HayeonLee's repositories

MetaD2A

Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

HELP

Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)

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D-VAE

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs, NeurIPS 2019

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gae-pytorch

Graph Auto-Encoder in PyTorch

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are-16-heads-really-better-than-1

Code for the paper "Are Sixteen Heads Really Better than One?"

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attention_sinks

Extend existing LLMs way beyond the original training length with constant memory usage, without retraining

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dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

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hardware-aware-transformers

[ACL 2020] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

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hayeonlee.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

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hgraph2graph

Hierarchical Generation of Molecular Graphs using Structural Motifs

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LSTM-Human-Activity-Recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

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minimalRL

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

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models

Models and examples built with TensorFlow

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nas-without-training

Neural Architecture Search Without Training

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nsganetv2

[ECCV2020] NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search

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OpenLongTailRecognition-OLTR

Pytorch implementation for "Large-Scale Long-Tailed Recognition in an Open World" (CVPR 2019 ORAL)

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pygcn

Graph Convolutional Networks in PyTorch

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pytorch_geometric

Geometric Deep Learning Extension Library for PyTorch

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streaming-llm

[ICLR 2024] Efficient Streaming Language Models with Attention Sinks

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tensorflow-yolo-v3

Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)

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TensorFlow2.0-Examples

🙄 Difficult algorithm, simple code.

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