htmpapers | Numenta published papers code and data | Machine Learning library

 by   numenta Jupyter Notebook Version: Current License: AGPL-3.0

kandi X-RAY | htmpapers Summary

kandi X-RAY | htmpapers Summary

htmpapers is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. htmpapers has no vulnerabilities, it has a Strong Copyleft License and it has low support. However htmpapers has 1 bugs. You can download it from GitHub.

This repository contains reproducible code for selected Numenta papers. It is currently under construction and will eventually include the source code for all the scripts used in Numenta's papers. In this paper we show that the biophysical properties of dendrites, synapses, and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. First, we propose a novel artificial neural network architecture that incorporates active dendrites and sparse representations into the standard deep learning framework. Next, we study the performance of this architecture in two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model’s prediction task changes throughout training. This paper demonstrates the implementation of a sensorimotor network that uses grid-cell computations to process a sequence of visual inputs, specifically a sequence of image patches from the MNIST dataset. The network is able to classify novel digits (as well as perform other tasks) in a way that is robust to the specific sequence over which the visual space is sampled, a challenging setting for typical machine learning approaches. The work builds on our previous paper, “Locations in the Neocortex.". In this paper we investigate how dendritic properties can add value to ANNs in the context of continual learning, an area where ANNs suffer from catastrophic forgetting. In this paper we discuss inherent benefits of high dimensional sparse representations. We focus on robustness and sensitivity to interference. These are central issues with today’s neural network systems where even small and large perturbations can cause dramatic changes to a network’s output. This paper provides an implementation for a location layer with grid-like modules that encode object-specific locations. This layer is incorpated into a network with an input layer and simulations show how the model can learn many complex objects and later infer which learned object is being sensed. This paper proposes a network model composed of columns and layers that performs robust object learning and recognition. The model introduces a new feature to cortical columns, location information, which is represented relative to the object being sensed. Pairing sensory features with locations is a requirement for modeling objects and therefore must occur somewhere in the neocortex. We propose it occurs in every column in every region. This paper describes an important component of HTM, the HTM spatial pooler, which is a neurally inspired algorithm that learns sparse distributed representations online. Written from a neuroscience perspective, the paper demonstrates key computational properties of HTM spatial pooler. 14th IEEE ICMLA 2015 - This paper discusses how we should think about anomaly detection for streaming applications. It introduces a new open-source benchmark for detecting anomalies in real-time, time-series data. This paper discusses the requirements necessary for real-time anomaly detection in streaming data, and demonstrates how Numenta's online sequence memory algorithm, HTM, meets those requirements. It presents detailed results using the Numenta Anomaly Benchmark (NAB), the first open-source benchmark designed for testing real-time anomaly detection algorithms. Foundational paper describing core HTM theory for sequence memory and its relationship to the neocortex. Written with a neuroscience perspective, the paper explains why neurons need so many synapses and how networks of neurons can form a powerful sequence learning mechanism.
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            kandi-support Support

              htmpapers has a low active ecosystem.
              It has 254 star(s) with 68 fork(s). There are 49 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 5 have been closed. On average issues are closed in 7 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of htmpapers is current.

            kandi-Quality Quality

              OutlinedDot
              htmpapers has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 1564 code smells.

            kandi-Security Security

              htmpapers has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              htmpapers code analysis shows 0 unresolved vulnerabilities.
              There are 9 security hotspots that need review.

            kandi-License License

              htmpapers is licensed under the AGPL-3.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

            kandi-Reuse Reuse

              htmpapers releases are not available. You will need to build from source code and install.
              htmpapers saves you 4181 person hours of effort in developing the same functionality from scratch.
              It has 8876 lines of code, 348 functions and 67 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed htmpapers and discovered the below as its top functions. This is intended to give you an instant insight into htmpapers implemented functionality, and help decide if they suit your requirements.
            • Core function for object learning and inference
            • Performs the inference on the object
            • Train the object
            • Records the object with random moves
            • Reset the experiment
            • Create a learning rate scheduler
            • Convert to torch ndarray
            • Creates training and validation data sampling
            • Creates a dictionary of episode batches
            • Creates a matplotlib figure for the cns2021 dataset
            • Compute the average average of the data
            • Create command parser
            • Setup data sets
            • Perform a training iteration
            • Displays the visualization of the hidden unit
            • Displays the hyperparameter dendrites_andrite_andrite_ffrite_and_ffrite_and_ffrites
            • Compute false negatives for the given kw
            • Return a dict of episode batches for each worker
            • Train a decoder network
            • Compute the performance of each task
            • Predict a small patch for each cell
            • Reset training
            • Performs blocked training
            • Provide hyperparameter selection panel
            • Generate SDRs for each image
            • Iterate through the learning loop
            Get all kandi verified functions for this library.

            htmpapers Key Features

            No Key Features are available at this moment for htmpapers.

            htmpapers Examples and Code Snippets

            No Code Snippets are available at this moment for htmpapers.

            Community Discussions

            QUESTION

            Using RNN Trained Model without pytorch installed
            Asked 2022-Feb-28 at 20:17

            I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

            I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

            I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

            ...

            ANSWER

            Answered 2022-Feb-17 at 10:47

            You should try to export the model using torch.onnx. The page gives you an example that you can start with.

            An alternative is to use TorchScript, but that requires torch libraries.

            Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

            ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

            A running example

            Just modifying a little your example to go over the errors I found

            Notice that via tracing any if/elif/else, for, while will be unrolled

            Source https://stackoverflow.com/questions/71146140

            QUESTION

            Flux.jl : Customizing optimizer
            Asked 2022-Jan-25 at 07:58

            I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.

            optimizer_pseudocode

            I'm using MNIST dataset.

            ...

            ANSWER

            Answered 2022-Jan-14 at 23:47

            Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.

            Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:

            Source https://stackoverflow.com/questions/70641453

            QUESTION

            How can I check a confusion_matrix after fine-tuning with custom datasets?
            Asked 2021-Nov-24 at 13:26

            This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.

            Background

            I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.

            Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.

            After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?

            An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image

            ...

            ANSWER

            Answered 2021-Nov-24 at 13:26

            What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.

            Source https://stackoverflow.com/questions/68691450

            QUESTION

            CUDA OOM - But the numbers don't add upp?
            Asked 2021-Nov-23 at 06:13

            I am trying to train a model using PyTorch. When beginning model training I get the following error message:

            RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)

            I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi I see these processes running

            ...

            ANSWER

            Answered 2021-Nov-23 at 06:13

            This is more of a comment, but worth pointing out.

            The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):

            Let's run the following python commands interactively:

            Source https://stackoverflow.com/questions/70074789

            QUESTION

            How to compare baseline and GridSearchCV results fair?
            Asked 2021-Nov-04 at 21:17

            I am a bit confusing with comparing best GridSearchCV model and baseline.
            For example, we have classification problem.
            As a baseline, we'll fit a model with default settings (let it be logistic regression):

            ...

            ANSWER

            Answered 2021-Nov-04 at 21:17

            No, they aren't comparable.

            Your baseline model used X_train to fit the model. Then you're using the fitted model to score the X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.

            The grid searched model is at a disadvantage because:

            1. It's working with less data since you have split the X_train sample.
            2. Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of X_val per fold).

            So your score for the grid search is going to be worse than your baseline.

            Now you might ask, "so what's the point of best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.

            So how should one go about conducting a fair comparison?

            1. Split your training data for both models.

            Source https://stackoverflow.com/questions/69844028

            QUESTION

            Getting Error 524 while running jupyter lab in google cloud platform
            Asked 2021-Oct-15 at 02:14

            I am not able to access jupyter lab created on google cloud

            I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.

            I tried the diagnostic tool, which gave the following result:

            but didn't fix it.

            Thanks in advance.

            ...

            ANSWER

            Answered 2021-Aug-20 at 14:00

            QUESTION

            TypeError: brain.NeuralNetwork is not a constructor
            Asked 2021-Sep-29 at 22:47

            I am new to Machine Learning.

            Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:

            I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!

            Kindly point out what I am missing here!

            Find below my code:

            ...

            ANSWER

            Answered 2021-Sep-29 at 22:47

            Turns out its just documented incorrectly.

            In reality the export from brain.js is this:

            Source https://stackoverflow.com/questions/69348213

            QUESTION

            Ordinal Encoding or One-Hot-Encoding
            Asked 2021-Sep-04 at 06:43

            IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?

            I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:

            ...

            ANSWER

            Answered 2021-Sep-04 at 06:43

            You're right. Just one thing to consider for choosing OrdinalEncoder or OneHotEncoder is that does the order of data matter?

            Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:

            • quality = ["bad", "average", "good", "excellent"] or
            • shirt_size = ["large", "medium", "small"]

            but it is obviously not the case for the:

            • color = ["white","orange","black","green"]

            column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white category should be encoded as 0 and black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)

            Source https://stackoverflow.com/questions/69052776

            QUESTION

            How to increase dimension-vector size of BERT sentence-transformers embedding
            Asked 2021-Aug-15 at 13:35

            I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language

            by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.

            code:

            ...

            ANSWER

            Answered 2021-Aug-10 at 07:39

            Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.

            Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.

            If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.

            Source https://stackoverflow.com/questions/68686272

            QUESTION

            How to identify what features affect predictions result?
            Asked 2021-Aug-11 at 15:55

            I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.

            Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?

            Table example:

            ...

            ANSWER

            Answered 2021-Aug-11 at 15:55

            You could build a model like this.

            x = features you have. y = true_lable

            from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).

            Source https://stackoverflow.com/questions/68744565

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

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            You can download it from GitHub.

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