Example: The Dishonest Casino • A casino has two dice: • Fair dice 1 Pr 𝑋 = 𝑘 = , 𝑓𝑜𝑟 1 ≤ 𝑘 ≤ 6 6 • Loaded dice 1 Pr 𝑋 = 𝑘 = , 𝑓𝑜𝑟 1 ≤ 𝑘 ≤ 5 10 1 Pr 𝑋 = 6 = 2 Casino player switches back-&-forth between fair and loaded dice once every 20 turns Slide credit: Eric Xing
Example: The Dishonest Casino • Given: A sequence of rolls by the casino player 124552656214614613613666166466 …. • Questions: • How likely is this sequence, given our model of how the casino works? (Evaluation) • What portion of the sequence was generated with the fair die, and what portion with the loaded die? (Decoding) • How “loaded” is the loaded die? How “fair” is the fair die? How often does the casino player change from fair to loaded, and back? (Learning) Slide credit: Eric Xing
time
output
output
• Hidden State: 𝐻𝑡 • Output: 𝑂𝑡 • Transition Prob between two states 𝑃(𝐻𝑡 = 𝑘|𝐻𝑡−1 = 𝑗) • Start Prob 𝑃(𝐻1 = 𝑗) • Emission Prob associated with each state 𝑃(𝑂𝑡 = 𝑖|𝐻𝑡 = 𝑗)
output
Hidden Markov Model
Hidden Markov Model (Generative Model)
𝑂𝑡
𝐻𝑡
Slide credit: Eric Xing
Example: dice rolling vs. speech signal • Output sequence: Discrete value
• Output sequence: Continuous data
• Limited number of states
• Syntax, semantics, accent,
rate, volume, and etc. • Temporal Segmentation
Limitations of HMM • Modeling continuous data • Long-term dependencies • Can only remember log(N) bits about what it generated so far.
Slide credit: G. Hinton
Feed-Forward NN vs. Recurrent NN
• “piped” vs. cyclic • Function vs. dynamic system
• Power of RNN: - Distributed hidden units - Non-linear dynamics ℎ𝑡 = 𝜎(𝑊𝐼 𝑥𝑡 + 𝑊𝐻 ℎ𝑡−1 + 𝑏) • Quote from Hinton
Providing input to recurrent networks
• Specify the initial states of all the units. • Specify the initial states of a subset of the units. • Specify the states of the same subset of the units at every time step.
w1
w3 w4 w2
• We can specify inputs in several ways:
w1
w3 w4
w2
w1
w3 w4
w2
time
• This is the natural way to model most sequential data.
Slide credit: G. Hinton
Teaching signals for recurrent networks • We can specify targets in several ways:
• Specify desired final activities of all the units • Specify desired activities of all units for the last few steps • Good for learning attractors • It is easy to add in extra error derivatives as we backpropagate.
• Specify the desired activity of a subset of the units.
Oct 4, 2014 - In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating .... It needs a fixed length of context (i.e. five words), whereas in our model, ..... The perplexity of MLBL-F and LBL now are 9.90.
28th International Conference on Machine Learning. (ICML), 2011. [7] R. Gemello, F. Mana, and R. De Mori, âNon-linear es- timation of voice activity to improve ...
experiments on the well-known airline travel information system. (ATIS) benchmark. ... The dialog manager then interprets and decides on the ...... He received an. M.S. degree in computer science from the University .... He began his career.
network will be elaborated in Section 3. We report our experimental results in Section 4 and conclude our work in Section 5. 2. RECURRENT DNN ARCHITECTURE. 2.1. Hybrid DNN-HMM System. In a conventional GMM-HMM LVCSR system, the state emission log-lik
auto-similar processes, VBR video traffic, multi-step-ahead pre- diction. ..... ulated neural networks versus the number of training epochs, ranging from 90 to 600.
two custom SLU data sets from the entertainment and movies .... searchers employed statistical methods. ...... large-scale data analysis, and machine learning.
There was a problem previewing this document. Retrying... Download. Connect more apps... Try one of the apps below to open or edit this item. Bengio ...
Submitted to the Council of College of Administration & Economics - University. of Sulaimani, As Partial Fulfillment for the Requirements of the Master Degree of.
classification by proposing a novel deep learning framework designed in an ... attention mechanism for the task of action recognition in videos. 3 Proposed ...
duce a model which uses a deep recurrent auto encoder neural network to denoise ... Training noise reduction models using stereo (clean and noisy) data has ...
Oct 14, 2015 - computing power is limited, our models are necessarily gross idealisations of real networks of neurones. The neuron model. Back to Contents. 3. ..... risk management target marketing. But to give you some more specific examples; ANN ar
that aim to reduce the average number of operations per step [18, 10], ours enables skipping steps completely. Replacing RNN updates with copy operations increases the memory of the network and its ability to model long term dependencies even for gat
Jul 24, 2015 - the input signal, we first stack frames so that the networks sees multiple (e.g. 8) ..... guage Technology Workshop, 1994. [24] S. Fernández, A.
Inspired by recent works in dialog systems (Seo et al., 2017 ... sequence mapping problem with a strong control- ling context ... minimum to improve train-ability in domains with limited data ...... ference of the European Chapter of the Associa-.
RNN architectures for large scale acoustic modeling in speech recognition. ... a more powerful tool to model such sequence data than feed- forward neural ...
Feb 19, 2014 - we use one neural net to generate a set of adversarial examples, we ... For the MNIST dataset, we used the following architectures [11] ..... Still, this experiment leaves open the question of dependence over the training set.
a specific AD signature from EEG. As a result they are not sufficiently ..... [14] Ifeachor CE, Jervis WB. Digital signal processing: a practical approach. Addison-.
consisted of a simulated M1 circuit (sM1, 150 neurons), which provided input to three separate spinal cord circuits (sSC1-â3, 25 neurons each performing ...
Feb 5, 2014 - an asymmetrical window, with 5 frames on the right and either 10 or. 15 frames on the left (denoted 10w5 and 15w5 respectively). The LSTM ...
many problems in computer vision, natural language processing or social networks, in which getting labeled ... inputs and on many different neural network architectures (see section 4). The paper is organized as .... Depending on the type of the grap
Dec 15, 2004 - social and geographical mobilities imply a fraction of random .... k ¼ 4n neighbouring sites on the square lattice (first ..... 10, 83â95. Bolker, B.
As we increase number of layers and their size the capacity increases: larger networks can represent more complex functions. ⢠We encountered this before: as we increase the dimension of the ... Lesson: use high number of neurons/layers and regular