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18
votes
0answers
372 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....
14
votes
0answers
2k 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 ...
9
votes
2answers
997 views

Input for LSTM for financial time series directional prediction

I'm working on using an LSTM to predict the direction of the market for the next day. My question concerns the input for the LSTM. My data is a financial time series $x_1 \ldots x_t$ where each $x_i$...
8
votes
2answers
2k views

Loss Function for Probability Regression

I'm trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
8
votes
2answers
2k views

Forecasting Multiple (few hundreds) uni-variate time series with inflated zeros

Hello Practitioners, Being a newbie seeking help to gain experience in Data Science. Lets take a scenario where a big company wants to forecast its sales (a specific product) across different stores ...
7
votes
2answers
192 views

How to implement hierarchical labeling classification?

I am currently working on the 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 ...
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
1answer
981 views

Math Behind GOSS (Gradient-Based One Side Sampling)?

As per my understanding through books & Google Search, GOSS (Gradient-Based One Side Sampling) is a novel sampling method that downsamples the instances on the basis of gradients. As we know ...
6
votes
0answers
180 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
1answer
6k views

Keras - Implementation of custom loss function with multiple outputs

I am trying to replicate (a way smaller version) the AlphaGo Zero system. However, in the network model, I am having a problem. The loss function I am supposed to implement is the following: $$l = (z -...
6
votes
0answers
579 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 ...
6
votes
1answer
349 views

Pyspark: Filter dataframe based on separate specific conditions

How can I select only certain entries that match my condition and from those entries, filter again using regex? For instance, I have this dataframe (df) ...
6
votes
1answer
198 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 ...
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
1answer
220 views

Training stateful LSTM with different number of sequences

I'm using a stateful LSTM for stock market analysis, and I have varying amounts of data for each stock, ranging from 20 years to just a few weeks (i.e. for newly listed stocks). I use 3 years of data ...
6
votes
1answer
402 views

Chi-square as evaluation metrics for nonlinear machine learning regression models

I am using machine learning models to predict an ordinal variable (values: 1,2,3,4, and 5) using 7 different features. I posed this as a regression problem, so the final outputs of a model are ...
6
votes
1answer
107 views

Gridsearch XGBoost for ensemble. Do I include first-level prediction matrix of base learners in train set?

I'm not quite sure how I should go about tuning xgboost before I use it as a meta-learner in ensemble learning. Should I include the prediction matrix (ie. df containing columns of prediction results ...
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
1answer
349 views

Visualizing 28 different variables with 28 different colors?

ColorBrewer seems to be very useful in selecting a color pallet to represent factors that have up to 12 possible values. I have 28. Is it a horrible idea to represent 28 variables with color? If so,...
6
votes
1answer
438 views

A clear visualization of a two-way ANOVA

To provide a full yet simple picture of a 3-level, one-way ANOVA, I use the following visualization where variation within each group (the filled circles) and variation between the groups (black ...
6
votes
0answers
1k 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
1answer
136 views

How to model segmentation of a sequence to similar parts?

I guess LSTM is good for sequence modeling but how would you model "clustering" with it? Meaning, the input is a sequence and the output is labels with similar properties (I have labeled data). For ...
5
votes
1answer
67 views

Imbalanced training set vs smaller balanced training set?

Say I am using a maximum likelihood approach and my output unit computes a softmax function. My training set is distributed as follows over 6 classes: ...
5
votes
3answers
88 views

How can data science teams inside businesses measure costs and efficiency of their technical work?

How can data science teams measure and improve costs of their technical work, when they often don't know the monetary value of the datasets and insights they are producing? Are they using industry ...
5
votes
0answers
484 views

XGBoost custom objective for regression in R

I implemented a custom objective and metric for a xgboost regression task. In order to see if I'm doing this correctly, I started with a quadratic loss. The ...
5
votes
0answers
749 views

Triangle Pattern Recognition on Financial Market with Python

I'm working on a personal project to find Triangles on any stock in Python. I detect the max and min points (shift(-5,+5) because if I consider only shift(-1+1) I have a lot of lines) and write lines ...
5
votes
2answers
66 views

Reasoning behind using Deep Learning on non-local data

I understand the using of deep learning for data that have "local" structure, for example, images/videos/texts, as the convolutional layers reduce the amount of dimensions. However, I saw ...
5
votes
2answers
73 views

Data transformations in hierarchical classification

I am building a hierarchical text classifier using the Local Classifier Per Parent Node (LCPN) approach with the 'siblings' policy as described in the A survey of hierarchical classification across ...
5
votes
2answers
71 views

Detecting abundance of a certain periodic pattern in a time series?

I am really stumped at the moment about how to solve a particular problem. I have many time series like this: This represents the number of hours a person spends on a website each day throughout the ...
5
votes
1answer
52 views

Is there a way to rank the Extracted Named Entities based on their importance/occurence in a document?

Looking for a way to rank the tens and hundreds of named entities present in any document in order of their importance/relevance in the context. Any thoughts ? Thanks in advance!
5
votes
2answers
101 views

How to interpret two continous variables output using GAM?

I really need help with GAM. I have to find out whether association is linear or non-linear by using GAM. The predictor variable is temperature at lag0 and the output is cardiovascular admissions (...
5
votes
0answers
9k 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-...
5
votes
0answers
2k views

Tensorflow v1 Dataset API AttributeError with ndim

I'd like to make pipeline for optimizing Gpu and Cpu. Dataset It's about 10000 datapoint and 4 description variables for the regression problem. ...
5
votes
1answer
389 views

Dataset image size and inference speed

Does training/fine tuning a pre-trained model on a the same dataset but with sizes scaled down (e.g., by 70%) improve inference speed? More generally, does training a CNN on smaller images improve ...
5
votes
1answer
774 views

Comparison between addition and multiplication function in deep neural network?

I designed a specific Convolution Neural Network to study in the area of image processing. The network has a part that there are two tensors which have to be transformed into a tensor in order to be ...
5
votes
1answer
125 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 ...
5
votes
0answers
65 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
1k views

Multivariate, multistep forecasting with LSTM

I want to use an RNN with LSTM to forecast multiple steps into the future, based on multiple inputs. I have some ideas for different ways to approach this, but I'm afraid I'm missing the "right way" ...
5
votes
1answer
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
133 views

Deep advantage learning: how to predict the value

I'm currently working on a collection of reinforcement algorithms: https://github.com/lhk/rl_gym For deep q-learning, you need to calculate the q-values that should be predicted by your network. There ...
5
votes
1answer
105 views

Predicting change of shapes/coordinates

I'm trying to find a way to predict/calculate how a shape (e.g. outline of a glacier) will change in the future—based on its history (previous shape) and additional factors (e.g. Δtemperature). In my ...
5
votes
3answers
690 views

Hyperparameter tuning in multiclass classification problem: which scoring metric?

I'm working with an imbalanced multi-class dataset. I try to tune the parameters of a DecisionTreeClassifier, ...
5
votes
1answer
173 views

Methods for ensembling ranked lists?

I was wondering if there's a good way to use ensembling when I have two or more algoritims producing ranked lists. That is, suppose I have the following datasets consisting of ordered lists (higher ...
5
votes
1answer
2k views

Gradient flow through concatenation operation

I need help in understanding the gradient flow through a concatenation operation. I'm implementing a network (mostly a CNN) which has a concatenation operation (in pytorch). The network is defined ...
5
votes
0answers
774 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 ...
5
votes
1answer
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. ...
5
votes
1answer
662 views

Time Series pattern recognition and classification problem

I have some labeled sensor data. Now, I would like to know how to extract features from time series using DFT, DWT, and HAAR transforms. I know that the transformations above transform a signal to ...
5
votes
2answers
316 views

Changing default values of ANNIE resources in GATE from Java code

In GATE, default values for ANNIE are set during initialization, but sometimes based on requirements they have to be changed. My Requirement : I want to extract English sentences without considering ...
5
votes
0answers
485 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 = ...
5
votes
1answer
290 views

Visualizing large number of points as a 3D density map

The result of my computational simulation is a (time-dependent) system of large number (~100k) of moving points in a confined space. Each point has its own Cartesian coordinates as well as a weight (...

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