# All Questions

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### 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 ...
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 ...
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2k 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. ...
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1k views

### Find missing object(s) in image with a priori knowledge about the missing object(s) (w.r.t base image)

Problem Statement: I am working on developing a method, or borrow/modify/combine existing ones, where given an golden image (reference or base with all expected objects to be present), it is able to ...
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816 views

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

I am using the gensim library for topic modeling, more specifically LDA. I created my corpus, my dictionary, and my LDA model. With the help of the pyLDAvis library I visualized the results. When I ...
109 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 ...
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182 views

### How to predict advantage value in deep reinforcement learning

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 ...
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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 ...
• 81
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-...
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3k 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. ...
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" ...
• 171
2k views

### Fine tuning accuracy lower than Raw Transfer Learning Accuracy

I've used transfer learning on Inception V3 with ImageNet weights on Keras with Tensorflow backend on python 2.7 to create an image classifier. I first extracted and saved the bottleneck features from ...
• 71
882 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 ...
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 ...
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506 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 = ...
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100 views

### How Exactly Does In-Context Few-Shot Learning Actually Work in Theory (Under the Hood), Despite only Having a "Few" Support Examples to "Train On"?

Recent models like the GPT-3 Language Model (Brown et al., 2020) and the Flamingo Visual-Language Model (Alayrac et al., 2022) use in-context few-shot learning. The models are able to make highly ...
48 views

### How sklearn SVM find the initial hyperplane before Optimisation?

The optimization goal of the SVM is to maximize the distance between the positive and negative hyperplanes. But before optimizing, how sklearn first find the positive and negative support vectors and ...
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121 views

### Unable to transform (greatly performing) Autoencoder into Variational Autoencoder

Following the procedure described in this SO question, I am trying to transform my (greatly performing) convolutional Autoencoder into a Variational version of the same Autoencoder. As explained in ...
167 views

### What does big O mean in KNN optimal weights?

Wiki gives this definition of KNN In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists ...
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