ML Reproducibility Challenge 2021 Fall Submissions

[Re] Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Anonymous
- 05 Feb 2022 (modified: 05 May 2023)
- ML Reproducibility Challenge 2021 Fall Blind Submission
- Readers: Everyone
[Re] Projection-based Algorithm for Updating the TruncatedSVD of Evolving Matrices
Shion Matsumoto, Rohan Sinha Varma, Andy Chen
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
Reproducibility of "Pixel-wise Anomaly Detection in Complex Driving Scenes" for ML Reproducibility Challenge 2021
Anonymous
- 05 Feb 2022 (modified: 05 May 2023)
- ML Reproducibility Challenge 2021 Fall Blind Submission
- Readers: Everyone
[Re] Distilling Knowledge via Knowledge Review
Anonymous
- 05 Feb 2022 (modified: 05 May 2023)
- ML Reproducibility Challenge 2021 Fall Blind Submission
- Readers: Everyone
Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations
Roopsa Sen, Sidharth Sinha, Parv Maheshwari, Animesh Jha
- Published: 11 Apr 2022, Last Modified: 21 Apr 2024
- RC2021
- Readers: Everyone
[Reproducibility Report] Path Planning using Neural A* Search
Anonymous
- 05 Feb 2022 (modified: 05 May 2023)
- ML Reproducibility Challenge 2021 Fall Blind Submission
- Readers: Everyone
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting
Abhishek Shukla, Sourya Roy, Yogesh Chawla, Avi Amalanshu, Shubhendu Pandey, Rudransh Agrawal, Aditya Uppal, Viswesh N, Pradipto Mondal, Anubhab Dasgupta, Debhashish Chakravarty
- Published: 11 Apr 2022, Last Modified: 21 Apr 2024
- RC2021
- Readers: Everyone
[RE] Bad Seeds: Evaluating Lexical Methods for Bias Measurement
Anonymous
- 05 Feb 2022 (modified: 05 May 2023)
- ML Reproducibility Challenge 2021 Fall Blind Submission
- Readers: Everyone
Reproducibility study - Counterfactual Generative Networks
Anonymous
- 05 Feb 2022 (modified: 05 May 2023)
- ML Reproducibility Challenge 2021 Fall Blind Submission
- Readers: Everyone
[RE] Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation
Aryan Mehta, Karan Uppal, Kaushal Jadhav, Monish Natarajan, Mradul Agrawal, Debashish Chakravarty
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
[Re] Domain Generalization using Causal Matching
RICHARD D JILES, Mohna Chakraborty
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
[Re] Differentiable Spatial Planning using Transformers
Rohit Ranjan, Himadri Bhakta, Animesh Jha, Parv Maheshwari
- Published: 11 Apr 2022, Last Modified: 21 Apr 2024
- RC2021
- Readers: Everyone
Replication study of "Privacy-preserving Collaborative Learning with Automatic Transformation Search"
Karolina Drabent, Konrad Bereda, Stefan Wijnja, Thijs Sluijter
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
Replicating and Improving GAN2Shape Through Novel Shape Priors and Training Steps
Alfred Nilsson, Alessio Galatolo
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
[Re] Exacerbating Algorithmic Bias through Fairness Attacks
Angelos Nalmpantis, Apostolos Panagiotopoulos, John Gkountouras, Konstantinos Papakostas
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Noah Van der Vleuten, Tadija Radusinović, Rick Akkerman, Meilina Reksoprodjo
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
[RE] Nondeterminism and Instability in Neural Network Optimization
Waqas Ahmed, Sheeba Samuel
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
[Re] Assessing the Reliability of Word Embedding Gender Bias Measures
Anonymous
- 05 Feb 2022 (modified: 05 May 2023)
- ML Reproducibility Challenge 2021 Fall Blind Submission
- Readers: Everyone
[Re] DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
Anonymous
- 05 Feb 2022 (modified: 05 May 2023)
- ML Reproducibility Challenge 2021 Fall Blind Submission
- Readers: Everyone
Reproduction Study of Variational Fair Clustering
Floor Eijkelboom, Mark Fokkema, Anna Lau, Luuk Verheijen
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
On the reproducibility of "Exacerbating Algorithmic Bias through Fairness Attacks"
Andrea Lombardo, Matteo Tafuro, Tin Hadži Veljković, Lasse Becker-Czarnetzki
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021 OutstandingPaper
- Readers: Everyone
Replication study of "Explaining in Style: Training a GAN to explain a classifier in StyleSpace"
Daan Korporaal, Evaline Bosch, Rutger Ettes, Gijs Van Meer
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
Reproducibility study for "Explaining in Style: Training a GAN to explain a classifier in StyleSpace"
Chase Myers van de Geijn, Victor Kyriacou, Irene Papadopoulou, Vasiliki Vasileiou
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
Badder Seeds: Reproducing the Evaluation of Lexical Methodsfor Bias Measurement
Jille Van der Togt, Lea Tiyavorabun, Matteo Rosati, Giulio Starace
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021
- Readers: Everyone
Strategic classification made practical: reproduction
Guilly Kolkman, Maks kulicki, Jan Athmer, Alex Labro
- Published: 11 Apr 2022, Last Modified: 05 May 2023
- RC2021 OutstandingPaper
- Readers: Everyone