All Questions

I would like the community to help me understand if the following example would be better represented as episodic or continuous task, this will help me structure the problem and chose the right RL ...

I am trying to develop a "simple" announcer for sports segments that mainly consists of events like goals, fouls, substitutions, and many other events that could happen in many sports. The ...

Why is Acme using own initializer for both tanh and ELU, when commonly used for tanh is Xavier and for ELU is He initializer? What mathematics is behind them? Here is the code. ...

This training of all layers of a CNN simultaneously is standard practice today. It is found in every CNN (AlexNet (2012), VGG, Inception, GANs, etc) and even pre-CNN networks such as Le et al. 2012. ...

In the data preparation phase, we have to divide the dataset into two parts: the training dataset and the test dataset. I have seen this post regarding the time complexity for training a model. ...

I need to use the automatic caption from Youtube to precisely isolate excerpts from the video aligned to text and generate the dataset to train a model in French. So I've already written the script, ...

I'm training a Tensorflow model that receives an image and segments the image into foreground and background. That is, if the input image is w x h x 3, then the ...

I am confused among the following in selecting the batch size for my model. #1: powers of 2 I generally see that batch sizes are in powers of two: 32, 64, 128, 256. #2: maximum GPU Suppose my GPU ...

What are knowledge graph embeddings? How are they useful? Are there any extensive reviews on the subject to know all the details? Note that I am asking this question just to give a quick overview of ...

EfficientDet outputs classes and bounding boxes. My question is about both but specifically I am interested in the class prediction net part. In the paper's diagram it shows 2 conv layers. I don't ...

I am trying to build a CNN model based on the concepts of Contrastive Learning. In specific based on Triplet loss. I have 5 different class labels and I create triplets such that in a triplet, two ...

While reading a textbook, I came across a story as follows A research project was to develop a logic-based inferring system. After the completion of the project, the system gave a terrible result on ...

Smart contracts (SC) are programs developed for Ethereum Blockchain initially. They are used for transferring Ether and subsequently can be applied in place of Banking transactions and credit cards. ...

Neural networks consist of so many parameters. Researchers could create as many possible neural networks as they wish. So I want to ask a general question. Could we devise an evolutionary algorithm ...

The following are the two types are projections that are generally used in image processing Affine transformation Projective transformation Affine transformation is a backbone operation in neural ...

I'm currently working on an AI playing Mandarin using Minimax. I can extract this dictionary from a game where 2 AI with different depth play with each other: ...

I am reading some material on meta-learning and I'm not completely sure about some things. Here's what I'm reading: I would like to know exactly what meta-testing is doing exactly and check if my ...

I'm trying to do multi-agent reinforcement learning on the grid world navigation task where multiple agents try to collectively reach multiple goals while avoiding collisions with stationary obstacles ...

In AI literature, deterministic vs stochastic and being fully-observable vs partially observable are usually considered two distinct properties of the environment. I'm confused about this because what ...

My question is about the relevance of concept size to the polynomial-time/example constraints in efficient PAC-learning. To ask my question precisely I must first give some definitions. Definitions: ...

I am doing a side project, where I am planning on recording with a bad mic and a good mic concurrently, and am trying to make a model to map your low quality audio to the high quality space. First ...

This is regarding the details stated in Wikipedia. I am reading optical flow in Computer Vision. I understood the Horn–Schunck method as such, but did not get how it is related to the aperture problem,...

Suppose there are some objects with features, and the target is parametric density estimation. Density estimation is model-based. Parameters are obtained by maximizing log-likelihood. $LL = \sum_{i \...

Now I am working on building a deep learning model for a regression problem. I used 50 inputs and try to add one new categorical input. The problem is that this one input is much more important than ...

ImageNet dataset is an established benchmark for the measurement of the performance of CV models. ImageNet involves 1000 categories and the goal of the classification model is to output the correct ...

Say I have a game of blackjack, and I am trying to teach a single forward-pass neural network to approximate the Q value of the current state and action. There are 3 inputs: The current card in hand, ...

I am learning about Reinforcement learning in the book Grokking Deep Reinforcement Learning. Below are snippets. Below is the description of Bandit Slippery Walk (BSW) Below is the description of two ...

While reading the chapter titled "Sequence Modeling: Recurrent and Recursive Nets" from the textbook named Deep Learning by Ian Goodfellow et al, I came across a subsection 10.2.3 titled &...

I have some data with ground truth that looks like a binary step function, where part of it is 0 and part is one. An example for the GT can be like ...

If an RNN is trained using only the teacher forcing, then the network takes the actual output from the previous time step as input to the hidden state the next time step. We know that the actual ...

The dataset is of microscopic cells. The data format is that it comes with annotations of the center point location of each cell. Usually, the object detection/tracking dataset comes with a bounding ...

The Mereology Theory below contains three first-order axioms that represent a part of a mereology theory. For this posting, it is important that the set of axioms should be considered as a theory. ...

I'm trying to implement a solution in python to detect skin in an image. I'm evaluating the Mask R-CNN model to create a mask on the skin (not on clothes). The problem is that every solution I have ...

I understand that FLOPS means floating-point operations per second, and throughput is the number of inputs (for example, images) per second. If a model has higher FLOPS, it means it performs faster. ...

Below are the two tensors ...

Is it possible to train dynamical variational autoencoders, such as Kalman Variational Autoencoders (KVAE), Recurrent Variational Autoencoders (RVAE), or Disentangled Sequential Autoencoders (DSAE) on ...

I came across two types of RNN while reading the chapter titled Sequence Modeling: Recurrent and Recursive Nets of the textbook named Deep Learning by Ian Goodfellow et al. First type: Recurrent ...

I have a dataset where I am recording temperature after every 4milliseconds till 500 and another feature "conductivity value". The length of the dataset is around a 1000 rows. I need to find ...

I've explored tools like amazon personalize, etc. for generating recommendations. It seems like amazon personalize is appropriate when all the content is with the company/a single entity. For example, ...

RNN has the same capability as a universal Turing machine. But I am confused whether RNN holds the same capabilities if we use teacher forcing. Consider the following excerpts from paragraphs taken ...

Is there a deep learning architecture where outputs of the same model with two different inputs are used for error calculation (backpropagation)? Workflow: Input1 -----> Model ------> Output1 ...

In Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes ...

This topic has been introduced in "Pattern Recognition and Machine Learning, Bishop, 2006", section 5.4.1. I am a bit confused about this method and I have two questions. Why this method ...

Implementations of variational autoencoders that I've looked at all include a sampling layer as the last layer of the encoder block. The encoder learns to generate a mean and standard deviation for ...

I am not very learned in the realm of ai and coding, but want to try to learn! There's a specific type of model I'm looking for but don't know how to find. I want to see if ai can predict the chances ...

I have an unusual but very interesting problem. I have a game that is very similar to Toon Blast (a puzzle mobile game). It's based on a Match-2 mechanic in which you can destroy 2 or more connected ...

Many people have heard of Hinton, Bengio, and LeCun in recent years, given the popularity of deep learning and neural networks, and their contributions to this subfield of Artificial Intelligence. For ...

Post-Interstellar, my aptitude shifted in terms of sci-fi. I no longer can enjoy excessively speculative, unfounded predominantly fantasy subordinately scientific story telling, and imagery. I used to ...

Consider the following excerpt paragraph taken from the section titled "Recurrent Neural Networks" of the chapter 10: Sequence Modeling: Recurrent and Recursive Nets of the textbook named ...

The task is to detect rotated alphanumeric characters embedded on colored shapes. We will have an aerial view of the object (from a UAS: Unarmed Aerial System), something of this sort: (One Uppercase ...