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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Empirical Evidence
Published:
‘Do you think it’s possible to get six packs by just exercising daily and controlling the food intake? I mean without taking extra whey protein and supplements and without going to the gym.’
Deep Learning for Autonomous Vehicles: Milestone 1
Published:
In this post, I provide a kickstarter guide to getting started with TrajNet++ framework for human trajectory forecasting, which will prove useful in helping you approach Milestone 1.
TrajNet++ : Dataset Conversion
Published:
In this blog post, I provide a quick tutorial to converting external datasets into the desired .ndjson format using the TrajNet++ framework. This post will focus on utilizing the TrajNet++ dataset code for easily converting new datasets.
Introducing TrajNet++ : A Framework for Human Trajectory Forecasting
Published:
In this blog post, I provide a kickstarter guide to our recently released TrajNet++ framework for human trajectory forecasting. We recently released TrajNet++ Challenge for agent-agent based trajectory forecasting. Details regarding the challenge can be found here. This post will focus on utilizing the TrajNet++ framework for easily creating datasets and learning human motion forecasting models.
portfolio
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publications
Adversarial Loss in Human Trajectory Prediction
Published in European Association for Research in Transportation (hEART), 2019
Highlighted an unexpected pitfall in the state-of-the-art architecture for multimodal human prediction via controlled experiments. Proposed a modification to the architecture leveraging the progress in the GAN community. Demonstrate the efficacy of the proposed modification on real world datasets, indicating room for improvement on state-of-the-art.
Recommended citation: Parth Kothari, Alexandre Alahi (2019). "Adversarial Loss in Human Trajectory Prediction." European Association for Research in Transportation (hEART). https://transp-or.epfl.ch/heart/2019/abstracts/hEART_2019_paper_148.pdf
Collaborative Sampling in Generative Adversarial Networks
Published in Association for Advancement of Artificial Intelligence (AAAI), 2020
Developed a collaborative sampling scheme between the generator and the discriminator for improved data generation during sampling. Proposed a practical discriminator shaping method for effective sample refinement. Experiments on synthetic and image datasets demonstrate the efficacy of our method to improve generated samples both quantitatively and qualitatively, offering a new degree of freedom in GAN sampling.
Recommended citation: Liu, Yuejiang, Parth Kothari and Alexandre Alahi. “Collaborative Sampling in Generative Adversarial Networks.” Association for Advancement of Artificial Intelligence (AAAI) 2020. https://arxiv.org/pdf/1902.00813.pdf
TrajNet++: Large-scale Trajectory Forecasting Benchmark
Published in 2nd Workshop on Long-Term Human Motion Prediction, ICRA, 2020
Current forecasting methods have been evaluated on different subsets of the available data without proper indexing of trajectories making it difficult to objectively compare the techniques. Our benchmark provides defined categorization of trajectories as well as a unified extensive evaluation system to test the gathered methods for a fair comparison.
Recommended citation: Kothari, Parth, Sven Kreiss and Alexandre Alahi. “Human Trajectory Forecasting in Crowds: A Deep Learning Perspective.” (2020). arxiv preprint arXiv:2007.03639. https://arxiv.org/pdf/2007.03639.pdf
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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