lstm

Today even look at three papers, not very detailed to see, nor concrete to achieve, but probably understand some new ideas. These three papers, an overview of the Decoder-encoder model, an extension of the model, the first proposed attention mechanism, the last one elaborated the LSTM and GRU work mechanism. After reading, I have a deeper understanding of the field of machine translation, as well as the application of lstm.Let's talk about the princip

, paragraphs, and documents. This concept are used for many text classification tasks such as sentiment analysis.Recursive deep Models for Semantic compositionality over a sentiment treebankSocher et al. 2013. Introduces Recursive neural Tensor Network and DataSet: "Sentiment Treebank." Includes demo site. Uses a parse tree.Distributed representations of sentences and DocumentsLe, Mikolov. Introduces Paragraph Vector. Concatenates and averages pretrained, fixed word vectors to create vectors for

. I explore this idea into the context of the Elman network later in this tutorial. Other networks Work on Recurrent-style Networks has is not stopped, and today, recurrent architectures-are setting the standard for Operatin G on time-series data. The long Short-term memory (LSTM) approach in deep learning has been used and convolutional networks to describe in Gener Ated language The content of images and videos. The

variable generation (Z→H) This section uses the current popular LSTM Structure 3 simulation steps between the memory relationship. In addition to the internal hidden state HT mentioned above, it also contains input it, forgetting ft, storage ct, output OT, candidate GT a total of 6 states. They are all m-dimensional variables. Input i, output o, and forgotten F are three "gate variables" that control the strength of other States, both through the imp

feudal Networks for hierarchical reinforcement Learning tags (space delimited): paper Notes Enhanced Learning Algorithm Feudal Networks for hierarchical reinforcement Learning Abstract Introduction model Learning Transition Policy gradients A Rchitecture details Dilated Lstm didn't look Abstract This paper is mainly on the improvement and application of fedual reinforcenment learning,First, the form of fedual reinforcement learning:1. Mainly divided

AV sensor readings to manipulate the best safe spacing between vehicles and potentially increase the risk of AV accidents or reduce traffic on the road. At the same time, AV, as a defender, tries to minimize pitch deviations to ensure robustness against attackers ' behavior. Since AV has no information about the attacker's behavior, and because of the infinite possibilities of manipulating data values, the results of the player's previous interactions are entered into the long short-term memory

segmentation. In view of this problem, the method of Word recognition based on sequence emerges. This kind of method is very similar to the speech recognition method, it regards the line of words as a whole, does not make the segmentation, and identifies the sequence of characters directly in batch or increment. This method can make use of the context Association of text sequences to eliminate the irreversible errors caused by the segmentation of character errors. In this framework, the trainin

learning which hot technology becomes the headache for AI scholars and practitioners. The purpose of this column is to help you to screen out interesting papers, to interpret the core ideas of the paper, to provide reading guidance for intensive reading. NIPS (Neural information processing systems, the Progress conference on Neural Information Processing systems) is a top-level meeting of AI and machine learning, hosted by the NIPS Foundation in December each year, which attracts machine learni

distribution after adding temperature is:\[p (x_{new}) = \frac{e^{\,{log (P (x_i))}\,/\,{temperature}}}{\sum\limits_i E^{\,{log (P (x_i))}\,/\,{temperature}}} \ Tag{1.1}\] The probability distributions obtained by different temperature were shown. The larger the temperature, the more uniform the new probability distribution, the greater the randomness, and the easier it is to generate some unexpected words. def sample(p, temperature=1.0): # 定义采样策略 distribution = np.log(p) / temperature d

The original paper: A structured self-attentive sentence embedding introduction This article presents a model that uses self-attention techniques to generate explanatory sentence vectors. Usually we use vectors to represent words, phrases, or sentence vectors, and in this article, the authors suggest that two-dimensional sentences can be used to represent sentences, and that each line of the matrix represents different parts of the sentence. The authors performed 3 different tasks on 3 different

the improved algorithm of Rnns, LSTM and GRU algorithm can solve the problem of vanishing gradient, and can effectively learn the correlation of longer range, which is more concerned in the development of these two algorithms. The improved algorithm of Rnns1, bidirectional Rnns (bidirectional Rnns Network)Article: "Bidirectional recurrent neural Networks"The idea of Brnns is that the output of time t is not only related to the previous element in the

the stability of the training Gradient explosions or gradients disappear due to the correlation between gradients So that the training can not find the direction of optimization, training failure Clip GradientCalculate to gradient explosion, use a ratio to replace W (gradient is the return calculation, the horizontal axis from right to left to see) Hack but cheap and effective LSTM (Long short-term Memory)The dis

BP algorithm is the foundation and the most important part of neural network. The loss function needs to be adjusted because the gradient disappears or explodes during the reverse propagation of the error. In the lstm, through the sigmoid to achieve three doors to solve the memory problem, in the process of tensorflow implementation, the need for gradient pruning operations to prevent gradient explosion. RNN's BPTT algorithm also has such problems, so

GuideThis paper discusses the reasons why deep neural network training is difficult and how to use highway Networks to solve the problem of deep neural network training, and realizes Highway Networks on Pytorch.I. The relationship between Highway Networks and deep NetworksThe deep neural network has better effect compared with the shallow neural network, in many aspects has achieved very good results, especially in the image processing has made a great breakthrough, however, with the increase in

units), which we mark as {s0,s1,..., st,st+1,...}, and these hidden units do the most important work. You will find in the diagram: There is a one-way flow of information from the input unit to the hidden unit, while another one-way flow of information from the hidden unit to the output unit. In some cases, Rnns will break the latter limit by directing information back to the hidden unit from the output unit, which is known as "projections", and the input of the hidden layer includes the state

reverse propagation. A better network. What needs to be stated briefly is that in practice a slightly different algorithm is commonly used, which is the long and long base memory network I mentioned earlier, referred to as lstm. Lstm is a special type of circular network. Because of its more powerful renewal equation and better dynamic reverse propagation mechanism, it has a better effect in practice. This

, including some interpretation and manipulation of graphs (graphs), sessions (session), tensor (tensor), variables (Variable). lesson three TensorFlow linear regression and simple use of classifications. The fourth lesson Softmax, cross-entropy (cross-entropy), dropout, and the introduction of various optimizations in TensorFlow. Fifth Lesson, CNN, and CNN to solve the problem of mnist classification. The sixth lesson uses Tensorboard to visualize the structure and visualize the process of the

This article introduces a very simple threshold rnn (gated recurrent neural network),Here are two doors horizontal/forget gate and Vertical/input Gate, i.e.which (Logistic sigmoid function)The following assumes that the input data XT meet the following properties,If the hidden layer node is initialized to 0, that is, the network response to the Pulse XT is,With attenuation to 0, the forget gate controls the attenuation speed, so when the hidden-layer node HT (i) encounters a strong signal,HT (i)

algorithm :In this paper, the algorithm flow as shown, first use embedding method, extract the problem and image feature, then co-attention Learning, and then the two weighted feature to be combined, and then input into the memory network, The choice of the final answer. Image Embedding: extracting feature with pre-trained model; Question embedding: using bidirectional LSTM network to study language features;Sequential co-attention:Here the synergist

lot of similarities with DRAW. It has been said that sequential version can be considered when improving GAN. The advantage of sequential models is that the next model can make use of the results from the previous step and make changes in the previous results, similar to a conditional way. In order for Gan to have this sequential ability, this paper [5] will combine Gan and lstm, called GRAN, to divide it into the process of step by step. Each step,