📱📰 Android client for the Nextcloud news/feed reader app
Обновлено 2024-11-08 08:46:54 +03:00
📰 RSS/Atom feed reader
Обновлено 2024-11-07 15:59:12 +03:00
A repository to reproduce the experiments of the MA-POCA paper
Обновлено 2024-10-16 20:20:35 +03:00
Implementation of Differentially Private n-gram Extraction (DPNE) paper
Обновлено 2024-09-28 02:15:04 +03:00
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
machine-learning
deep-learning
python
platform
research
finance
algorithmic-trading
auto-quant
fintech
investment
paper
quant
quant-dataset
quant-models
quantitative-finance
quantitative-trading
research-paper
stock-data
Обновлено 2024-09-12 18:44:27 +03:00
Official repository for our NeurIPS 2021 paper "Unadversarial Examples: Designing Objects for Robust Vision"
Обновлено 2024-07-25 14:04:47 +03:00
Code to reproduce experiments in the paper "Constrained Language Models Yield Few-Shot Semantic Parsers" (EMNLP 2021).
Обновлено 2024-05-31 20:48:22 +03:00
Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization
Обновлено 2024-05-23 23:09:38 +03:00
Code to reproduce experiments in the paper "Task-Oriented Dialogue as Dataflow Synthesis" (TACL 2020).
Обновлено 2024-05-01 00:56:47 +03:00
This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA).
nlp
ml
ner
named-entity-recognition
entity-extraction
entity-linking
entity-resolution
grn
language-understanding
linkingpark
nlp-resources
unitrans
xl-ner
bertel
can-ner
cross-lingual-ner
entity-disambiguation
Обновлено 2024-03-16 09:53:11 +03:00
Multiple paper open-source codes of the Microsoft Research Asia DKI group
semantic-parsing
compositional-generalization
conversational-semantic-parsing
microsoft-research-asia
text-to-sql
utterance-rewriting
Обновлено 2023-11-10 05:14:43 +03:00
Chibitronics Love To Code board editor with Microsoft MakeCode
Обновлено 2023-09-06 09:01:20 +03:00
Visuomotor policies from event-based cameras through representation learning and reinforcement learning. Accompanies our paper: https://arxiv.org/abs/2103.00806
Обновлено 2023-08-15 00:50:21 +03:00
Code accompanying the paper "Better Exploration with Optimistic Actor Critic" (NeurIPS 2019)
Обновлено 2023-08-11 22:50:02 +03:00
Source code for EMNLP2019 paper "Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL".
Обновлено 2023-07-22 19:54:50 +03:00
The code of EMNLP 2019 paper "A Split-and-Recombine Approach for Follow-up Query Analysis"
Обновлено 2023-07-20 15:42:43 +03:00
Research code of ICCV 2021 paper "Mesh Graphormer"
Обновлено 2023-07-07 01:06:33 +03:00
Research code for CVPR 2021 paper "End-to-End Human Pose and Mesh Reconstruction with Transformers"
Обновлено 2023-07-07 01:02:19 +03:00
Research code accompanying paper on predicting the probability of a positive SARS-CoV-2 test, using OMOP common data model.
Обновлено 2023-07-07 01:01:33 +03:00
Official implementation for ECCV 2020 paper CONFIG: Controllable Neural Face Image Generation
Обновлено 2023-07-07 01:01:06 +03:00
Code associated with our paper "Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations": https://arxiv.org/abs/1909.06993
Обновлено 2023-06-27 15:58:01 +03:00
Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Обновлено 2023-06-12 21:56:30 +03:00
Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search
deep-learning
automl
neural-architecture-search
image-classification
cifar-100
cifar10
deep-learning-algorithms
imagenet
Обновлено 2023-06-12 21:22:32 +03:00
Dataset and code for three Web crawling-related papers from SIGIR-2019, NeurIPS-2019. and ICML-2020.
Обновлено 2023-06-12 21:21:59 +03:00
Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.
Обновлено 2023-06-12 21:21:58 +03:00
This is an implementation of AAAI'20 paper "Semantics-Aligned Representation Learning for Person Re-identification". We leverages dense semantics to address both the spatial misalignment and semantics misalignment challenges in person re-identification.
Обновлено 2023-06-12 21:21:23 +03:00
ICLR 2022 Paper, SOTA Table Pre-training Model, TAPEX: Table Pre-training via Learning a Neural SQL Executor
Обновлено 2023-02-06 11:06:18 +03:00
Rock, Paper, Scissors, Lizard, Spock - Sample Application
azure
javascript
python
java
nodejs
dotnet-core
tensorflow
php
blazor
aspnetcore
azure-functions
azure-kubernetes-service
Обновлено 2023-01-27 21:01:49 +03:00
Codes for the AAAI 2020 paper "F3Net: Fusion, Feedback and Focus for Salient Object Detection"
Обновлено 2023-01-19 23:20:38 +03:00