All Questions

I've recently been reading this paper on LIME, an algorithm to interpret ANY machine learning model. I encountered this equation (in red) on page 4 and have just been having a hard time deciphering ...

Recently I trained a BYOL model on a set of images to learn an embedding space where similar vectors are close by. The performance was fantastic when I performed approximate K-nearest neighbours ...

I have been reading about the Schema Theorem - one of the first theorems from the field of evolutionary computing and genetic algorithms, largely responsible for justifying the use of genetic ...

what are the factors that affect the number of kernels and filters on CNN ? like one is 256 sometimes or 128 .. What does this change depend on? what is the difference between kernel and filter in the ...

Suppose I have several journal articles. I would like to train a binary classifier on whether the journal article is insightful. NLP models such as BERT certainly fit my need by scanning the whole ...

I recently read the "paper NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search", which can be found here. I can say that I understood most of the paper but I am not ...

The image below is from a Berkeley AI course pdf I found. My question is, why do the terms accounting for the ghosts and pellets come in raised to the number of units? For example, there are two ...

I've been reading about reinforcement learning, and it seems to me that reinforcement learning assumes the environment is static, and therefore that the reward for taking a particular action will be ...

CONTEXT I'm trying to colorize images with Variational Autoencoder. Input is 256x256 gray image. Output is 256x256x2 as I convert image to a LAB color space and then put gray channel as input and ...

I am working on a problem regarding the multi-classification of multivariate time signals. So I have multiple signals and try to train an algorithm on them. My current approach is to build a neural ...

I have a set of items with their attributes. I want to use this data to train a an open-source model that uses users-items interactions as positive preferences data. Would it be a good idea to ...

I have a problem that can be optimized by taking five actions, and finally, after a series of steps to achieve a solution. The actions (1 to 5) are picked randomly. A time-step (epoch) is concluded ...

I am trying to implement a stochastic policy gradient for continuous actions in a partially observable CartPole environment. Specifically, only the current cart position and pole angle are visible, ...

Say I have a game with 4 base actions [left, right, up, down] and then a value n, which determines how many times the chosen action is repeated. For example, action = left, n = 3 -> go left 3 times....

I've always found bounding box regression a bit weird. There's no positional encoding like in vision transformers, so how does the network "know" the absolute position when producing ...

Since pose estimation is often a task where spatial-temporal context should be helpful in finding subsequent key points, I thought there should be many papers on it. However, I could not find any work ...

Given a one-dimensional training dataset with 3 points, 2 negative points at -1 and 1, and a positive point at 0 (as in the picture above): (a) What solution would minimize the linear Hard Margin SVM ...

I want to know the relation between logic AI and learning AI. Logic AI here refers to the branch of AI that is based on mathematical logic. Learning AI refers to the branch of AI that is based on ...

It is to my understanding that, in deep learning, we are essentially trying to minimize the loss function that we have defined and reach its global optima through some form of optimization technique. ...

I have been given a data set with 30.000 text documents (each text file is rather small with respect to its length and consists in most cases of around 20 sentences), which are labelled with 0 or 1. ...

I have been looking for some explanation that could convince me over the right way of thinking about CV. My challenge is related to the automation of the model configuration process due to same kind ...

A nearest neighbour recommendation engine may, given a query vector identify the ranked top-K nearest vectors within a dataset using a distance measure such as cosine similarity. In my problem domain, ...

I have implemented a reinforcement problem with Open AI gym in Python and I would like to solve it using different algorithms like Deep-Q-Learning and SARSA (any maybe others). My question is which ...

I am running some evaluation metrics using the YOLOv5 object detection algorithm, and wish to calculate my true positives and false positives. For instance, the evaluation metric outputs are as ...

Imagine a car is driving on a long street (= x-axis). The car can go in both directions and it will arbitrarily change its direction. I'm trying to formulate an Inputvector to tell a neural Network ...

Suppose there are two players in my zero-sum game and they play in a row like chess. And I want to learn the policy function using the REINFORCE algorithm. I have doubts about passing reward values in ...

We shall assume that we have a trained neural network model for some task $A$. The dataset used to train the model contained some $n$ features per sample. Using this dataset, we were able to train a ...

I have a timestep dataset of IMU data that I need to normalize. I learned about commonly used methods, but I don't know what I choose. I have a dataset with shape (batch_size, timesteps, features) ... ...

I've been trying to figure out why this model won't train (the accuracy stays at 0). ...

I want to implement a full symbolic integration. To achieve this. I've learned from Prof. Patrick Winston's AI lecture that Matlab uses 12 safe transformations, like that constant out, sums, etc. 12 ...

I am self-studying on optimization algorithm on https://d2l.ai/chapter_optimization/momentum.html and couldn't get my head around some derivation: Instead of the standard gradient descent update ...

If I was Given a set of large training examples (xi,yi), how can training a neural network (NN) via stochastic gradient descent differs from using regular gradient descent in terms of the mathematical ...

I'm new to machine learning and have many questions, but today I want to know if my case can be solved by machine learning, and if the answer is yes, I would like to know what to learn first and which ...

Consider the following algorithm from the textbook titled Reinforcement Learning: An Introduction (second edition) by Richard S. Sutton and Andrew G. Bart While playing the game for the generation of ...

I have a time series (e.g., temperature data) from 1st January 2003 to 31st December 2017 with a one-second sampling rate, which indicates there are about $24 \times 3,600 \times 365 \times15= 473,040,...

I just had a very strange experience where I was training an 8 layer deep & pretty wide (max: 512 neurons) neural network for a regression task. I had assumed since it was big enough that it would ...

In Sutton's 2020 Reinforcement Learning text (in chapter 13.7 Policy Parameterization for Continuous Actions) it's stated actions [may be] chosen from a normal (Gaussian) distribution. However, I ...

In the original DDQN article (https://arxiv.org/pdf/1509.06461.pdf,) the phrase "number of actions" is used twice; First, in the following context: Secondly in Theorem 1. I have a hard ...

For a sequence labeling task (NER), we compute the loss by passing the softmax distribution of the classes (e.g. vocabulary) with the gold label to the loss function (...

I was looking at the following diagram, The reward obtained with SARSA is higher. However, the path that Q learning chooses is eventually the optimal one, isn't it? Why is the SARSA reward higher if ...

Which would be better approach for getting top k embedding vectors such that they are similar to embedding vector A and dissimilar to vector B. Approach 1: calculate ...

Let's say I have a CNN that classifies shirts. And let's say that it performs poorly on shirts that have horizontal stripes. How would I force network to put more emphasis on shirts with horizontal ...

Is it possible that sklearn's k-means algorithm will generate a cluster that has no points at all, given that the number of k is large and the initial centroid is just random? Furthermore, will k-...

I recently read two papers: BYOL Bootstrap your own latent: A new approach to self-supervised Learning DINO Emerging Properties in Self-Supervised Vision Transformers. I am confused about the terms ...

A class of CNN is popular due to the implementation of residual connections. We can use both terms "residual connections" and "skip connections" interchangeably as they refer to ...

I have made a (D)DQN Model. In this model, regardless of whether I initialize it in DDQN or DQN mode, it uses an experience replay memory. The way I gather transitions for this experience replay ...

I've been following a deep learning book and the current section I'm on is about convolutional neural networks. The author presents some code to create a basic CNN with about 1 million parameters, ...

I ask because if RL is indeed an MDP, then there should be absolutely no problem with training an agent on any available episode roll-out data, right? Because an MDP implies for any state S, the ...

I have been running some experiments to see whether a CNN can detect whether two images are the same. However, I can't seem to make it work. I am wondering whether CNNs are not able to do what I am ...

I've recently been looking into language models but ran into a small question out of curiosity. For some language models like GPT-3, it is possible to generate dialogue, with the model basing its ...