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15
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0answers
270 views

Formal proof of vanilla policy gradient convergence

So I stumbled upon this question, where the author asks for a proof of vanilla policy gradient procedures. The answer provided points to some literature, but the formal proof is nowhere to be included....
12
votes
0answers
1k views

Understanding Timestamps and Batchsize of Keras LSTM considering Hiddenstates and TBPTT

What I'm trying to do What I am trying to do is predicting the next data-point $x_t$ for each point in the timeseries $[x_0, x_1, x_2,...,x_T]$ in the context of a date-stream in real-time, in theory ...
7
votes
0answers
1k views

AdaBoost implementation and tuning for high dimensional feature space in R

I am trying to implement the AdaBoost.M1 algorithm (trees as base-learners) to a data set with a large feature space (~ 20.000 features) and ~ 100 samples in R. ...
6
votes
0answers
131 views

Is ground truth fit worse than cross validated fit on noisy data?

I am having these weird results when playing around with cross validation that I would greatly appreciate to have any comments. Briefly, I have a lower mean squared error (MSE) when doing regression (...
6
votes
0answers
141 views

Are there any graph embedding algorithms like this already?

I wrote an algorithm for generating node embeddings based on the graph's topology. Most of the explanation is done in the readme file and the examples. The question is: Am I reinventing the wheel? ...
6
votes
0answers
103 views

bias and variance trade off related question

I am having difficulty to understand the expected squared errors formula in this website: $y=f(x)+e$ true regression line $\hat{y}=\hat{f}(x)$ your estimated regression line $error(x)=\bigg(\...
6
votes
0answers
2k views

Why is my Keras model not learning image segmentation?

Edit: as is turns out, not even the model's initial creator could successfully fine-tune it. This is most likely a problem of implementation, or possibly related to the non-intuitive way in which the ...
6
votes
0answers
2k views

Python : Feature Matching + Homography to find Multiple Objects

I'm trying to use OpenCV via Python to find multiple objects in a train image and match it with the key points detected from a query image. For my case, I'm trying to detect the tennis courts in the ...
6
votes
0answers
892 views

Comparing transition matrices for Markov chains

I have a population, each unit of which exists in one of several states that change over time. I am using first-order Markov chains to model these state transitions. My population can be segmented ...
6
votes
0answers
1k views

How does Seaborn calculate error bars when using estimators other than the arithmetic mean?

If I create a barplot using Seaborn and specify the geometric mean or the median as the estimator, does Seaborn know to use the appropriate standard error formula to create error bars?
5
votes
0answers
354 views

Gensim LDA model: return keywords based on relevance (λ - lambda) value

I am using gensim library for topic modeling, more specifically LDA. I have created my corpus, my dictionary and my lda model, and with the help of pyLDAvis library I visualize the results. When I ...
5
votes
0answers
131 views

When is the sum of models the model of the sum?

The response variable in a regression problem, $Y$, is modeled using a data matrix $X$. In notation, this means: $Y$ ~ $X$ However, $Y$ can be separated out into different components that can be ...
5
votes
0answers
2k views

Tensorflow v1 Dataset API AttributeError with ndim

I'd like to make pipeline for optimizing Gpu and Cpu. Dataset:https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant It's about 10000 datapoint and 4 description variables for regression ...
5
votes
0answers
56 views

Training value neural network AlphaGo style

I have been trying to replicate the results obtained by AlphaGo following their supervise learning protocol. The papers specify that they use a network that has two heads: a value head that predicts ...
5
votes
0answers
2k views

Loss function for optimising precision & recall / sensitivity & specificity?

I've been using precision and recall as my metrics, as per keras-team/keras/pull/9393/files Sensitivity & specificity is what I want to optimise for. Every epoch I output it: ...
5
votes
0answers
472 views

differences between LSQR and FTRL when working with very sparse data

I have a 2M instances dataset with millions of very very sparse dummy variables created using the hashing trick = ...
4
votes
0answers
98 views
+50

Who invented the concept of over-fitting?

The wikipedia article on overfitting references to Oxford dictionary entry that claims: "Origin 1930s; earliest use found in Quarterly Review of Biology. From over- + fitting." It is ...
4
votes
0answers
24 views

Exploring variables to guide xgboost tuning

In short: How to think about the type and distribution of my variables when choosing parameter values for xgboost? Context: I have a dataset which I want to classify using the ...
4
votes
0answers
47 views

XGBoost skews towards minority class

I have a dataset with 85k positive labels and 53k negative labels. For this use-case, I am trying to maximize my efforts to the negative class (accurately identify true negatives, and minimize false ...
4
votes
0answers
106 views

How to achieve SHAP values for a CatBoost model in R?

I'm asked to create a SHAP analysis in R but I cannot find it how to obtain it for a CatBoost model. I can get the SHAP values of an XGBoost model with ...
4
votes
0answers
104 views

Seaborn Histogram: Unexpected gap at center of data

I'm going through this YouTube series on simulation by The Coding Train. I'm trying to graph some filtered random numbers, but seaborn is leaving an odd gap in the very middle of the histogram. My ...
4
votes
0answers
191 views

How to detect vanishing and exploding gradients with Tensorboard?

I have two "sub-questions" 1) How can I detect vanishing or exploding gradients with Tensorboard, given the fact that currently write_grads=True is deprecated in the Tensorboard callback as per "un-...
4
votes
0answers
499 views

Tensorflow, Optimizer.apply_gradient: 'NoneType' object has no attribute 'merge_call'

My program gives the following error message: ...
4
votes
0answers
8k views

How is WordPiece tokenization helpful to effectively deal with rare words problem in NLP?

I have seen that NLP models such as BERT utilize WordPiece for tokenization. In WordPiece, we split the tokens like playing to play and ##ing. It is mentioned that it covers a wider spectrum of Out-Of-...
4
votes
0answers
55 views

Fitting model to differenced time series

I have a time series on daily stock price of company(2013 data points).I took a first order difference and the following acf and pacf plots of the differenced series were obtained. However, I am ...
4
votes
0answers
141 views

How to implement hierarchical labeling classification?

I am currently working on task of eCommerce product name classification, so I have categories and subcategories in product data. I noticed that using subcategories as labels delivers worse results (84%...
4
votes
0answers
111 views

LSTM Long Term Dependencies Keras

I am familiar with the LSTM unit (memory cell, forget gate, output gate etc) however I am struggling to see how this links to the LSTM implementation in Keras. In Keras the input data structure for X ...
4
votes
0answers
1k views

Using the Python Keras multi_gpu_model with LSTM / GRU to predict Timeseries data

I'm having an issue with python keras LSTM / GRU layers with multi_gpu_model for machine learning. When I use a single GPU, the predictions work correctly ...
4
votes
0answers
2k views

Isolation Forest Feature Importance

As of scikit-learn version 0.19.1, there is no implementation for calculating feature importance in an Isolation Forest. I'm also having trouble finding any online resources proposing ways to get at ...
4
votes
0answers
951 views

Preparing ground truth labels for YOLO3

I want to train YOLO3 for a custom dataset that has raw labels in JSON format. Each bounding box in JSON is specified as [x1, y1, x2, y2]. So far, I have ...
4
votes
0answers
287 views

Multiple activation functions with TensorFlow estimator DNNClassifier

I just want to know if is it possible to use tf.estimator.DNNClassifier with multiple different activation functions. I mean, could I use a DNNClassifier estimator which use different activation ...
4
votes
0answers
187 views

Confidence value in AdaBoost?

I read this introduction about AdaBoost (http://www.cs.man.ac.uk/~nikolaon/~nikolaon_files/Introduction_to_AdaBoost.pdf), and am curious why confidence for each model is defined as $$\alpha_j=\frac{...
4
votes
0answers
281 views

Maths of Xavier initialization

The paper I read is Glorot et al (2010). And the math part is in Section 4.2.1. Formula (5) and (10) make sense to me but I cannot derive formula (6) and (7) myself from (2) and (3). I found many ...
4
votes
0answers
69 views

make prediction with a time serie

I want to build a model that can detect which driver is driving now the car based on a dataset that contains 20 driver records for 3600s each driver ( the dataset contains all the car sensors values ...
4
votes
0answers
59 views

Learning a logical function with a 2 layer BDN network - manual weight setting rule question?

So I am trying to construct a 2-layer network of binary decision neurons as proposed by McCullough and Pitts (1943) to learn a logical function (a composition of AND's and OR's) such as: $((\neg x_1\...
4
votes
0answers
3k views

Kalman filter for time series prediction

I have the information about the behaviour of 400 users across period of 1 months (30 days). Across those 30 days I measure 4 different information (let's call it A,B,C and D), hence I have a total of ...
4
votes
0answers
552 views

Adversarial Learning for Semantic Segmentation

I am incorporating Adversarial Training for Semantic Segmentation from Adversarial Learning for Semi-Supervised Semantic Segmentation. The idea is like this: The discriminator takes as input a ...
4
votes
0answers
724 views

ALS in Spark: what loss function is it minimizing?

I’ve playing with the MovieLens ratings dataset under Spark’s ALS and a manual implementation of ALS and comparing results with the same hyperparameters. I’d like to know this exactly in order to make ...
4
votes
0answers
1k views

In XGBoost, how to change eval function and keeping same objective?

I want to keep objective as "reg:linear" and eval_metric as customised rmse as follows. ...
4
votes
0answers
730 views

How does XGBoost compute the probabilities in predict_proba()?

I'm using the sklearn wrapper for XGBoost. I didn't manage to find a clear explanation for the way the probabilities given as output by predict_proba() are computed. In random forest for example, I ...
4
votes
0answers
97 views

Quantifying 'growth friction' when projecting target goals

As part of my DS work I spend some fraction of my time helping the team make growth projections, either for setting growth targets or when forecasting actual data. There is obviously a range of ways ...
4
votes
0answers
1k views

Keras objective function shared between outputs

Is there any way to implement a loss function that is shared between outputs? I have a 2D image output and scalar classification that are both used by a single loss function. I have attempted writing ...
4
votes
0answers
95 views

Fixed-radius range search in non-Euclidean space

I'm trying to find an indexing data structure most suitable for my metric space: set of IP network related data (IP addresses, ports, TCP flags, ...), distance function is continuous, non-Euclidean ...
4
votes
0answers
11k views

Tuning Gradient Boosted Classifier's hyperparametrs and balancing it

I am not sure if it is a correct stack. Maybe I should have put my question into crossvalidated. Nevertheless, I perform following steps to tune the hyperparameters for a gradient boosting model: ...
4
votes
0answers
264 views

how to propagate error from convolutional layer to previous layer?

I've been trying to implement a simple convolutional neural network. But I've been stuck at this problem for over a week. To be specific, assume there are 3 layers in a convolutional pass, marked as ...
4
votes
0answers
86 views

Meta-analysis of public 16S data

I am trying to start a meta-analysis for which I want to extract some 16S-based information from public databases. Moreover, I want to relate this information with any metadata found in the associated ...
4
votes
0answers
399 views

Illustrating the dimensionality reduction done by a classification or regression model

Tl;DR: You can predict something, but how do you explain the prediction? Your usual classification/regression setup Lets say the data is a classic regression/classification problem: several ...
4
votes
0answers
259 views

3D map using leaflet

I'm trying to create 3D bars on this map. Can anyone please advise if this is possible, and how? http://leafletjs.com/examples/choropleth.html My data: UFO sightings in the USA (location wise). ...
3
votes
0answers
25 views

Why softmax in YouTube’s DNN recommender

I am confused about the softmax layer of YouTube’s DNN candidate generation. A user may interact with many videos. Softmax is assuming classes are exclusive. For example, logits = [[4.0, 4.0, 1.0]], ...
3
votes
0answers
70 views

Why the sigmoid activation function results in sub-optimal gradient descent?

I need some help understanding the second shortcoming of the sigmoid activation function as described in this video from Stanford. She says that because the output of sigmoid is always positive, that ...

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