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The main contribution of [Understanding the difficulty of training deep feedforward neural networks](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) by Glorot et al. is a **normalized weight initialization** $$W \sim U \left [  \frac{\sqrt{6}}{\sqrt{n_j + n_{j+1}}}, \frac{\sqrt{6}}{\sqrt{n_j + n_{j+1}}} \right ]$$ where $n_j \in \mathbb{N}^+$ is the number of neurons in the layer $j$. Showing some ways **how to debug neural networks** might be another reason to read the paper. The paper analyzed standard multilayer perceptrons (MLPs) on a artificial dataset of $32 \text{px} \times 32 \text{px}$ images with either one or two of the 3 shapes: triangle, parallelogram and ellipse. The MLPs varied in the activation function which was used (either sigmoid, tanh or softsign). However, no regularization was used and many minibatch epochs were learned. It might be that batch normalization / dropout might change the influence of initialization very much. Questions that remain open for me: * [How is weight initialization done today?](https://www.reddit.com/r/MLQuestions/comments/4jsge9) * Figure 4: Why is this plot not simply completely dependent on the data? * Is softsign still used? Why not? * If the only advantage of softsign is that is has the plateau later, why doesn't anybody use $\frac{1}{1+e^{0.1 \cdot x}}$ or something similar instead of the standard sigmoid activation function?
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The paper discusses a number of extensions to the Skipgram model previously proposed by Mikolov et al (citation [7] in the paper): which learns linear word embeddings that are particularly useful for analogical reasoning type tasks. The extensions proposed (namely, negative sampling and subsampling of high frequency words) enable extremely fast training of the model on large scale datasets. This also results in significantly improved performance as compared to previously proposed techniques based on neural networks. The authors also provide a method for training phrase level embeddings by slightly tweaking the original training algorithm. This paper proposes 3 improvements for the skipgram model which allows for learning embeddings for words. The first improvement is subsampling frequent word, the second is the use of a simplified version of noise constrastive estimation (NCE) and finally they propose a method to learn idiomatic phrase embeddings. In all three cases the improvements are somewhat adhoc. In practice, both the subsampling and negative samples help to improve generalization substantially on an analogical reasoning task. The paper reviews related work and furthers the interesting topic of additive compositionality in embeddings. The article does not propose any explanation as to why the negative sampling produces better results than NCE which it is suppose to loosely approximate. In fact it doesn't explain why besides the obvious generalization gain the negative sampling scheme should be preferred to NCE since they achieve similar speeds. 
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(See also a more thorough summary in [a LaTeX PDF][1].) This paper has some nice clear theory which bridges maximum likelihood (supervised) learning and standard reinforcement learning. It focuses on *structured prediction* tasks, where we want to learn to predict $p_\theta(y \mid x)$ where $y$ is some object with complex internal structure. We can agree on some deficiencies of maximum likelihood learning:  ML training fails to assign **partial credit**. Models are trained to maximize the likelihood of the groundtruth outputs in the dataset, and all other outputs are equally wrong. This is an increasingly important problem as the space of possible solutions grows.  ML training is potentially disconnected from **downstream task reward**. In machine translation, we usually want to optimize relatively complex metrics like BLEU or TER. Since these metrics are nondifferentiable, we have to settle for optimizing proxy losses that we hope are related to the metric of interest. Reinforcement learning offers an attractive alternative in theory. RL algorithms are designed to optimize nondifferentiable (even stochastic) reward functions, which sounds like just what we want. But RL algorithms have their own problems with this sort of structured output space:  Standard RL algorithms rely on samples from the model we are learning, $p_\theta(y \mid x)$. This becomes intractable when our output space is very complex (e.g. 80token sequences where each word is drawn from a vocabulary of 80,000 words).  The reward spaces for problems of interest are extremely sparse. Our metrics will assign 0 reward to most of the 80^80K possible outputs in the translation problem in the paper.  Vanilla RL doesn't take into account the groundtruth outputs available to us in structured prediction. This paper designs a solution which combines supervised learning with a reinforcement learninginspired smoothing method. Concretely, the authors design an **exponentiated payoff distribution** $q(y \mid y^*; \tau)$ which assigns high mass to highreward outputs $y$ and low mass elsewhere. This distribution is used to effectively smooth the loss function established by the groundtruth outputs in the supervised data. We end up optimizing the following objective: $$\mathcal L_\text{RML} =  \mathbb E_{x, y^* \sim \mathcal D}\left[ \sum_y q(y \mid y^*; \tau) \log p_\theta(y \mid x) \right]$$ This optimization depends on samples from our dataset $\mathcal D$ and, more importantly, the stationary payoff distribution $q$. This contrasts strongly with standard RL training, where the objective depends on samples from the nonstationary model distribution $p_\theta$. To make that clear, we can rewrite the above with another expectation: $$\mathcal L_\text{RML} =  \mathbb E_{x, y^* \sim \mathcal D, y \sim q(y \mid y^*; \tau)}\left[ \log p_\theta(y \mid x) \right]$$ ### Model details If you're interested in the lowlevel details, I wrote up the gist of the math in [this PDF][1]. ### Analysis #### Relationship to label smoothing This training approach is mathematically equivalent to label smoothing, applied here to structured output problems. In nextword prediction language modeling, a popular trick involves smoothing the target distributions by combining the groundtruth output with some simple base model, e.g. a unigram word frequency distribution. (This just means we take a weighted sum of the onehot vector from our supervised data and a normalized frequency vector calculated on some corpus.) Mathematically, the cross entropy with label smoothing is $$\mathcal L_\text{MLsmooth} =  \mathbb E_{x, y^* \sim \mathcal D} \left[ \sum_y p_\text{smooth}(y; y^*) \log p_\theta(y \mid x) \right]$$ (The equation above leaves out a constant entropy term.) The gradient of this objective looks exactly the same as the rewardaugmented ML gradient from the paper: $$\nabla_\theta \mathcal L_\text{MLsmooth} = \mathbb E_{x, y^* \sim \mathcal D, y \sim p_\text{smooth}} \left[ \log p_\theta(y \mid x) \right]$$ So rewardaugmented likelihood is equivalent to label smoothing, where our smoothing distribution is logproportional to our downstream reward function. #### Relationship to distillation Optimizing the rewardaugmented maximum likelihood is equivalent to minimizing the KL divergence $$D_\text{KL}(q(y \mid y^*; \tau) \mid\mid p_\theta(y \mid x))$$ This divergence reaches zero iff $q = p$. We can say, then, that the effect of optimizing on $\mathcal L_\text{RML}$ is to **distill** the reward function (which parameterizes $q$) into the model parameters $\theta$ (which parameterize $p_\theta$). It's exciting to think about other sorts of more complex models that we might be able to distill in this framework. The unfortunate (?) restriction is that the "source" model of the distillation ($q$ in this paper) must admit to efficient sampling. #### Relationship to adversarial training We can also view rewardaugmented maximum likelihood training as a data augmentation technique: it synthesizes new "partially correct" examples using the reward function as a guide. We then train on all of the original and synthesized data, again weighting the gradients based on the reward function. Adversarial training is a similar data augmentation technique which generates examples that force the model to be robust to changes in its input space (robust to changes of $x$). Both adversarial training and the RML objective encourage the model to be robust "near" the groundtruth supervised data. A highlevel comparison:  Adversarial training can be seen as data augmentation in the input space; RML training performs data augmentation in the output space.  Adversarial training is a **modelbased data augmentation**: the samples are generated from a process that depends on the current parameters during training. RML training performs **databased augmentation**, which could in theory be done independent of the actual training process.  Thanks to Andrej Karpathy, Alec Radford, and Tim Salimans for interesting discussion which contributed to this summary. [1]: https://drive.google.com/file/d/0B3Rdm_P3VbRDVUQ4SVBRYW82dU0/view 
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This very new paper, is currently receiving quite a bit of attention by the [community](https://www.reddit.com/r/MachineLearning/comments/5qxoaz/r_170107875_wasserstein_gan/). The paper describes a new training approach, which solves the two major practical problems with current GAN training: 1) The training process comes with a meaningful loss. This can be used as a (soft) performance metric and will help debugging, tune parameters and so on. 2) The training process does not suffer from all the instability problems. In particular the paper reduces mode collapse significantly. On top of that, the paper comes with quite a bit mathematical theory, explaining why there approach works and other approachs have failed. This paper is a must read for anyone interested in GANs. 
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In object detection the boost in speed and accuracy is mostly gained through network architecture changes.This paper takes a different route towards achieving that goal,They introduce a new loss function called focal loss. The authors identify class imbalance as the main obstacle toward one stage detectors achieving results which are as good as two stage detectors. The loss function they introduce is a dynamically scaled cross entropy loss,Where the scaling factor decays to zero as the confidence in the correct class increases. They add a modulating factor as shown in the image below to the cross entropy loss https://i.imgur.com/N7R3M9J.png Which ends up looking like this https://i.imgur.com/kxC8NCB.png in experiments though they add an additional alpha term to it,because it gives them better results. **Retina Net** The network consists of a single unified network which is composed of a backbone network and two task specific subnetworks.The backbone network computes the feature maps for the input images.The first subnetwork helps in object classification of the backbone networks output and the second subnetwork helps in bounding box regression. The backbone network they use is Feature Pyramid Network,Which they build on top of ResNet. 