Measuring Coding Challenge Competence With APPS

Related tags

Deep Learningapps
Overview

Measuring Coding Challenge Competence With APPS

This is the repository for Measuring Coding Challenge Competence With APPS by Dan Hendrycks*, Steven Basart*, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt.

Download the APPS dataset here.

This repository contains evaluation code.

For other benchmarks of enormous Transformers, see a dataset which tests ability in competition MATH, a dataset which tests knowledge of ETHICS, and a dataset spanning 50+ academic subjects.

Citation

If you find this useful in your research, please consider citing

@article{hendrycksapps2021,
  title={Measuring Coding Challenge Competence With APPS},
  author={Dan Hendrycks and Steven Basart and Saurav Kadavath and Mantas Mazeika and Akul Arora and Ethan Guo and Collin Burns and Samir Puranik and Horace He and Dawn Song and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2105.09938},
  year={2021}
}
Owner
Dan Hendrycks
PhD student at UC Berkeley.
Dan Hendrycks
Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)

Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021) Single-cause Perturbation (SCP) is a framework to estimate the m

Zhaozhi Qian 9 Sep 28, 2022
RID-Noise: Towards Robust Inverse Design under Noisy Environments

This is code of RID-Noise. Reproduce RID-Noise Results Toy tasks Please refer to the notebook ridnoise.ipynb to view experiments on three toy tasks. B

Thyrix 2 Nov 23, 2022
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
Two-stage CenterNet

Probabilistic two-stage detection Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network. Probabilistic two-st

Xingyi Zhou 1.1k Jan 03, 2023
Group-Free 3D Object Detection via Transformers

Group-Free 3D Object Detection via Transformers By Ze Liu, Zheng Zhang, Yue Cao, Han Hu, Xin Tong. This repo is the official implementation of "Group-

Ze Liu 213 Dec 07, 2022
Python package to generate image embeddings with CLIP without PyTorch/TensorFlow

imgbeddings A Python package to generate embedding vectors from images, using OpenAI's robust CLIP model via Hugging Face transformers. These image em

Max Woolf 81 Jan 04, 2023
Coarse implement of the paper "A Simultaneous Denoising and Dereverberation Framework with Target Decoupling", On DNS-2020 dataset, the DNSMOS of first stage is 3.42 and second stage is 3.47.

SDDNet Coarse implement of the paper "A Simultaneous Denoising and Dereverberation Framework with Target Decoupling", On DNS-2020 dataset, the DNSMOS

Cyril Lv 43 Nov 21, 2022
BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation

BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation Installing The Dependencies $ conda create --name beametrics python

7 Jul 04, 2022
Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022)

Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022) Please cite "Independent SE(3)-Equivar

Octavian Ganea 154 Jan 02, 2023
Experiments on continual learning from a stream of pretrained models.

Ex-model CL Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them

Antonio Carta 6 Dec 04, 2022
Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Tested on many Common CNN Networks and Vision Transformers. ⭐ Includes smoo

Jacob Gildenblat 6.6k Jan 06, 2023
Implementation of SSMF: Shifting Seasonal Matrix Factorization

SSMF Implementation of SSMF: Shifting Seasonal Matrix Factorization, Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi. NeurIPS, 2021

Koki Kawabata 9 Jun 10, 2022
Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

Data Augmentation for Scene Text Recognition (ICCV 2021 Workshop) (Pronounced as "strog") Paper Arxiv Why it matters? Scene Text Recognition (STR) req

Rowel Atienza 152 Dec 28, 2022
ERISHA is a mulitilingual multispeaker expressive speech synthesis framework. It can transfer the expressivity to the speaker's voice for which no expressive speech corpus is available.

ERISHA: Multilingual Multispeaker Expressive Text-to-Speech Library ERISHA is a multilingual multispeaker expressive speech synthesis framework. It ca

Ajinkya Kulkarni 43 Nov 27, 2022
A python comtrade load library accelerated by go

Comtrade-GRPC Code for python used is mainly from dparrini/python-comtrade. Just patch the code in BinaryDatReader.parse for parsing a little more eff

Bo 1 Dec 27, 2021
Semi-Supervised Signed Clustering Graph Neural Network (and Implementation of Some Spectral Methods)

SSSNET SSSNET: Semi-Supervised Signed Network Clustering For details, please read our paper. Environment Setup Overview The project has been tested on

Yixuan He 9 Nov 24, 2022
Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks"

HKD Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks" cifia-100 result The implementation of compared methods are ba

Wang Yucheng 30 Dec 18, 2022
Experiments for Neural Flows paper

Neural Flows: Efficient Alternative to Neural ODEs [arxiv] TL;DR: We directly model the neural ODE solutions with neural flows, which is much faster a

54 Dec 07, 2022
PyTorch implementation of Densely Connected Time Delay Neural Network

Densely Connected Time Delay Neural Network PyTorch implementation of Densely Connected Time Delay Neural Network (D-TDNN) in our paper "Densely Conne

Ya-Qi Yu 64 Oct 11, 2022
A command line simple note taking app

Why yet another note taking program? note was designed with a very specific target in mind: me, and my 2354 scraps of paper. It runs from the command

64 Nov 20, 2022