https://i.imgur.com/QxHktQC.png 
The fundamental question that the paper is going to answer is weather deep learning can be realized with other prediction model other thahttps://i.imgur.com/Wh6xAbP.pngn neural networks. The authors proposed deep forest, the realization of deep learning using random forest(gcForest). The idea is simple and was inspired by representation learning in deep neural networks which mostly relies on the layer-by-layer processing of raw features.

Importance: Deep Neural Network (DNN) has several draw backs. It needs a lot of data to train. It has many hyper-parameters to tune. Moreover, not everyone has access to GPUs to build and train them. Training DNN is mostly like an art instead of a scientific/engineering task. Finally, theoretical analysis of DNN is extremely difficult. The aim of the paper is to propose a model to address these issues and at the same time to achieve performance competitive to deep neural networks.

 Model: The proposed model consists of two parts. First part is a deep forest ensemble with a cascade structure similar to layer-by-layer architecture in DNN. Each level is an ensemble of random forest and to include diversity a combination of completely-random random forests and typical random forests are employed (number of trees in each forest is a hyper-parameter). The estimated class distribution, which is obtained by k-fold cv from forests, forms a class vector, which is then concatenated with the original feature vector to be input to the next level of cascade. Second part is a multi-grained scanning for representational learning where spatial and sequential relationships are captured using a sliding window scan (by applying various window sizes) on raw features, similar to the convolution and recurrent layers in DNN. Then, those features are passed to a completely random tree-forest and a typical random forest in order to generate transformed features. When transformed feature vectors are too long to be accommodated, feature sampling can be performed.

Benefits: gcForest has much fewer hyper-parameters than deep neural networks. The number of cascade levels can be adaptively determined such that the model complexity can be automatically set. If growing a new level does not improve the performance, the growth of the cascade terminates. Its performance is quite robust to hyper-parameter settings, such that in most cases and across different data from different domains, it is able to get excellent performance by using the default settings. gcForest achieves highly competitive performance to deep neural networks, whereas the training time cost of gcForest is smaller than that of DNN.

Experimental results: the authors compared the performance of gcForest and DNN by fixing an architecture for gcForest and testing various architectures for DNN, however assumed some fixed hyper-parameters for DNN such as activation and loss function, and dropout rate. They used MNIST (digit images recognition), ORL(face recognition), GTZAN(music classification ), sEMG (Hand Movement Recognition), IMDB (movie reviews sentiment analysis), and some low-dimensional datasets. The gcForest got the best results in these experiments and sometimes with significant differences.

My Opinions: The main goal of the paper is interesting; however one concern is the amount of efforts they put to find the best CNN network for the experiments as they also mentioned that finding a good configuration is an art instead of scientific work. For instance, they could use deep recurrent layers instead of MLP for the sentiment analysis dataset, which is typically a better option for this task. For the time complexity of the method, they only reported it for one experiment not all. More importantly, the result of CIFAR-10 in the supplementary materials shows a big gap between superior deep learning method result and gcForest result although the authors argued that gcForest can be tuned to get better result. gcForest was also compared to non-deep learning methods such as random forest and SVM which showed superior results. It was good to have the time complexity comparison for them as well. In my view, the paper is good as a starting point to answer to the original question, however, the proposed method and the experimental results are not convincing enough.

Github link: https://github.com/kingfengji/gcForest

The authors proposed a end-to-end way to learn how to play a game, which involves both images and text, called GuessWhat?!. They use both supervised learning as a baseline and reinforcement learning to improve their results.

**GuessWhat Rules :**

*From the paper :* "GuessWhat?! is a cooperative two-player game in which both players see the picture of a rich visual scene with several objects. One player – the oracle – is randomly assigned an object (which could be a person) in the scene. This object is not known by the other player – the questioner – whose goal is to locate the hidden object. To do so, the questioner can ask a series of yes-no questions which are answered by the oracle"

**Why do they use reinforcement learning in a dialogue context ?**

Supervised learning in a dialogue system usually brings poor results because the agent only learns to say the exact same sentences that are in the training set. Reinforcement learning seems to be a better option since it doesn't try to exactly match the sentences, but allow more flexibility as long as you get a positive reward at the end. The problem is : In a dialogue context, how can you tell that the dialogue was either "good" (positive reward) or "bad" (negative reward). In the context of the GuessWhat?! game, the reward is easy. If the guesser can find the object that the oracle was assigned to, then it gets a positive reward, otherwise it gets a negative reward.

The dataset is composed of 150k human-human dialogues.

**Models used** 

*Oracle model* : Its goal is to answer by 'yes' or 'no' to the question asked by the agent.
They are concatenating :
- LSTM encoded information of the question asked
- Information about the location of the object (coordinate of the bounding box)
- The object category

Then the vector is fed to a single hidden layer MLP
https://i.imgur.com/SjWkciI.png

*Question model* : The questionner is split in two models :
- The question generation :
    - **Input** : History of questions already asked (if questions were asked before) and the beginning of the question (if this is not the first word of the question) 
    - **Model** : LSTM with softmax
    - **Output** : The next word in the sentence
- The guesser
    - **Input** : The image + all the questions + all the answers
    - **Model** : MLP + softmax
    - **Output** : Selection of one object among the set of all objects in the image.

**Training procedure** :

Train all the components above, in a supervised way.
Once the training is done, you have a dialogue system that is good enough to play on it's own, but the question model is still pretty bad. To improve it, you can train it using REINFORCE Algorithm, the reward being positive if the question model guessed the good object, negative otherwise.

**Main Results :**

The results are given on both new objects (images have been already seen, but the objected selected had never been selected during training) and new images.
The results are in % of the human score, not in absolute accuracy (100% means human-level performance).

|                       | New objects | New images |
|-----------------------|-------------|------------|
| Baseline (Supervised) | 53.4%        | 53%         |
| Reinforce             | 63.2%        | 62%         |

We can improvement using the REINFORCE algorithm. This is mainly because supervised algorithm doesn't know when to stop asking questions and give an answer. On the other hand REINFORCE is more accurate but tends to stop too early (and giving wrong answers)

One last thing to point out regarding the database : The language learned by the agent is still pretty bad, the question are mostly "Is it ... ?" and since the oracle only answers yes/no questions, the interaction is relatively poor.