Deep Learning, NLP, NMT, AI, ML
Powerful Telegram bot to countdown to your important events in any group chat. Live countdown timer. (days : hours : minutes : seconds) Config Vars API_ID : Telegram API_ID, get it from my.telegram.org/apps API_HASH : Telegram API_ID, get it from my.telegram.org/apps BOT_TOKEN : A Valid Telegram Bot Token, get it from @Botfather Commands Works in any group (
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ArXiv (pdf) Official pytorch implementation of the paper: “One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective” NeurIPS 2021 Released on September 29, 2021 This paper proposes a novel deep hashing model with only a single learning objective which is a simplification from most state of the art papers generally use lots of losses and regularizer. Specifically, it maximizes the cosine similarity between the continuous codes and their corresponding binary orthogonal codes to ensure both […]
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deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch. Design Architecture As shown below, each pretraining/fine-tuning model is decomposed into two modules: Encoder and Head. Encoder Encoder has Embedding and Backbone. Embedding makes continuous/categorical features tokenized or simply normalized. Backbone processes the tokenized features. Pretraining/Fine-tuning Head Pretraining/Fine-tuning Head uses Encoder module for training. Implemented Methods Available Modules Encoder – Embedding FeatureEmbedding TabTransformerEmbedding Encoder – Backbone MLPBackbone FTTransformerBackbone SAINTBackbone Model – Head Model – Pretraining […]
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Abstract We propose using self-supervised discrete representations for the task of speech resynthesis. To generate disentangled representation, we separately extract low-bitrate representations for speech content, prosodic information, and speaker identity. This allows to synthesize speech in a controllable manner. We analyze various state-of-the-art, self-supervised representation learning methods and shed light on the advantages of each method while considering reconstruction quality and disentanglement properties. Specifically, we evaluate the F0 reconstruction, speaker identification performance (for both resynthesis and voice conversion), recordings’ intelligibility, […]
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November 7, 2021 By: Shuo Sun, Ahmed El-Kishky, Vishrav Chaudhary, James Cross, Francisco Guzmán, Lucia Specia Abstract Sentence-level Quality Estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels. Recent QE models have achieved previously-unseen levels of correlation with human judgments, but they rely on large multilingual contextualized language models that are computationally expensive and thus infeasible for many real-world applications. In this […]
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Abstract Inflectional morphology has since long been a useful testing ground for broader questions about generalization in language and the viability of neural network models as cognitive models of language. Here, in line with that tradition, we explore how recurrent neural networks acquire the complex German plural system and reflect upon how their strategy compares to human generalization and rule-based models of this system. We perform analyses including behavior experiments, diagnostic classification, representation analysis and causal interventions, suggesting that the […]
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Abstract Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e.g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines). This task has been previously instantiated as a form of textual entailment, which results in poor accuracy due to the complexity of the policies. In this paper we propose to address policy compliance detection via decomposing it into question answering, where questions check whether […]
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I have been confused about python modules and packages, this text tries to clear the topic up a bit. Sources: https://chrisyeh96.github.io/2017/08/08/definitive-guide-python-imports.html https://abarker.github.io/understanding_python_imports/ https://stackoverflow.com/questions/44834/can-someone-explain-all-in-python https://docs.python.org/3/reference/import.html https://stackoverflow.com/questions/43059267/how-to-do-from-module-import-using-importlib Modules Each file with extension .py can be used as a module. The module is in source file module_source.py, while it is imported as import module_source. In this example, both the module source file and the importing file are in the same directory. Lets import the module. (the module source includes a print statement print(“module_foo […]
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Pytorch-Lightning implementation of the Box-Aware Tracker. Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds. ICCV 2021 Chaoda Zheng, Xu Yan, Jiaotao Gao, Weibing Zhao, Wei Zhang, Zhen Li*, Shuguang Cui Citation @InProceedings{zheng2021box, title={Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds}, author={Chaoda Zheng, Xu Yan, Jiaotao Gao, Weibing Zhao, Wei Zhang, Zhen Li, Shuguang Cui}, journal={ICCV}, year={2021} } To finish reading, please visit source site
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Needs to be improved Prerequisites Need to have graphiz installed on the machine. Refer to Hints to generate JSON file Use JSON.stringify(). Example JSON.stringify(db.people.find({name:{$gt: “A”}}).sort({name:1}).explain(“executionStats”)) To run
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