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6

When crossover happens and one parent is fitter than the other, the nodes from the more fit parent are carried over to the child. This is the case as disjoint and excess genes are only carried over from the fittest parent. Here's an example: // Node Crossover Parent 1 Nodes: {[0][1][2]} // more fit parent Parent 2 Nodes: {[0][1][2][3]} Child Nodes: {[0]...


4

A non-starving policy is a (behavior) policy that is theoretically guaranteed to visit each state and take all possible actions from each state an infinite number of times, so that to always update $Q(s, a)$, $\forall s, \forall a$, an infinite number of times. In the context of off-policy prediction, this criterion implies that any trajectory will have no ...


4

In language theory, there are generally several admitted levels that can be studied in relation with one another or independently. The semantic level is the one dealing with the meaning of the text ("semantic" comes from the greek and means "to signify"). The semantic level is therefore generally independent from the syntax and even the language used to ...


3

The square brackets $[]$ in $[\tau_{ij}]^\alpha$ and $[\eta_{ij}]^\beta$ may be just a way of emphasing that the elements $\tau_{ij} \in \mathbb{R}$ and $\eta_{ij} \in \mathbb{R}$ of respectively the matrices $\mathbf{\tau} \in \mathbb{R}^{n \times n}$ and $\mathbf{\eta} \in \mathbb{R}^{n \times n}$ (where $n$ is the number of nodes in the graph) are ...


3

locality of pixel dependencies probably means that neighboring pixels tend to be correlated, while faraway pixels are usually not correlated. This assumption is usually made in several image processing techniques (e.g. filters). Of course, the size and the shape of the neighborhood could vary, depending on the region of the image (or whatever), but, in ...


3

It was difficult to find because recurrent network designs predate LSTM extensions of that earlier idea by decades. Although the term recurrent was not yet used as a primary description of the technology advancement, recurrence was an essential feature of the theoretical treatment of artificial networks that learned actions in Attractor dynamics and ...


3

Dennis Soemers provides an important point that from a theoretical standpoint, this can be seen as a non-issue. However, what you bring up is an important practical issue of potential-based reward shaping (PBRS). The issue is actually worse than you describe---it's more general than s = s'. In particular, the issue presents itself differently based on the ...


3

I don't think the situation you're sketching should be a problem at all. If $P(s)$ is high (e.g. $P(s) = 1000$), this means (according to your shaping / "heuristic") that it's valuable to be in the state $s$, that you expect to be able to get high future returns from that state. If you then continuously take actions that keep you in the same state $s$, it ...


2

The paper you are citing is the paper that introduced the cascaded convolution neural network. In fact, in this paper, the authors say To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN). This combination requires the introduction of a new input feature which ...


2

Your interpretation is quite correct. I could not understand how it would speed up the convergence though. What they are doing is basically re-assigning the magnitude of the weight vector (also called norm of the weight vector). To put things in the perspective, the conventional approach to any Machine Learning cost function is to not only check the ...


2

Poel's paper on Translating Values into Design Requirements articulates a framework for mapping abstract values and norms into concrete design constraints that an engineer could work with. The example used in the paper is mapping beliefs about animal welfare to design constraints on chicken coups. The newer paper by Tubella et al. on Governance by Glass-...


2

So this paper is by google, but is very similar where they use 2D positional embeddings and perform MHA on the flattened image. Are you talking about Attention Augmented Convolutional Networks


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In laymen's terms, a non-expansive operator is a function that brings points closer together or at least no further apart. An example of a non-expansive operator is the function $f(x) = x/2$. The two numbers $0$ and $5$ are a distance of $5$ apart. The two output numbers $f(0) = 0$ and $f(5) = 2.5$ are 2.5 apart (which is smaller than $5$ apart). It is easy ...


2

First of all you made a mistake, equation 8 in the paper is defined with $\frac{\partial L(\theta)}{\partial s_t}$ not $\frac{\partial L(\theta)}{\partial\theta}$. Loss is defined as: $L(\theta) = - \mathbb{E}_{w^s \sim p_{\theta}}[r(w^s)]$ If we use definition of expectation (for discrete case): $\mathbb{E}[X] = \sum\limits_{i} p_i(x_i)x_i$ we get ...


2

The adjective bad isn't mathematically descriptive. A better term is sub-optimal, which implies the state of learning might appear optimal based on current information but the optimal solution from among all possibilities is not yet located. Consider a graph representing a loss function, one of the names to measure disparity between the current learning ...


1

The impossibility is referring how to learn the disentangled representations from the observed distribution or to know whether you have a disentangled representation in the first place. Basically, an unsupervised learning agent tasked with learning a disentangled transformation of some features $\mathbf{z}$ needs to infer a set of features from the data ...


1

An identity recurrent neural network (IRNN) is a vanilla recurrent neural network (as opposed to e.g. LSTMs) whose recurrent weight matrices are initialized with the identity matrix, the biases are initialized to zero, and the hidden units (or neurons) use the rectified linear unit (ReLU). An IRNN can be trained more easily using gradient descent (as ...


1

$F$ in this context is the output of the Convolutional Neural Network that's being trained, which is of the same size as $X$.


1

These answers are based on my personal understanding of Bert from both the paper and official_implementation, hope it will help: What do they mean by "maximum scoring span is used as the prediction"? As you know in SQuAD the input sequence is divided to 2 parts: Question and Document (from which we extract the answer if possible). Sometimes the input ...


1

Replacement you suggest is replacement of random variable by its expectation in forward part of TD. It would make IQN into modification of C51 with randomly sampled function approximator instead of discrete distribution. Both distribution produced and especially exploration behavior with your replacement would be very different. The authors of paper ...


1

Suppose that $X$ and $Y$ are metric spaces. A metric space is a set equipped with a metric, which is a function that defines the intuitive notion of distance between the elements of the set. For example, the set of real numbers, $\mathbb{R}$, equipped with the metric induced by the absolute value function (which is a norm). More precisely, the metric $d$ can ...


1

Input vector contains two concatenated parts. The low part represents the current word: word in time t encoded using 1-of-N coding [...] - size of vector x is equal to size of vocabulary V (this can be in practice 30 000-200 000) plus [...] where, as you said, 1-of-N means (see here, 1-of-V): If you have a fixed-size vocabulary of symbols with V ...


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