263 citations · 438 across the 13 of their papers we have counts for
7 papers · 1 filter
VALAN: Vision and Language Agent Navigation
Larry Lansing, Vihan Jain, Harsh Mehta +2
VALAN is a lightweight and scalable software framework for deep reinforcement learning based on the SEED RL architecture. The framework facilitates the development and evaluation o…
RecSim: A Configurable Simulation Platform for Recommender Systems
Eugene Ie, Chih-wei Hsu, Martin Mladenov +5
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim all…
Transferable Representation Learning in Vision-and-Language Navigation
Haoshuo Huang, Vihan Jain, Harsh Mehta +4
Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic envir…
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
Gabriel Ilharco, Vihan Jain, Alexander Ku +2
In instruction conditioned navigation, agents interpret natural language and their surroundings to navigate through an environment. Datasets for studying this task typically contai…
Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
Vihan Jain, Gabriel Magalhaes, Alexander Ku +3
Advances in learning and representations have reinvigorated work that connects language to other modalities. A particularly exciting direction is Vision-and-Language Navigation(VLN…
Multi-modal Discriminative Model for Vision-and-Language Navigation
Haoshuo Huang, Vihan Jain, Harsh Mehta +2
Vision-and-Language Navigation (VLN) is a natural language grounding task where agents have to interpret natural language instructions in the context of visual scenes in a dynamic…
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
Eugene Ie, Vihan Jain, Jing Wang +10
Most practical recommender systems focus on estimating immediate user engagement without considering the long-term effects of recommendations on user behavior. Reinforcement learni…