Auxiliary data to the CHIIR paper Searching to Learn with Instructional Scaffolding

Overview

Searching to Learn with Instructional Scaffolding

This is the data and analysis code for the paper "Searching to Learn with Instructional Scaffolding", accepted at CHIIR 2021.

The paper PDF can be found here.

More details can be found at the analysis notebook.


Under data, you will find the following files:


Any questions, please open an issue or send an email to:

A.BarbosaCamara [at] tudelft.nl


If you want to cite this paper, please use:

@INPROCEEDINGS{Camara2021CHIIR,
  title     = "Searching to Learn with Instructional Scaffolding",
  booktitle = "Proceedings of the 2021 Conference on Human Information
               Interaction and Retrieval",
  author    = "C{\^a}mara, Arthur and Roy, Nirmal and Maxwell, David and Hauff,
               Claudia",
  publisher = "Association for Computing Machinery",
  pages     = "209--218",
  series    = "CHIIR '21",
  month     =  mar,
  year      =  2021,
  address   = "New York, NY, USA",
  location  = "Canberra ACT, Australia"
}
Owner
Arthur Câmara
PhD Candidate in Information Retrieval @tudelft
Arthur Câmara
Continuum Learning with GEM: Gradient Episodic Memory

Gradient Episodic Memory for Continual Learning Source code for the paper: @inproceedings{GradientEpisodicMemory, title={Gradient Episodic Memory

Facebook Research 360 Dec 27, 2022
Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search This is an implementation for our paper Contextual Non-Loca

Tencent YouTu Research 50 Dec 03, 2022
Pytorch implementation of our paper under review -- 1xN Pattern for Pruning Convolutional Neural Networks

1xN Pattern for Pruning Convolutional Neural Networks (paper) . This is Pytorch re-implementation of "1xN Pattern for Pruning Convolutional Neural Net

Mingbao Lin (林明宝) 29 Nov 29, 2022
Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

DiscoGAN Official PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. Prerequisites Python 2.7

SK T-Brain 754 Dec 29, 2022
The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

Sun Yi 201 Nov 21, 2022
This is a clean and robust Pytorch implementation of DQN and Double DQN.

DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained

XinJingHao 15 Dec 27, 2022
A collection of easy-to-use, ready-to-use, interesting deep neural network models

Interesting and reproducible research works should be conserved. This repository wraps a collection of deep neural network models into a simple and un

Aria Ghora Prabono 16 Jun 16, 2022
An open-source Kazakh named entity recognition dataset (KazNERD), annotation guidelines, and baseline NER models.

Kazakh Named Entity Recognition This repository contains an open-source Kazakh named entity recognition dataset (KazNERD), named entity annotation gui

ISSAI 9 Dec 23, 2022
Pytorch Implementation of LNSNet for Superpixel Segmentation

LNSNet Overview Official implementation of Learning the Superpixel in a Non-iterative and Lifelong Manner (CVPR'21) Learning Strategy The proposed LNS

42 Oct 11, 2022
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation.

Understanding Minimum Bayes Risk Decoding This repo provides code and documentation for the following paper: Müller and Sennrich (2021): Understanding

ZurichNLP 13 May 01, 2022
Code for Greedy Gradient Ensemble for Visual Question Answering (ICCV 2021, Oral)

Greedy Gradient Ensemble for De-biased VQA Code release for "Greedy Gradient Ensemble for Robust Visual Question Answering" (ICCV 2021, Oral). GGE can

21 Jun 29, 2022
NP DRAW paper released code

NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation This repo contains the official implementation for the NP-DRAW paper.

ZENG Xiaohui 22 Mar 13, 2022
Cooperative Driving Dataset: a dataset for multi-agent driving scenarios

Cooperative Driving Dataset (CODD) The Cooperative Driving dataset is a synthetic dataset generated using CARLA that contains lidar data from multiple

Eduardo Henrique Arnold 124 Dec 28, 2022
Optimizes image files by converting them to webp while also updating all references.

About Optimizes images by (re-)saving them as webp. For every file it replaced it automatically updates all references. Works on single files as well

Watermelon Wolverine 18 Dec 23, 2022
In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy

PixMix Introduction In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard te

Andy Zou 79 Dec 30, 2022
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

Rethinking the Value of Labels for Improving Class-Imbalanced Learning This repository contains the implementation code for paper: Rethinking the Valu

Yuzhe Yang 656 Dec 28, 2022
[AAAI2022] Source code for our paper《Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation Learning》

SSVC The source code for paper [Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation Learning] samples of the

7 Oct 26, 2022
Code for CVPR2021 "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Visualizing Adapted Knowledge in Domain Transfer @inproceedings{hou2021visualizing, title={Visualizing Adapted Knowledge in Domain Transfer}, auth

Yunzhong Hou 80 Dec 25, 2022
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

EMI-Group 175 Dec 30, 2022
Uni-Fold: Training your own deep protein-folding models.

Uni-Fold: Training your own deep protein-folding models. This package provides and implementation of a trainable, Transformer-based deep protein foldi

DeepModeling 88 Jan 03, 2023