15 citations · 24 across the 6 of their papers we have counts for
8 papers
General Geospatial Inference with a Population Dynamics Foundation Model
Mohit Agarwal, Mimi Sun, Chaitanya Kamath +31
Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex rel…
Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting
Parth Kothari, Danya Li, Yuejiang Liu +1
Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this c…
Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting
Parth Kothari, Alexandre Alahi
Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. Following the success of Soci…
TF-GNN: Graph Neural Networks in TensorFlow
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais +24
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that…
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
Parth Kothari, Christian Perone, Luca Bergamini +2
Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an…
Interpretable Social Anchors for Human Trajectory Forecasting in Crowds
Parth Kothari, Brian Sifringer, Alexandre Alahi
Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and co…