Implementation of various Vision Transformers I found interesting

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Kim Seonghyeon
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Kim Seonghyeon
Public repository containing materials used for Feed Forward (FF) Neural Networks article.

Art041_NN_Feed_Forward Public repository containing materials used for Feed Forward (FF) Neural Networks article. -- Illustration of a very simple Fee

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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

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(CVPR 2022) A minimalistic mapless end-to-end stack for joint perception, prediction, planning and control for self driving.

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Main Results on ImageNet with Pretrained Models

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On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (Findings of EMNLP 2021))

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Framework that uses artificial intelligence applied to mathematical models to make predictions

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FewBit — a library for memory efficient training of large neural networks

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Code for "Single-view robot pose and joint angle estimation via render & compare", CVPR 2021 (Oral).

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Project for tracking occupancy in Tel-Aviv parking lots.

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Pydantic models for pywttr and aiopywttr.

Pydantic models for pywttr and aiopywttr.

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A scanpy extension to analyse single-cell TCR and BCR data.

Scirpy: A Scanpy extension for analyzing single-cell immune-cell receptor sequencing data Scirpy is a scalable python-toolkit to analyse T cell recept

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Self-training with Weak Supervision (NAACL 2021)

This repo holds the code for our weak supervision framework, ASTRA, described in our NAACL 2021 paper: "Self-Training with Weak Supervision"

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Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021)

Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021) In this repository we provide PyTorch implementations for GeMCL; a

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RL Algorithms with examples in Python / Pytorch / Unity ML agents

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Repository containing the PhD Thesis "Formal Verification of Deep Reinforcement Learning Agents"

Getting Started This repository contains the code used for the following publications: Probabilistic Guarantees for Safe Deep Reinforcement Learning (

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EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

MADE (Multi-Adapter Dataset Experts) This repository contains the implementation of MADE (Multi-adapter dataset experts), which is described in the pa

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Multi Task RL Baselines

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