Data Analysis, NLP

Analyzing, storing and visualizing big data, scientifically

root The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data in a very efficient way. Having the data defined as a set of objects, specialized storage methods are used to get direct access to the separate attributes of the selected objects, without having to touch the bulk of the data. Included are histograming methods in an arbitrary number of dimensions, curve fitting, function evaluation, minimization, graphics and […]

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Graph, NLP

A Python library for Deep Graph Networks

PyDGN This is a Python library to easily experiment with Deep Graph Networks (DGNs). It provides automatic management of data splitting, loading and the most common experimental settings. It also handles both model selection and risk assessment procedures, by trying many different configurations in parallel (CPU). This repository is built upon the Pytorch Geometric Library, which provides support for data management. If you happen to use or modify this code, please remember to cite our tutorial paper: Bacciu Davide, Errica […]

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Network, NLP

Learning Versatile Neural Architectures by Propagating Network Codes

NCP Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang, Ping Luo Introduction This work includes:(1) NAS-Bench-MR, a NAS benchmark built on four challenging datasets under practical training settings for learning task-transferable architectures.(2) An efficient predictor-based algorithm Network Coding Propagation (NCP), which back-propagates the gradients of neural predictors to directly update architecture codes along desired gradient directions for various objectives. This framework is implemented and tested with Ubuntu/Mac OS, CUDA 9.0/10.0, Python 3, Pytorch 1.3-1.6, […]

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Machine Learning, NLP

Adaptive Class Suppression Loss for Long-Tail Object Detection

ACSL This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection. Requirements 1. Environment: The requirements are exactly the same as BalancedGroupSoftmax. We tested on the following settings: python 3.7 cuda 10.0 pytorch 1.2.0 torchvision 0.4.0 mmcv 0.2.14 conda create -n mmdet python=3.7 -y conda activate mmdet pip install cython pip install numpy pip install torch pip install torchvision pip install pycocotools pip install matplotlib pip install terminaltables # download the source […]

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NLP, Robot

A simple, multipurpose Discord bot

EpicBot EpicBot is a multipurpose Discord bot that was designed to make your Discord life easier and cooler. It is also an open source project which other developers can contribute to and work on it together. Features 📌 Over 130+ commands! 🔼 99%+ uptime. 🟢 Low latency, super fast response time. 💻 Web dashboard! (coming soon) 🎶 Extremely high quality Music playback. 🎊 Welcome and Leave messages, Autorole. ✨ Level up system. 🎉 Giveaway commands. 🔨 Powerful moderation. 🎀 Regularly […]

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Machine Learning, NLP

A minimal Automatic Machine Learning solution with PyTorch

carefree-learn carefree-learn is a minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch. Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. User Side import cflearn import numpy as np x = np.random.random([1000, 10]) y = np.random.random([1000, 1]) m = cflearn.make().fit(x, y) Developer Side import cflearn import numpy as np cflearn.register_model(“wnd_full”, pipes=[cflearn.PipeInfo(“fcnn”), cflearn.PipeInfo(“linear”)]) x = np.random.random([1000, 10]) y = np.random.random([1000, 1]) m = cflearn.make(“wnd_full”).fit(x, y) Please refer to Quick Start and Build Your Own […]

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Data Analysis, NLP

Fit models to your data in Python with Sherpa

Sherpa Sherpa is a modeling and fitting application for Python. It contains a powerful language for combining simple models into complex expressions that can be fit to the data using a variety of statistics and optimization methods. It is easily extensible to include user models, statistics, and optimization methods. It provides a high-level User Interface for interactive data-analysis work, such as within a Jupyter notebook, and it can also be used as a library component, providing fitting and modeling capabilities […]

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NLP, synchronous bidirectional NMT

Issue #125 – Synchronous Bidirectional Neural MT

08 Apr21 Issue #125 – Synchronous Bidirectional Neural MT Author: Akshai Ramesh, Machine Translation Scientist @ Iconic Introduction In recent years, Neural machine translation (NMT) based on the encoder-decoder architecture has significantly improved the quality of machine translation. Despite their remarkable performance, NMT models have a number of flaws (Koehn and Knowles, 2017), one of which is the issue of unbalanced outputs in translation. Current neural machine translation (NMT) approaches produce the target language sequence token-by-token from left to right […]

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Machine Learning, NLP

Implementation of the Swin Transformer in PyTorch

swin-transformer-pytorch Implementation of the Swin Transformer architecture. This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with shifted […]

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Machine Learning, NLP

Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer projects: [email protected] Highlights End-to-End, Post-processing Free STARK is an end-to-end tracking approach, which directly predicts one accurate bounding box as the tracking result.Besides, STARK does not use any hyperparameters-sensitive post-processing, leading to stable performances. Real-Time Speed STARK-ST50 and STARK-ST101 run at 40FPS and 30FPS respectively on a Tesla V100 GPU. Strong performance Tracker LaSOT (AUC) GOT-10K (AO) TrackingNet (AUC) STARK 67.1 […]

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