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• Learning to Teach
  This is a summary of the paper titled: "Learning to Teach", authored by Yang Fan, Fei Tian, Tao Qin, Xiang-Yang Li, and Tie-Yan ...ent, determining the appropriate data, loss function, and hypothesis space to facilitate the learning of the student model. ...
  21 KB (3,351 words) - 18:40, 16 December 2018
• sandbox to test w2l
  54 bytes (12 words) - 09:46, 30 August 2017
• Learning What and Where to Draw
  ...label or a non-localized caption. The authors of 'Learning What and Where to Draw' believe that image synthesis will be drastically enhanced by incorpor ...scription what each image is intended to depict. The proposed model learns to perform location and content-controllable image synthesis on the Caltech-UC ...
  18 KB (2,781 words) - 12:35, 4 December 2017
• link to my paper
  </ref>. Now we turn to ...
  204 bytes (22 words) - 09:45, 30 August 2017
• From Variational to Deterministic Autoencoders
  ...er are able to generate samples that are comparable or better when applied to domains of images and structured objects. The authors point to several drawbacks currently associated with VAE's including: ...
  15 KB (2,313 words) - 19:11, 2 December 2020
• Link to my paper
  #REDIRECT [[link to my paper]] ...
  30 bytes (5 words) - 09:45, 30 August 2017
• Sandbox to test w2l
  #REDIRECT [[sandbox to test w2l]] ...
  33 bytes (6 words) - 09:46, 30 August 2017
• Pixels to Graphs by Associative Embedding
  ...ons between them. An explicit representation of this semantics is referred to as a scene graph where we represent objects grounded in the scene as vertic ...all of the objects in the scene, then isolate individual pairs of objects to identify the relationships between them. This breakdown often restricts the ...
  17 KB (2,749 words) - 18:26, 16 December 2018
• learning Spectral Clustering, With Application To Speech Separation
  ...hat generalizes to the unseen datasets when spectral clustering is applied to them. Traditional spectral clustering techniques assume a metric or a simil Clustering refers to partition a given dataset into clusters such that data points in the same c ...
  35 KB (5,767 words) - 09:45, 30 August 2017
• Towards Deep Learning Models Resistant to Adversarial Attacks
  This is a summary of the paper "Towards Deep Learning Models Resistant to Adversarial Attacks" by Aleksander Madry, Aleksandar Makelov, Ludwig Schmid ...e|'''Figure 1.''' Before and after an input image of a panda was subjected to perturbations.^{[https://arxiv.org/abs/1412.6572 Source]}]] ...
  14 KB (2,192 words) - 03:01, 23 November 2018
• Learning Spectral Clustering, With Application To Speech Separation
  #REDIRECT [[learning Spectral Clustering, With Application To Speech Separation]] ...
  81 bytes (9 words) - 09:45, 30 August 2017
• Learning to Navigate in Cities Without a Map
  [https://arxiv.org/pdf/1804.00168.pdf Learning to Navigate in Cities Without a Map] ...forcement learning (RL), it suffers from data inefficiency and sensitivity to changes in the environment. Thus, it is unclear whether this method could b ...
  28 KB (4,494 words) - 00:24, 17 December 2018
• DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION
  ...ue function, directly on latent state samples which help to enable scaling to more complex tasks. ...omes with using finite imagination horizons. The authors have also managed to demonstrate empirical performance for visual control by evaluating the mode ...
  13 KB (2,072 words) - 06:07, 10 December 2020
• rOBPCA: A New Approach to Robust Principal Component Analysis
  ...ix. Since the classical estimation for covariance matrix is very sensitive to the presence of outliers, it is not surprising that the principal component ...to show that Bayesian robust estimator may be alternative choice compared to classical robust estimators. ...
  15 KB (2,414 words) - 09:46, 30 August 2017
• Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
  ...n from Tel Aviv University. This paper is part of the NIPS 2018 conference to be hosted in December 2018 at Montréal, Canada. This paper summary is based ...framework for capturing such effects is structured prediction, which seeks to predict structured objects (such as graphs with nodes and edges) rather tha ...
  29 KB (4,603 words) - 21:21, 6 December 2018
• neural Machine Translation: Jointly Learning to Align and Translate
  ...hod is more effective compared to other neural network models when applied to long sentences. ...word. The decoder then selectively combines the most relevant annotations to generate each target word; this implements a mechanism of attention in the ...
  14 KB (2,221 words) - 09:46, 30 August 2017
• stat946F18/Beyond Word Importance Contextual Decomposition to Extract Interactions from LSTMs
  ...for analyzing individual predictions made by the LSTMs without any change to the underlying original model. The problem of sentiment analysis is chosen ...n domain, this paper shows how the contextual decomposition method is used to successfully extract positive and negative negations from an LSTM. This pap ...
  31 KB (5,069 words) - 18:21, 16 December 2018
• ROBPCA: A New Approach to Robust Principal Component Analysis
  #REDIRECT [[rOBPCA: A New Approach to Robust Principal Component Analysis]] ...
  75 bytes (10 words) - 09:46, 30 August 2017
• Summary of A Probabilistic Approach to Neural Network Pruning
  ...proposes that the subnetworks can achieve similar accuracy without having to be further trained. However, finding these lottery tickets inside a large n ...theoretical guarantees of pruning. This study, ''A Probabilistic Approach to Neural Network Pruning'' by Xin Qian and Diego Klabjan [18], focuses on the ...
  28 KB (4,367 words) - 00:30, 23 November 2021
• A Game Theoretic Approach to Class-wise Selective Rationalization
  ...alternative conclusions. Each class consists of three players who compete to find evidence for both factual and counterfactual circumstances. In a simpl ...ng explanations for a specific class by probing the importance with regard to the relevant class logit. ...
  11 KB (1,594 words) - 13:14, 25 November 2021
• Neural Machine Translation: Jointly Learning to Align and Translate
  #REDIRECT [[neural Machine Translation: Jointly Learning to Align and Translate]] ...
  81 bytes (10 words) - 09:46, 30 August 2017
• STAT946F17/ Teaching Machines to Describe Images via Natural Language Feedback
  ...rd in that we can easily point to where the mistakes occur and suggest how to correct them. ...n also be seen as a multimodal problem where the whole network/model needs to combine the solution space of learning in both the image processing and tex ...
  23 KB (3,760 words) - 10:33, 4 December 2017
• a Rank Minimization Heuristic with Application to Minimum Order System Approximation
  ...stics and signal processing. Except in some special cases the RMP is known to be computationally hard. \mbox{subject to: } & X \in C, ...
  8 KB (1,446 words) - 09:45, 30 August 2017
• a New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
  ...pular online shopping website Amazon.com for recommending related products to users of Amazon.com based on what these users have recently purchased from Our goal, then, is to predict or infer the other preferences---in a sense, completing the matrix. ...
  24 KB (3,853 words) - 09:45, 30 August 2017
• A Rank Minimization Heuristic with Application to Minimum Order System Approximation
  #REDIRECT [[a Rank Minimization Heuristic with Application to Minimum Order System Approximation]] ...
  98 bytes (12 words) - 09:45, 30 August 2017
• A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
  #REDIRECT [[a New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization]] ...
  105 bytes (12 words) - 09:45, 30 August 2017
• a Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis
  ...rform [http://en.wikipedia.org/wiki/Inference inference] across data sets. To this end, they demonstrate their penalized CCA method on a genomic data set ...r value decomposition will give the best rank-<math>r</math> approximation to the matrix. ...
  30 KB (4,829 words) - 09:45, 30 August 2017
• Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples
  ...lassify with high confidence. These attacks pose a major threat that needs to be addressed before these systems can be deployed on a large scale, especia ...much lower than claimed. In fact, the majority of these attacks were found to be ineffective against true iterative white box attacks. ...
  27 KB (3,974 words) - 17:54, 6 December 2018
• A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis
  #REDIRECT [[a Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis]] ...
  131 bytes (15 words) - 09:45, 30 August 2017
• Graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns
  ...RECT [[graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns]] ...
  145 bytes (17 words) - 09:46, 30 August 2017
• graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns
  ...ide information and incorporate the side information in the classification to improve the algorithms. ...uctured classification problem in practice, we need both an expressive way to represent our beliefs about the structure, as well as an efficient probabil ...
  17 KB (2,924 words) - 09:46, 30 August 2017
• End to end Active Object Tracking via Reinforcement Learning
  ...g box labeling. In addition, Camera Control is non-trivial, which can lead to many expensive trial-and-errors in the real world. To address these challenges, this paper presents an end-to-end active tracking solution via deep reinforcement learning. More specific ...
  29 KB (4,453 words) - 18:27, 16 December 2018
• DeepVO Towards end to end visual odometry with deep RNN
  ...lude the VO field, thus the paper proposes a novel deep-learning based end-to-end VO algorithm and then empirically demonstrates its viability. ...ture based methods and direct methods, which differ in the method employed to select reference points. Sparse feature based methods establish reference p ...
  16 KB (2,430 words) - 18:30, 16 December 2018
• stat946f15/Sequence to sequence learning with neural networks
  ...amount of work to learn more than one language past childhood. The ability to efficiently and quickly translate between languages would then be of great ...s that capture their meaning, as sentences with similar meanings are close to each other while sentences with different meanings will be far. ...
  23 KB (3,755 words) - 19:49, 5 February 2018
• End-to-End Differentiable Adversarial Imitation Learning
  ...is that the training requires large amounts of expert data, which is hard to obtain. In addition, an agent trained using BC is unaware of how its action ...re it takes each action since the transition function to move from state A to state B is not learned. ...
  24 KB (3,880 words) - 23:00, 20 April 2018
• Stat946f15/Sequence to sequence learning with neural networks
  #REDIRECT [[stat946f15/Sequence to sequence learning with neural networks]] ...
  75 bytes (10 words) - 09:46, 30 August 2017
• Augmix: New Data Augmentation method to increase the robustness of the algorithm
  ...s & Dietterich (2019), showing that the classification error rose from 25% to 62% when some corruption was introduced on the ImageNet test set. ...ce that networks trained on translation augmentations are highly sensitive to the shifting of pixels. ...
  11 KB (1,652 words) - 18:44, 6 December 2020
• U-Time:A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging Summary
  ...pecially trained to be applied on one dataset alone and might be difficult to use for non-experts in a more general setting (Perslev et al., 2019). ...r architectural tuning to be applied to variable data sets, and it is able to classify sleep stages at any temporal resolution (Perslev et al., 2019). ...
  8 KB (1,170 words) - 01:41, 26 November 2021
• From Machine Learning to Machine Reasoning
  #REDIRECT [[from Machine Learning to Machine Reasoning]] ...
  56 bytes (7 words) - 09:46, 30 August 2017
• Convolutional Sequence to Sequence Learning
  '''Sequence to sequence learning''' has been used to solve many tasks such as machine translation, speech recognition, and text ...other. This allows to precisely control the maximum length of dependencies to be modeled. ...
  27 KB (4,178 words) - 20:37, 28 November 2017
• from Machine Learning to Machine Reasoning
  ...82, 273–302.</ref>. Algorithms for inference do exist but they do however, come at a price of reduced expressive capabilities in logical inference and prob ...ut not yet formal or logical. Informal logic is attractive because we hope to avoid the computational complexity that is associated with combinatorial se ...
  21 KB (3,225 words) - 09:46, 30 August 2017
• learn what not to learn
  ...ees to climb"). Then a machine learning model can be trained to generalize to unseen states. ...with high probability. '''Note that the core assumption is that it is easy to predict which actions are invalid or inferior in each state and leverage th ...
  29 KB (4,751 words) - 13:38, 17 December 2018

Page text matches

• Imagination Augmented Agents for Deep Reinforcement Learning
  ...experience, yet in complex domains for which a simulator is not available to the agents, the performance of model-based agents employing standard planni ...ct useful knowledge gathered from model simulations. This allows the agent to benefit from model-based imagination without the pitfalls of conventional m ...
  2 KB (210 words) - 20:39, 9 March 2018
• is Multinomial PCA Multi-faceted Clustering or Dimensionality Reduction
  ...ed using a convex combination to a number of clusters rather than uniquely to one cluster. This is an unsupervised version of the so-called multi-class c ...e data, authors have recently proposed discrete analogues to PCA. We refer to the method as multinomial PCA(mPCA) because it is a precise multinomial ana ...
  2 KB (321 words) - 09:45, 30 August 2017
• Deep Transfer Learning with Joint Adaptation Networks
  ...ean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains ...
  760 bytes (109 words) - 15:32, 2 October 2017
• Link to my paper
  #REDIRECT [[link to my paper]] ...
  30 bytes (5 words) - 09:45, 30 August 2017
• Sandbox to test w2l
  #REDIRECT [[sandbox to test w2l]] ...
  33 bytes (6 words) - 09:46, 30 August 2017
• importance Sampling June 2 2009
  ...lt but there exists a probability distribution function g(x) which is easy to sample from, then <math>I</math> can be written as<br> ...playstyle E_g(w(x)) \rightarrow</math>the expectation of w(x) with respect to g(x) ...
  2 KB (395 words) - 09:45, 30 August 2017
• Learning Spectral Clustering, With Application To Speech Separation
  #REDIRECT [[learning Spectral Clustering, With Application To Speech Separation]] ...
  81 bytes (9 words) - 09:45, 30 August 2017
• Neural Machine Translation: Jointly Learning to Align and Translate
  #REDIRECT [[neural Machine Translation: Jointly Learning to Align and Translate]] ...
  81 bytes (10 words) - 09:46, 30 August 2017
• ROBPCA: A New Approach to Robust Principal Component Analysis
  #REDIRECT [[rOBPCA: A New Approach to Robust Principal Component Analysis]] ...
  75 bytes (10 words) - 09:46, 30 August 2017
• A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
  #REDIRECT [[a New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization]] ...
  105 bytes (12 words) - 09:45, 30 August 2017
• A Rank Minimization Heuristic with Application to Minimum Order System Approximation
  #REDIRECT [[a Rank Minimization Heuristic with Application to Minimum Order System Approximation]] ...
  98 bytes (12 words) - 09:45, 30 August 2017
• deepGenerativeModels
  To properly train a neural network a large labeled dataset, however large data These models scale linearly in proportion to the number of classes in the data sets. The number of evaluations could be ...
  466 bytes (70 words) - 09:46, 30 August 2017
• A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis
  #REDIRECT [[a Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis]] ...
  131 bytes (15 words) - 09:45, 30 August 2017
• contributions on Video-Based Face Recognition Using Adaptive Hidden Markov Models
  ...the methods used for video-based face recognition were based on the still-to-still techniques which aimed at selecting good frame and then performed som ...s kind of application, which is called online video. The other scenario is to process the video content offline, like indexing the meeting records or ana ...
  3 KB (512 words) - 09:45, 30 August 2017
• Pre-Training-Tasks-For-Embedding-Based-Large-Scale-Retrieval
  ...ifically, this paper explores whether we can train machine learning models to learn from dialog. *Evaluated some baseline models on this data and compared them to standard supervised learning. ...
  2 KB (309 words) - 19:52, 17 November 2020
• Graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns
  ...RECT [[graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns]] ...
  145 bytes (17 words) - 09:46, 30 August 2017
• Stat946f15/Sequence to sequence learning with neural networks
  #REDIRECT [[stat946f15/Sequence to sequence learning with neural networks]] ...
  75 bytes (10 words) - 09:46, 30 August 2017
• From Machine Learning to Machine Reasoning
  #REDIRECT [[from Machine Learning to Machine Reasoning]] ...
  56 bytes (7 words) - 09:46, 30 August 2017
• sign up for your presentation
  ...following table. Put your name and a link to the paper that you are going to present. Chose a date between Nov 16 and Dec 2 (inclusive). .../Correlate/pmd.pdf], [[A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis|Summary]] ...
  4 KB (570 words) - 09:45, 30 August 2017
• a Dynamic Bayesian Network Click Model for web search ranking
  ...a document they will not examine the next document. This model is similar to hidden Markov model in that there is a conditional dependency between the p ...that if a URL clicked by a user that means it’s both examined and relevant to the query . In another word , given a query q , position i and URL u the pr ...
  3 KB (593 words) - 09:46, 30 August 2017
• Hash Embeddings for Efficient Word Representations
  ...hence because of this for whatever data we need to feed in the network has to be continuous in nature. Images can easily be represented as real-valued ve ...parameters it needs to learn is quite high. There have been some solutions to it: ...
  4 KB (646 words) - 19:44, 26 October 2017
• Batch Normalization Summary
  ...batch-normalization layers right before the activations (to have the input to the activations be normalized as desired). Both networks were trained with ...he 15th, 50th, and 85th percentiles of the input were recorded. The figure to the left demonstrates how these values changed during training. The y axis ...
  4 KB (637 words) - 02:07, 28 November 2018
• learning2reasoning
  ...properties (cite). Algorithms for inference do exist but they do however, come at a price of reduced expressive capabilities in logical inference and prob ...
  852 bytes (116 words) - 09:46, 30 August 2017
• paper Summaries
  ==A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis== [[A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis]] ...
  2 KB (222 words) - 09:45, 30 August 2017
• acceptance-Rejection Sampling
  ...>; on the other hand we would reject the samples if the ratio is not close to 1. At x=9; we will reject samples according to the ratio <math> \frac {f(x)}{c \cdot g(x)} </math> after sampling from <ma ...
  6 KB (937 words) - 09:45, 30 August 2017
• Task Understanding from Confushing Multitask Data
  3 data sets are used to compare CSL to existing methods, 1 function regression task and 2 image classification tas ...s <math>f_j</math> as well as determine which mapping function corresponds to each of the <math>m</math> observations. 3 scalar-valued, scalar-input func ...
  5 KB (878 words) - 19:25, 15 November 2020
• Meta-Learning-For-Domain Generalization
  ...d during training time. Here by defining tasks as domains, the paper tries to overcome the problem in a model-agnostic way. ...
  1 KB (200 words) - 15:47, 9 November 2020
• copyofstat341
  ...sed for the uniform distribution, other methods must be developed in order to generate pseudo random numbers from other distributions. ...he fact that when a random sample from the uniform distribution is applied to the inverse of a cumulative density function (cdf) of some distribution, th ...
  5 KB (836 words) - 09:45, 30 August 2017
• again on Markov Chain
  ...on of its classes. This decomposition is always possible and it is reduced to one class only in the case of an irreducible chain. ...ath> The state 3 can go to every other state but none of the others can go to it ...
  7 KB (1,129 words) - 09:45, 30 August 2017
• proof of Lemma 1
  -\textbf{u}^T\textbf{a} \; \textrm{ subject } \; \textrm{ to } \; \|\textbf{u}\|^2_2 \leq 1, \; \|\textbf{u}\|_1 \leq c_1 and we differentiate, set the derivative to 0 and solve for <math>\textbf{u}</math>: ...
  2 KB (311 words) - 09:45, 30 August 2017
• main Page
  '''NOTE: Wiki has been migrated from wikicoursenote.com to wiki.math.uwaterloo.ca/statwiki''' ==Go to [[stat841f10|Stat441/841 & CM 463/763-Fall 2010]] == ...
  5 KB (769 words) - 22:53, 5 September 2021
• s13Stat946proposal
  ...pefully, the pattern of the teams and lineups in the latent space can lead to interesting conclusions. Secondly, we apply the selected methods to lineup data sets and get the plots of the lineups in the low-dimensional sp ...
  6 KB (983 words) - 09:46, 30 August 2017
• markov Chain Definitions
  ...<math>f(x)</math> so that a variation of importance estimation can be used to estimate an integral in the form<br /> All that is required is a Markov chain which eventually converges to <math>f(x)</math>. ...
  5 KB (865 words) - 09:45, 30 August 2017
• Batch Normalization
  ...ork, the inputs are no longer normalized at each hidden layer. So, we want to reduce this internal covariate shift by normalizing the input at each hidde ...However, this is a very expensive operation, and does not necessarily lead to a gradient function that is well defined. ...
  6 KB (931 words) - 21:10, 28 November 2018
• proposal for STAT946 (Deep Learning) final projects Fall 2015
  ...r the gander , some of which occasionally amuses but none of which amounts to much of a story” contains negative sentiment, but it is not immediately cle This competition seeks to implement machine learning algorithms that can determine the sentiment of a ...
  7 KB (1,125 words) - 09:46, 30 August 2017
• bayesian and Frequentist Schools of Thought
  ...n the Bayesian and Frequentist views on probability, along with references to '''Bayesian Inference'''. ...enough, by the central limit theorem, the Normal distribution can be used to approximate a Binomial distribution. ...
  6 KB (924 words) - 09:45, 30 August 2017
• f11Stat946presentation
  ...n up your name at the moment. When you chose the paper that you would like to present, add its title and a link to the paper. ...
  3 KB (418 words) - 09:45, 30 August 2017
• Deep Residual Learning for Image Recognition Summary
  ...ces as the parameters in the model are tuned, and thus the model is unable to evolve. ...would result in the error values of the deeper network being at most equal to those of the shallower network. However, this result is not seen in practic ...
  6 KB (1,020 words) - 12:01, 3 December 2021
• Convolutional Neural Networks for Sentence Classification
  ...riants of this model have been introduced by the authors, two of which try to learn task-specific word vectors for words. It is observed that learning ta ...different models for doing different tasks. For instance, they can be fed to CNNs for document or sentence classification. The vector representations us ...
  7 KB (1,086 words) - 22:49, 13 November 2018
• a Deeper Look into Importance Sampling
  ...playstyle E_g(h(x)) \rightarrow</math>the expectation of h(x) with respect to g(x), where <math>\displaystyle \frac{f(x)}{g(x)} </math> is a weight <math The method of Importance Sampling is simple but can lead to some problems. The <math> \displaystyle \hat I </math> estimated by Importa ...
  6 KB (1,083 words) - 09:45, 30 August 2017
• stat940F21
  |width="30pt"|Link to the paper |width="30pt"|Link to the video ...
  5 KB (642 words) - 23:29, 1 December 2021
• test1
  {{Cleanup|date=September 2010|reason=explain what needs to be done}} ...
  255 bytes (46 words) - 09:45, 30 August 2017
• Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network
  ...nvolutional Neural Network, and Support Vector Machine models are proposed to address this issue. In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and ...
  4 KB (515 words) - 18:44, 17 December 2021
• the Indian Buffet Process: An Introduction and Review
  The Indian buffet process can also be used to define a prior distribution in any setting where the where <math> \alpha </math> is a hyper-parameter, which is similar to the parameter defined in DP. ...
  6 KB (1,032 words) - 09:46, 30 August 2017
• deflation Method for Penalized Matrix Decomposition Sparse PCA
  ...Trevor Hastie. (2009) "A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis". ''Biostati The penalized matrix decomposition can be used to obtain a version of sparse PCA. In this case, ...
  2 KB (277 words) - 09:45, 30 August 2017
• metric and Kernel Learning Using a Linear Transformation
  ...tructure in the data explicityly but most of them are unable to generalize to new added data points as only implicit non-linear transformation is given. ...that it can handle out-of-sample extensions. Also, even though the matrix to be learned may be infinite-dimensional, it can be fully represented in term ...
  6 KB (1,007 words) - 09:46, 30 August 2017
• genetics
  ...o account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. ...type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders. ...
  6 KB (980 words) - 09:46, 30 August 2017
• proof
  ...<math>\lambda_{\max}(\cdot)</math> at the matrix <math>X \in S_n</math>. To do this we must first define the subgradient. ...tion we are interested in is <math>\lambda_{\max}(\cdot)</math>. In order to define the subgradiant of this function we must first ensure it is convex. ...
  3 KB (589 words) - 09:45, 30 August 2017
• importance Sampling and Monte Carlo Simulation
  ...order to get a distribution for the probability 'p' of a Binomial, we have to divide the Binomial distribution by n. This new distribution has the same s # Compute <math>\displaystyle \delta = p_1 - p_2</math> in order to get n values for <math>\displaystyle \delta</math>; ...
  7 KB (1,232 words) - 09:45, 30 August 2017
• link to my paper
  </ref>. Now we turn to ...
  204 bytes (22 words) - 09:45, 30 August 2017