Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [multitask-learning]

35 questions
15
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1 answer

Multi task learning in Keras

I am trying to implement shared layers in Keras. I do see that Keras has keras.layers.concatenate, but I am unsure from documentation about its use. Can I use it to create multiple shared layers? What would be the best way to implement a simple…
Aditya
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Error on multitask neural nets where all outputs not observed for every example

Let's say I have 2 datasets, each from a set of experiments. Dataset A measures a set of properties X for set S, while dataset B measures properties Y for set T. X and Y are highly correlated, and S and T have (not perfect) overlap. To give an…
jamesmf
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How to do multitask learning using Caffe?

I wonder how to do multitask learning using Caffe. Should I simply use the output layer SigmoidCrossEntropyLoss or EuclideanLoss, and define more than one outputs? E.g. is the following architecture valid (3 outputs, i.e. 3 tasks concurrently…
Franck Dernoncourt
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4 answers

Multi target classification for different types of target variables

I am new to machine learning and I got this task in my university. I have a dataset with over 100 columns and two target variables: $target1$ is categorical i.e. $0$ or $1$ and $target2$ is continuous i.e. values in range $0 \space to \space…
Rajeev Motwani
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4
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Should I rescale losses before combining them for multitask learning?

I have a multitask network taking one input and trying to achieve two tasks (with several shared layers, and then separate layers). One task is multiclass classification using the CrossEntropy loss, the other is sequence recognition using the CTC…
Silver Duck
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4
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Missing outputs in multiple-output neural net

I am looking at a task, where I want to predict multiple things from an image (an animal's breed [categorical], age [continuous number] and gender [categorical]). Unsurprisingly, my first thought was to use a neural network (e.g. adding multiple…
Björn
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Thoughts on improving the Multitask Learning Model

Any Thoughts on improving the Model. So far i was able to achieve around accuracy 0.20 on each task specific dense network of a Multi task Learning Architecture. I have posted model and validation Accuracy Accuracy and Model and validation loss…
James K J
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4
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Multi-task learning for Multi-label classification?

I have a multi-label classification problem wherein each example can belong to one of the pre-defined classes (or can belong to none of them). I was wondering if I can somehow apply multi-task learning (MTL) to this problem. Essentially, treat each…
SHASHANK GUPTA
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Multi-Source Time Series Data Prediction

I was wondering if anyone has experience with time series prediction for data from multiple sources. So for instance, time series $a,b,..,z$ each have their own shape, some may be correlated with others. The ultimate goal is to have a model trained…
Nmaple
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How do we bring Pareto optimality into the realm of Machine Learning?

I have a multi-objective optimisation problem with a large number of objectives (more than 10) which is generally the case in most-real life problems. The use of traditional GAs such as NSGA-II or SPEA-II fails in this scenario because of 'the curse…
thatbangaloreanguy
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What kind of learning problem is this?

Say I have $n$ multi-class classification problems $p_1$, ..., $p_n$. Each of these have their own training data. While they are all distinct problems, there may be similarities in their data (which are in my case images), e.g. the data for class…
Velvet Ghost
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Are more target labels in a multi-label classification always better?

Context We work on medical image segmentation. There are a lot of potential labels for one and the same region we segment. There can be different medically defined labels like anatomical regions, more biological labels like tissue types or spatial…
Spenhouet
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Multi-modal neural network

How do you start with creating a multi-modal neural network?
Nikto
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What is the difference between Multi task learning and domain generalization

I was wondering about the differences between "multi-task learning" and "domain generalization". It seems to me that both of them are types of inductive transfer learning but I'm not sure of their differences.
Milad Sikaroudi
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Emotion Recognition with Multi-task Learning

Introduction I am a beginner in Data Science and currently working on a learning project aimed at emotion recognition from a bio-medical sensor dataset. The dataset consists of 8 sensors data from 20 subjects, here I have attached a screenshot of a…
H.PTavakoli
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