommonSubgraphIsomorphis Fig2a.png (file) 17 KB Omoreira Figure 2. a) Connected MCES. In: Raymond & Willett (2002) 1
04:29, 24 November 2018 2002 MaximumCommonSubgraphIsomorphis Fig4.png (file) 16 KB Omoreira Figure 4.</B> MCS algorithm classification. In: Raymond & Willett (2002). 1
23:56, 18 November 2018 2016 MetaLearningwithMemoryAugmented Fig1.png (file) 77 KB Omoreira '''Figure 1'''. Task structure. (a) Omniglot images (or x-values for regression), <math>x_t</math>, are presented with time-offset labels (or function values), <math>y_{t−1}</math>, to prevent the network from simp... 1
22:48, 18 November 2018 2016 HierarchicalAttentionNetworksfo Fig2.png (file) 52 KB Omoreira '''Figure 2:''' Hierarchical Attention Network. In: Yang et al., (2016) 1
05:15, 18 November 2018 2016 BidirectionalRecurrentNeuralNet Fig1.png (file) 23 KB Omoreira Figure 1: Description of the model predicting punctuation <math>y_t</math> at time step <math>t</math> for the slot before the current input word <math>x_t</math>. In: Tilk & Alumae (2016) 1
06:41, 15 November 2018 2015 BayesianMarkovBlanketEstimation Fig2.png (file) 137 KB Omoreira <i><B>Figure 2:</B> One exemplary Markov blanket <math>(p = 10, q = 90)</math> and its reconstruction by BGL and BMB. Note that the graphs only display edges between <math>p</math> query and <math>q</math> remaining variable... 1
06:09, 15 November 2018 2006 Kugala Chap4 Fig1.png (file) 40 KB Omoreira Figure 4.1: Example of Markov Blanket for node <math>X_i</math>. In: Tomasz Kulaga. (2006).[https://pdfs.semanticscholar.org/cc15/5a644386d8a86ed1124d330e3655e929fe7e.pdf "The Markov Blanket Concept in Bayesian Networks and Dynamic Baye... 1
22:31, 14 November 2018 2001 AMachLearnApprToCorefResOfNounPhrases Fig1.png (file) 48 KB Omoreira <B>Figure 1</B> System architecture of natural language processing pipeline. In: Soon et al., (2001) 1
19:57, 11 November 2018 2016 NeuralGenerativeQuestionAnsweri Fig1.png (file) 50 KB Omoreira Figure 1: System diagram of GENQA. In: Yin et al., (2016) 1
17:01, 11 November 2018 2017 AutomaticQuestionAnsweringUsing Fig4.png (file) 97 KB Omoreira '''Fig. 4.''' The block-diagram of the proposed similarity network. IN: Minaee & Liu, 2017 1
23:31, 4 November 2018 Fader et al 2014 Fig1.png (file) 51 KB Omoreira Figure 1: OQA automatically mines millions of operators (left) from unlabeled data, then learns to compose them to answer questions (right) using evidence from multiple knowledge bases. In: Anthony Fader, [[Luke Zet... 1
23:51, 28 October 2018 diags-figure1.png (file) 32 KB Omoreira   1
23:50, 28 October 2018 diags-figure0.png (file) 7 KB Omoreira   1
23:24, 28 October 2018 Jurasky Martin 2018 Chap9 Fig5.png (file) 22 KB Omoreira <B>Figure 9.5</B> Forward inference in a simple recurrent network. In:Daniel Jurafsky, and James H. Martin (2018). [https://web.stanford.edu/~jurafsky/slp3/9.pdf "Chapter 9 -- Sequence Processing with Recurrent Networks"]. In: [https:/... 1
23:20, 28 October 2018 Jurasky Martin 2018 Chap9 Fig4.png (file) 38 KB Omoreira <B>Figure 9.4</B> A simple recurrent neural network shown unrolled in time. Network layers are copied for each timestep, while the weights <i>U</i>, <i>V</i> and <i>W</i> are shared in common across all timesteps. In: [[Da... 1
15:51, 28 October 2018 Ting et al 2017 LWRControl Fig1.png (file) 103 KB Omoreira .In: Jo-Anne Ting, Franzisk Meier, Sethu Vijayakumar, and Stefan Schaal (2017) [https://link.springer.com/referenceworkentry/10.1007/978-1-4899-7687-1_493 "Locally Weighted Regression for Control"]. In: Sammut & Webb (2017). <P><B>... 1
20:59, 21 October 2018 Jurafsky Martin 2018 Chap9 Fig14.png (file) 29 KB Omoreira Figure 9.14 Basic neural units used in feed-forward, simple recurrent networks (SRN), long short-term memory (LSTM) and gate recurrent units. In: Daniel Jurafsky, and James H. Martin (2018). [https://web.stanford.edu/~juraf... 1
04:36, 20 October 2018 Smith 2017 Fig2.png (file) 37 KB Omoreira Figure 2. Triangular learning rate policy. The blue lines represent learning rate values changing between bounds. The input parameter stepsize is the number of iterations in half a cycle. In: Leslie N. Smith (2017, March). [http... 1
22:00, 14 October 2018 D2AG LSTM.png (file) 67 KB Omoreira Figure 3: D2AG-LSTM In: Zemin Liu, Vincent W. Zheng, Zhou Zhao, Fanwei Zhu, Kevin Chen-Chuan Chang, Minghui Wu, and Jing Ying (2018). [http://forward.cs.illinois.edu/pubs/2017/dagembed-aaai2018-lzzzcwy-201711.pdf "Distan... 1
21:21, 14 October 2018 Shuai 1026 DAG RNN Fig3.png (file) 62 KB Omoreira Figure 3: The architecture of the full labeling network, which consists of three functional layers: (1), convolution layer: it produces discriminative feature maps; (2), DAG-RNN: it models the [[conte... 1
20:42, 14 October 2018 DAG LSTM.png (file) 68 KB Omoreira Figure 1: An example of DAG-LSTM in modeling a sentence. Nodes with different colors contain different types of LSTM memory blocks. In: Xiaodan Zhu, Parinaz Sobhani, and Hongyu Guo (2016). [http://www.aclweb.org/anthology/N1... 1
17:33, 14 October 2018 Goller Kuchler 1996 Fig2.png (file) 22 KB Omoreira Figure 2: Tree and DAG representation of a set of terms. In: Christoph Goller, and Andreas Kuchler (1996). [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.49.1968&rep=rep1&type=pdf "Learning task-dependent distributed represen... 1
23:00, 7 October 2018 SequeezeNet Architeture.png (file) 86 KB Omoreira Figure 2: Macroarchitectural view of our SqueezeNet architecture. Left: SqueezeNet (Section 3.3); Middle: SqueezeNet with simple bypass (Section 6); Right: SqueezeNet with complex bypass (Section 6). In:Forrest N. Iandola, [[Song Ha... 1
23:00, 7 October 2018 SqueezeNet FireModule.png (file) 56 KB Omoreira Figure 1: Microarchitectural view: Organization of convolution filters in the Fire module. In this example, s1x1 = 3, e1x1 = 4, and e3x3 = 4. We illustrate the convolution filters but not the activations. In: Forrest N. Iandola, [[S... 1
23:51, 30 September 2018 Resnet He 2015 Fig3.png (file) 164 KB Omoreira <P>Figure 3. Example network architectures for ImageNet. Left: the VGG-19 model(19.6 billion FLOPs) as a reference. Middle: a plain network with 34 parameter layers (3.6 billion FLOPs). Right: a residual network with 34 parameter layers... 1
First pagePrevious pageNext pageLast page
Retrieved from "http://www.gabormelli.com/RKB/Special:ListFiles"