#### Problem addressed: 
Training specific generative model under milder unsupervised assumption

#### Summary: 
This paper implemented an attention based scheme for learning generative models, which could make the unsupervised learning more applicable in practice. In common unsupervised settings, one would assume that the unsupervised data is already in the desired format that one could used directly, which could be a very strong assumption. In this work, the demonstrated a specific application of the idea for training face models. They use a canonical low resolution face model that model the object in mind, alone with a search scheme that resembles the attention to search the face region in a high resolution image. The whole scheme is formalized as a full probabilistic model, and the attention is thus implemented as a inference through the model. The probabilistic model is implemented using RBMs. For inference, they imployed hybrid Monte Carlo, as with all MCMC methods, it is hard for the sampling methods to go between modes that are separated by low density areas. To overcome this, they instead used convnet to propose the moves and used hmc with the convnet initilized states so that the full system is still probabilistic. The result demonstrated are pretty interesting.

#### Novelty:
The application of visual attention using RBM with probabilistic inference.

#### Drawbacks:
The inference do need a good convnet for initilization, and it seems the mixing of Markov chain is a big problem.

#### Datasets:
Caltech and CMU face dataset

#### Resources:
A neurobiological model of visual attention and
 invariant pattern recognition based on dynamic routing of information, is the paper of visual attention

#### Presenter:
Yingbo Zhou

TLDR; The authors apply a neural seq2seq model to sentence summarization. The model uses an attention mechanism (soft alignment).


#### Key Points

- Summaries generated on the sentence level, not paragraph level
- Summaries have fixed length output
- Beam search decoder
- Extractive tuning for scoring function to encourage the model to take words from the input sequence
- Training data: Headline + first sentence pair.

#### Problem addressed: 
Variational learning of Bayesian networks

#### Summary: 
This paper present a generic method for learning belief networks, which uses variational lower bound for the likelihood term.

#### Novelty:
Uses a re-parameterization trick to change random variables to deterministic function plus a noise term, so one can apply normal gradient based learning

#### Drawbacks:
The resulting model marginal likelihood is still intractible, may not be very good for applications that require the use of actual values of the marginal probablities

#### Datasets:
MNIST, Frey face

#### Additional remarks:
Experimentally compared with wake sleep algorithm on logliklihood lower bound as well as estimated marginal likelihood

#### Resources:
Implementation: https://github.com/y0ast/Variational-Autoencoder

#### Presenter:
Yingbo Zhou

They propose a joint model for 1) identifying event keywords in a text, 2) identifying entities, and 3) identifying the connections between these events and entities. They also do this across different sentences, jointly for the whole text.

https://i.imgur.com/ETKZL7V.png

Example of the entity and event annotation that the system is modelling.

The entity detection part is done with a CRF; the structure of an event is learned with a probabilistic graphical model; information is integrated from surrounding sentences using a Stanford coreference system; and these are all tied together across the whole document using Integer Linear Programming.

### **Keyword**: RNN, serialized model; non-differentiable backpropogarion; action detection in video 


**Abstract**: This paper uses an end-to-end model which is a recurrent neural network trained by REINFORCE to directly predict the temporal bounds of actions. The intuition is that people will observe moments in video and decide where to look to predict when an action is occurring. After training, Serena et al manage to achieve the state-of-art result by only observing 2% of the video frames.

**Model**: In order to take a long video and output all the instances of given action, they use two parts including an observation network and recurrent network. 
* observation network:  encode the visual representation of video frames.
     * input:  $ln$ -- the normalized location of the frame + frame $v_{ln}$
     * network: fc7 feature of finetuned VGG16 network
     * output: $on$ of 1024 dimension indicate time and frame feature 
     
* recurrent network: sequentially process the visual representations and decide where to watch next and whether to emit detection.
##### for each timestep:
     * input:  $on$ -- the representation of the frame + previous state $h_{n-1}$
     * network: $d_n = f_d(h_n; \theta_d)$. $pn = fp(h_n,\theta_p)$,$fd$ is fc. $fp$ is fc+sigmoid  
     * output: $d_n = (s_n,e_n,c_n )$as the candidate detection, where $s_n$,$e_n$ is the start and end of the detection, $c_n$ is confidence level;  $p_n$ whether $d_n$ is a valid detection. $l_{n+1}$ where to observe next. all the parameter falls in [0,1]
https://i.imgur.com/SeFianV.png


**Training**: in order to learn the supervision annotation in long videos and handle the non-differentiable components, authors use BP to train $d_n$ while use REINFORCE to train $p_n$ and $l_{n+1}$
* for $d_n$: $L(D) = \sum_n L_{cls}(d_n) + \sum_n \sum_m 1[y_{mn} = 1] L_{loc}(d_n,g_m)$
* for $p_n,l_{n+1}$: reward $J(\theta) = \sum_{a\in A} p(a) r(a)$ p(a) is the distribution of action and r(a) is the reward for the action. so training needs to maximize this.


**Summary**:
This paper uses a serialized model which first extract the feature from each frame, then use the frame feature and previous state info to generate the next observation time, detection and detection indicator. Specifically, in order to use previous information, they use RNN to store information and use REINFORCE to train $p_n$ and $l_n$, where the goal is to maximize reward for an action sequence and use Monte-Carlo sampling to numerically calculate the gradient for high dimension function.

**questions**:
1. why  $p_n$ and $l_n$ are non-differentiable components?
2. if $p_n$ and $l_n$ are non-differentiable components indeed, how do we come up with REINFORCE to compute the gradient?
3. why don't we get $p_n$ from $p_n = f_p(h_n, \theta_p)$ directly but rather use fp as the parameter in bernoulli distribution, similar question can be applied to calculation for $l_{n+1}$ in trainning time.