373 skills found · Page 9 of 13
marijoAI / MarijoAINo-Code Neural Network for binary classification
Sahrawat1 / Semantic Textual SimilarityAbstract Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications of this task include machine translation, summarization, text generation, question answering, short answer grading, semantic search, dialogue and conversational systems. We developed Support Vector Regression model with various features including the similarity scores calculated using alignment-based methods and semantic composition based methods. We have also trained sentence semantic representations with BiLSTM and Convolutional Neural Networks (CNN). The correlations between our system output the human ratings were above 0.8 in the test dataset. Introduction The goal of this task is to measure semantic textual similarity between a given pair of sentences (what they mean rather than whether they look similar syntactically). While making such an assessment is trivial for humans, constructing algorithms and computational models that mimic human level performance represents a difficult and deep natural language understanding (NLU) problem. Example 1: English: Birdie is washing itself in the water basin. English Paraphrase: The bird is bathing in the sink. Similarity Score: 5 ( The two sentences are completely equivalent, as they mean the same thing.) Example 2: English: The young lady enjoys listening to the guitar. English Paraphrase: The woman is playing the violin. Similarity Score: 1 ( The two sentences are not equivalent, but are on the same topic. ) Semantic Textual Similarity (STS) measures the degree of equivalence in the underlying semantics of paired snippets of text. STS differs from both textual entailment and paraphrase detection in that it captures gradations of meaning overlap rather than making binary classifications of particular relationships. While semantic relatedness expresses a graded semantic relationship as well, it is non-specific about the nature of the relationship with contradictory material still being a candidate for a high score (e.g., “night” and “day” are highly related but not particularly similar). The task involves producing real-valued similarity scores for sentence pairs. Performance is measured by the Pearson correlation of machine scores with human judgments.
alexanderganderson / Bnn VisTrain and Visualize Binary Neural Networks (Code for: The High-Dimensional Geometry of Binary Neural Networks)
celer-network / Celer ClientPrebuilt Celer client binary to interact with Celer Network
VDIGPKU / NAS BNNThe official implementation of "NAS-BNN: Neural Architecture Search for Binary Neural Networks"
omfaku / Cnn Malware ClassificationClassifying malware families by converting their binaries to images and then applying Convolutional Neural Network solutions.
Rogiel / PacketBufferPacketBuffer is a C++14 header-only library designed specifically to be really fast at processing binary network packets.
sanidhyanarayansingh / LifiLi-Fi technology is an efficient data communication mechanism involving visible light as a medium of transmission. The main ideology behind this technological innovation is that visible light illuminated by a light emitting diode (LED) is methodically amplitude modulated at the transmission end by rapid switching of LED lights at a speed not perceptible to human eye, whereas at the receiving end, photodiodes detect the modulated light and demodulates it to binary form by synchronized receiver circuits. It consists of a light source, line-of-sight (LOS) propagation medium, and a light detector. Information (streaming content), in the form of digital or analogue signals, is input to electronic circuitry that modulates the light source. The source output passes through an optical system (to control the emitted radiation, e.g., to ensure that the transmitter is eye safe) into the free space. The received signal comes through an optical system (e.g., an optical filter that rejects optical noise, a lens system or concentrator that focuses light on the detector), passes through the photo diode (PD), and the resulting photo-current is amplified before the signal processing electronics transforms it back to the received data stream. In this way, data communication is successfully achieved. Unlike Wi-Fi, the technology uses visible light spectrum instead of the increasingly congested radio frequency (RF) spectrum. Similarly to Wi-Fi, this technology allows connection of different web-enabled devices such as computers, smart TVs, smart phones, etc. to internet; provides the inter-connection of Wi-Fi enabled things such as refrigerators, watches, cameras, etc. in Internet of Things (IoT); and makes off-loading from cellular networks possible, addressing this way capacity needs for mobile broadband connections. In addition, Li-Fi has a huge amount of visible light spectrum that is unregulated and does not require licenses. It has to be ensured, however, that Li-Fi systems do not present any health hazards and that they are properly installed so as not to create any electromagnetic interference. The files here are the codes for relay transmitter , receiver , for sending and receiving audio.
erilyth / HybridBinaryNetworks WACV18Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory - WACV18
thouis / Triplet HashingPython version of "Fast Training of Triplet-based Deep Binary Embedding Networks" by Zhuang et al.
penpaperkeycode / Binarized Neural Network PytorchCourbariaux, Matthieu, Yoshua Bengio, and Jean-Pierre David. "Binaryconnect: Training deep neural networks with binary weights during propagations." Advances in neural information processing systems. 2015.
TadejMurovic / BNN DeploymentPart of paper: Massively Parallel Combinational Binary Neural Networks for Edge Processing
Imiloin / MiniCNNSJTU MST3314 course project (CNN). A binary classification convolutional neural network in Verilog.
NaelF / BinaryCoPBinary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices
Henryjiepanli / PU RSThe official implement of 《Progressive Uncertainty Guided Network for Binary Segmentation of Remote Sensing Imagery》
Jindi0 / SQNNScalable Quantum Neural Network builds and trains a large-scale QNN in a modular fashion. SQNN is evaluated with a binary classification task on the MNIST dataset.
K2 / RelocTransform dumped executable memory back into an identical match from disk. Use network or local database to de-locate relocated binaries and ensure a cryptographically secure hash match for code running on your legacy systems. A client tool that downloads relocation data for various PE files. This ensures when extracting data from memory dumps that you can match memory to disk files precisely.
marcgarnica13 / Ml Interpretability European FootballUnderstanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.
rval735 / BNN PhDBinary Neural Networks research project
human-analysis / LBCNNLocal Binary Convolutional Neural Networks
