All Questions

Decades ago there were and are books in machine vision, which by implementing various information processing rules from gestalt psychology, got impressive results with little code or special hardware ...

Geometry and AI Matrices, cubes, layers, stacks, and hierarchies are what we could accurately call topologies. Consider topology in this context the higher level geometrical design of a learning ...

I know that one of the recent fads right now is to train a neural network to generate screenplays and new episodes of e.g. the Friends or The Simpsons, and that's fine: it's interesting and might be ...

Let's call our dataset splits train/test/evaluate. We're in a situation where we require months of data so we prefer to use the evaluation dataset as infrequently as possible to avoid polluting our ...

Hopfield Nets are able to store a vector and retrieve it starting from a noisy version of it. They do so setting weights in order to minimise the energy function when all neurons are set equal the ...

Furthermore, we observe that mode collapses traditionally plaguing GANs tend to happen very quickly, over the course of a dozen minibatches. Commonly they start when the discriminator overshoots, ...

I need a machine learning algorithm to identify any patterns in a CSV file, which contains details of a cache performance of a CPU workload. More specifically, the CSV file contains columns like ...

The Transformer model introduced in "Attention is all you need" by Vaswani et al. incorporates a so-called position-wise feed-forward network (FFN): In addition to attention sub-layers, each of the ...

I am looking to try different loss functions for a hierarchical multi-label classification problem. So far, I have been training different models or submodels like multilayer perceptron ( MLP )branch ...

I want suggestions on literature on Reinforcement Learning algorithms that perform well with asynchronous feedback from the environment. What I mean by asynchronous ...

My understanding of the vanishing gradient problem in deep networks is that as backprop progresses through the layers the gradients become small, and thus training progresses slower. I'm having a hard ...

In genetic algorithms, a function called "fitness" (or "evaluation") function is used to determine the "fitness" of the chromosomes. Creating a good fitness function is one of the challenging tasks in ...

Most of bibliography consider text classification as the classification of documents. When using bag of words and bayesian classification, they usually use the statistic TFIDF, where TF normalizes the ...

I'm new to neural network, I study electrical engineering, and i just started working with ADALINEs. I use Matlab, and in their Documentation they cite : However, here the LMS (least mean squares) ...

When a neural network learns something from a data set, we are left with a bunch of weights which represent some approximation of knowledge about the world. Although different data sets or even ...

How can we prove that an auto-associator network will continue to perform if we zero the diagonal elements of a weight matrix that has been determined by the Hebb rule? In other words, suppose that ...

I have a set of integers [$c_1$, $c_2$, $c_3$, ... , $c_N$]. A non-negative integer D, greater than a certain threshold, divides each 𝑐𝑖 and leaves remainder 𝑟𝑖,i.e., $r_i$ can be written as $r_i=...

Sort of a conceptual question here. I was implementing a SOM algorithm to better understand its variations and parameters and got curious about one bit: the BMU (best matching unit == the neuron that ...

We have the popular TextRank API which given a text, ranks keywords and can apply summarization given a predefined text length. I am wondering if there is a similar tool for video summarization. ...

I am very beginner to this world. I still learning the basics of Machine learning and AI but i have a problem at hand and i am not sure which technique or Algorithm can be applied on it. I am working ...

This is my first project using machine learning so I'm looking for some guidance. I am extending a model-based testing (MBT) system to a learning-based testing system by integrating a machine learning ...

Suppose there is an evaluation policy called $\pi_{e}$ and there are two behavior policies $\pi_{b1}$ and $\pi_{b2}$. I know that it is possible to estimate the return of policy $\pi_{e}$ through ...

In this article here, the writer claims that a new type of neural net is required to deal with data that is both continuous, and also sparsely sampled. It was my understanding that this was the ...

Problem My problem is the following: Given 1000 wins, losses, and ties from a chess simulation I am using, what shape should each game be (I.e., sequence of moves leading to win/loss/tie) in order to ...

I am new to the object recognition community. Here I am asking about the broadly accepted ways to calculate the error rate of a deep CNN when the network produces different results using the same data....

There is a popular story regarding the back-of-the-envelope calculation performed by a British physicist named G. I. Taylor. He used dimensional analysis to estimate the power released by the ...

I have two classes in the training set: one that has images with a feature and the other of images without that feature. Can there be a LOT more images with "no feature" so I can fit in all possible ...

I am doing a project on Visual Place Recognition in Changing Environments. The CNN used here is mostly AlexNet, and a feature vector is constructed from Layer 3. Does anyone know of similar work ...

I want to design a simple model that predicts the movement of coordinates with RNNs. In a typical three-dimensional LSTM model, one feature is encoded as one hot encoding, and the ...

I installed a local running instance of the ConceptNet5 knowledgebase in an elasticsearch server. I used this data to implement the so-called "Analogietechnik" (a creativity technique to solve a ...

I am trying to think of some marketing-related classification challenges that a feed-forward neural network would be suited for. Any ideas?

I'm working on research in this sector where my supervisor wants to do cannonicalization of name data using VAEs, but I don't think it's possible to do, but I don't know explicitly how to show it ...

Can someone explain to me why it is possible to eliminate the rest of the middle branch in this image for alpha-beta pruning? I am confused because it seems the only information you know is that Helen ...

I have trained a RNN, GRU, and LSTM on the same dataset, and looking at their respective predictions I have observed, that they all display an upper limit on the value they can predict. I have ...

I'm training a multi-label classifier that's supposed to be tested on underwater images. I'm wondering if feeding the model drawings of a certain class plus real images can affect the results badly. ...

In training a neural network, you often see the curve showing how fast the neural network is getting better. It usually grows very fast then slows down to almost horizontal. Is there a mathematical ...

As experiment, I have tried using an autoencoder to encode height data from the alps, however the decoded image is very pixellated after training for several hours as show in the image below. This ...

What is the best way to train / do inference when the context matters highly as to what the inferred result should be? For example in the image below all people are standing upright, but because of ...

After doing some more research, I thought I'd reframe my question a little. I’m looking to make an NLP model that can achieve a dual purpose. One purpose being that it can hold interesting ...

When exactly is a model considered over-parameterized? There are some recent researches in Deep Learning about the role of over-parameterization toward generalization, so it would be nice if I can ...

So I’ve been working on my own little dynamic architecture for a deep neural network (any number of hidden layers with any number of nodes in every layer) and got it solving the XOR problem ...

I have just started playing with Reinforcement learning and starting from the basics I'm trying to figure out how to solve Banana Gym with coach. Essentially ...

In my implementation of Thompson sampling (TS) for online Reinforcement Learning, my distribution for selecting $a$ is $\mathcal{N}(Q(s, a), \frac{1}{C(s,a)+1})$ where $C(s,a)$ is the number of times $...

Imagine a game played on a 10x10 grid system where a player can move up down left or right and imagine there are two players on this grid: An enemy and you. In this game, there are walls on the grid ...

On Sutton and Barto's RL book, the reward hypothesis is stated as that all of what we mean by goals and purposes can be well thought of as the maximization of the expected value of the cumulative ...

Kaggle is limited to only supervised learning problems. There used to be www.rl-competition.org but they've stopped. Is there anything else I can do other than locally trying out different algorithms ...

"Why would the application of boosting prevent underfitting" I read in some paper that applying boosting would prevent you from underfitting. Why is that? Source: http://www.cs.cornell.edu/courses/...

Facebook has just pushed out a bigger version of their multi-lingual language model XLM, called XLM-R. My question is: do these kind of multi-lingual models imply, or even ensure, that their ...

I know this is a very general question, but I'm trying to illustrate this topic to people who are not from the field, and also my understanding is very limited since I'm just a second-year physics ...

I was following some examples to get familiar with tensorflow LSTM related api, but noticed that all LSTM initialization functions require only num_units parameter which denotes number of hidden units ...