Questions tagged [performance]

For question about methods of evaluating the performance (e.g. the accuracy) of an artificial intelligence algorithm or model.

Filter by
Sorted by
Tagged with
0
votes
1answer
25 views

How do I know what a good mean absolute error value is? [closed]

I have just run an MAE calculation for my machine learning models and the results show: SVM MAE = 28.850 deg. Random Forest MAE = 33.832 deg. How do I know what a good MAE value is? What is the ...
0
votes
0answers
15 views

Train a model for different scenario and gather performance results in a single place

Recently, I extend a master's thesis. I am now in a training phase for the model associated with it. I have access to many node GPUs. I would like to train this model on different scenarios, e.g. ...
0
votes
0answers
35 views

Why is the F-beta score not increasing while the validation loss fluctuates?

I'm trying to implement a multi-label image classification from a CT scan data set. The goal of the work is to find out which CT scan image has eleven of the most common fractures if it is fractured. ...
0
votes
0answers
25 views

Identifying if a model is over or under-fitting via graphs

I am working on a Neural Network and have plotted the performance of my model. However the plots seem not to fit the "trends" (which help you identify the issue with your model) presented in ...
0
votes
0answers
80 views

What is the difference between learning agents and other kinds of agents, and more specifically, between performance standard and performance measure?

I am reading AI: A Modern Approach. In the 2nd chapter when introducing different agent types, i.e., reflex, utility-based, goal-based, and learning agents, I understood that all types of agents, ...
2
votes
1answer
40 views

How would the performance of federated learning compare to the performance of centralized machine learning when the data is i.i.d.?

How would the performance of federated learning (FL) compare to the performance of centralized machine learning (ML), when the data is independent and identically distributed (i.i.d.)? Moreover, what ...
1
vote
0answers
60 views

Is the performance of a neural network, which was trained with encrypted data and weights, affected if the weights are decrypted?

Suppose that a neural network is trained with encrypted (for example, with homomorphic encryption and, more precisely, with the Paillier partial scheme) data. Moreover, suppose that it is also trained ...
0
votes
1answer
47 views

Can the attention mechanism improve the performance in the case of short sequences?

I am aware that the attention mechanism can be used to deal with long sequences, where problems related to gradient vanishing and, more generally, representing effectively the whole sequence arise. ...
2
votes
1answer
49 views

How should I change the hyper-parameters of the C51 algorithm, in order to obtain higher reward?

I have a scenario where, in an ideal situation, the greedy approach is the best, but when non-idealities are introduced which can be learned, DQN starts doing better. So, after checking what DQN ...
0
votes
1answer
37 views

How is the performance of a model affected by adding a ReLU to fully connected layers?

How significant is adding a ReLU to fully connected (FC) layers? Is it necessary, or how is the performance of a model affected by adding ReLU to FC layers?
4
votes
2answers
77 views

How do you measure multi-label classification accuracy?

Multi-label assignment is the task in machine learning to assign to each input value a set of categories from a fixed vocabulary where the categories need not be statistically independent, so ...
1
vote
0answers
23 views

Would performance of atomic models matter in ensemble methods?

Suppose I have two fitted ensemble models $F_1 := (f_1, f_2, f_3, \cdots f_n)$ and $G_1 := (g_1, g_2, g_3, \cdots g_n)$. And they were using the same ensemble methods (boosting or bagging). And I am ...
2
votes
0answers
26 views

What is human-level performance for semantic segmentation?

I see so many papers claim to have an algorithm that beats 'human-level performance' for semantic segmentation tasks, but I can't find any papers reporting on what the human-level performance actually ...
0
votes
0answers
139 views

How to calculate the performance measure of a vacuum cleaner agent?

A simple vacuum cleaner agent is having two sensors. One sensor senses the current location of the agent (floor A or floor B). The other sensor senses the status of the current location (Dirty or ...
0
votes
0answers
18 views

Calculating processing time of a deep learning model

My model deals with videos, and I want to calculate how fast it can process frames as in frames per second or processing time for 1 frame. I have made a single function to get predictions, it takes in ...
1
vote
0answers
26 views

How is the performance of a CNN trained with monochrome images on image recognition tasks degraded?

For CNN image recognition tasks, like object recognition/face recognition/object segmentation/posture recognition, are there experiment results about how much will the performance be degraded with ...
0
votes
1answer
150 views

Which kind of data does sigmoid kernel performance well?

While I was playing with some hyperparameters, I came to a wired situation. My dataset is IRIS dataset to be specific. SVM algorithm has some hyperparameters that we can tune, such as Kernels, and C ...
1
vote
0answers
37 views

How many training runs are needed to obtain a credible value for performance?

I'm trying to optimize a neural network. For that, I'm changing parameters like the batch size, learning rate, weight initialization, etc. A neural network is not a deterministic algorithm, so, in ...
1
vote
4answers
350 views

Is it normal to have the root mean squared error greater on the test dataset than on the training dataset?

I am new to deep learning. I am training a model and I am getting a root mean squared error (RMSE) greater on the test dataset than on the training dataset. What could be the reason behind this? Is ...
1
vote
1answer
44 views

How to estimate the accuracy upper limit of any CNN model over a computer vision classification task

We are given a computer vision classification task, that is, a task that asks us to predict the category of an image over $n$ predefined classes (the so-called closed set classification problem). ...
2
votes
0answers
31 views

Can the addition of low-quality images to the training dataset increase the network performance?

I already trained a deep neural network called YOLO (You Only Look Once) with high-quality images (1920 by 1080 pixels) for a detection task. The result for mAP and IOU were 93% and 89% respectively. ...
1
vote
1answer
841 views

How can I merge outputs of two separate layers so that the overall performance improves?

I am training a combined model (fine-tuned VGG16 for images and shallow FCN for numerical data) to do a binary classification. However, the overall AUC score is not what I expected it to be. Image-...
1
vote
0answers
38 views

Sample size for the evaluation of Deep Learning Models

I'm evaluating the performance and accuracy in detecting objects for my data set using three deep learning algorithms. In total there are 24,085 images. I measure the performance in terms of time ...
1
vote
0answers
23 views

What are the properties of a model that is well suited for for high performance real-time inference

What are general best practices or considerations in designing a model that is optimized for real-time inference?
1
vote
1answer
623 views

Why is the effective branching factor used for measuring performance of a heuristic function?

For search algorithms with heuristic functions, performance of heuristic functions are measured by effective branching factor ${b^*}$ which involves total nodes expanded ${N}$ and depth of the ...
1
vote
0answers
17 views

Quantization techniques and new GPU architectures [closed]

Quantization means using low resolution formats for some variables some of the time: binary (e.g. BinaryConnect), ternary, etc. The Turing architecture recently introduced by Nvidia is much faster ...
1
vote
1answer
60 views

What will change when workstations will have ARM Machine Learning Processors onboard?

lately we read that many manufacturers are forcing ARM architectures to be used on future workstations. One of ARM's recent announcements is a machine learning processor. What will change in terms of ...
0
votes
0answers
81 views

What to do when an image classifier does good for a class but bad for another?

So I wrote a convolutional neural network for a binary image classification. I have around 5300 images for each class which I thought would be enough to at least give me a good accuracy on the ...
5
votes
1answer
49 views

How can we conclude that an optimization algorithm is better than another one

When we test a new optimization algorithm, what the process that we need to do?For example, do we need to run the algorithm several times, and pick a best performance,i.e., in terms of accuracy, f1 ...
1
vote
0answers
21 views

Improving Recall of a Certain Class

Let's say that we have a test data set with $20,000$ observations for which we want to make a binary prediction for. When we apply our best trained model to this data set (e.g. logistic regression ...
4
votes
1answer
164 views

How to evaluate an RL algorithm when used in a game?

I'm planning to create a web-based RL board game, and I wondered how I would evaluate the performance of the RL agent. How would I be able to say, "Version X performed better than version Y, as ...
2
votes
0answers
17 views

In addition to matrix algebra, can GPU's also handle the various Kernel functions for Neural Networks?

I've read a number of articles on how GPUs can speed up matrix algebra calculations, but I'm wondering how calculations are performed when one uses various kernel functions in a neural network. If ...
1
vote
0answers
33 views

Why does precision-recall curve become more stable when neural net begins to overfit?

I am training a convLSTM with a dropout layer (with prob 0.5). If I train over more than 5 epochs I notice that the network starts to overfit: my validation set loss becomes stationary while the ...
5
votes
2answers
141 views

How do I improve accuracy and know when to stop training?

I am training a modified VGG-16 to classify crowd density (empty, low, moderate, high). 2 dropout layers were added at the end on the network each one after one of the last 2 FC layers. network ...
3
votes
1answer
141 views

What are the aspects that most impact on the inference time for neural networks in embedded systems?

I work with neural networks for real-time image processing on embedded softwares and I tested different architectures (Googlenet, Mobilenet, Resnet, custom networks...) and different hardware ...
7
votes
1answer
500 views

Why isn't the ElliotSig activation function widely used?

The Softsign (a.k.a. ElliotSig) activation function is really simple: $$ f(x) = \frac{x}{1+|x|} $$ It is bounded $[-1,1]$, has a first derivative, it is monotonic, and it is computationally ...
2
votes
0answers
40 views

Performance measure on windowed time series data

I have time series data where I use a sliding window to detect anomalies in those windows. A sliding window is an interval of the dataset that steps one datapoint for each iteration. Datapoints are ...
2
votes
1answer
147 views

How can we compare the intelligence of AI systems?

One way of ranking human intelligence is based on IQ tests. But how can we compare the intelligence of AI systems? For example, is there a test that tells me that a spam filter system is more ...
1
vote
0answers
32 views

How to visualize/interpret text prediction model results?

I am using LSTM model to predict the next xml markup from an input seed. I have trained my model on 1500 xml files. Each xml file is generated randomly. I am wondering if there is a way to visualize ...
2
votes
1answer
110 views

How do I check that the combination of these models is good?

I've selected more than 10 discriminative (classification) models, each wrapped with a BaggingClassifier object, optimized with a ...
4
votes
1answer
108 views

Relative compute time for each type of layer in a neural network

Hello, I would like to know whether this picture from the paper: Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability valid? Questions: 1) Does ...
10
votes
3answers
1k views

What size of neural networks can be trained on current consumer grade GPUs? (1060,1070,1080)

is it possible to give a rule of thumb estimate about the size of neural networks that are trainable on common consumer grade GPUs? For example: The Emergence of Locomotion (Reinforcement) paper ...
5
votes
2answers
2k views

Does the quality of training images affect the accuracy of the neural network?

I just got into AI few months ago. I noticed most of the images in training datasets are usually low quality( almost pixelated). Does the quality of training images affect the accuracy of the neural ...
6
votes
4answers
493 views

What does deep learning offer with respect to standard machine learning?

I've been reading a lot about DL. I can understand to an extent how it works, in theory at least, and how it's technically different from conventional ML. But what I'm looking for is more of a "...
2
votes
1answer
433 views

Can ConvNets be used for real-time object recognition from video feed?

Convolutional neural network are leading type of feed-forward artificial neural network for image recognition. Can they be used for real-time image recognition for videos (frame by frame), or it takes ...
7
votes
2answers
536 views

Why is the generation of deep style images so slow and resource-hungry?

Consider these neural style algorithms which produce some art work: Neural Doodle neural-style Why is generating such images so slow and why does it take huge amounts of memory? Isn't there any ...
11
votes
4answers
2k views

What is the “dropout” technique?

What purpose does the "dropout" method serve and how does it improve the overall performance of the neural network?