no code implementations • ACL (splurobonlp) 2021 • Sayali Kulkarni, Shailee Jain, Mohammad Javad Hosseini, Jason Baldridge, Eugene Ie, Li Zhang
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates.
no code implementations • EMNLP (SpLU) 2020 • Harsh Mehta, Yoav Artzi, Jason Baldridge, Eugene Ie, Piotr Mirowski
These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn.
1 code implementation • 14 Mar 2021 • Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e. g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years.
no code implementations • EACL 2021 • Ming Zhao, Peter Anderson, Vihan Jain, Su Wang, Alexander Ku, Jason Baldridge, Eugene Ie
Vision-and-Language Navigation wayfinding agents can be enhanced by exploiting automatically generated navigation instructions.
no code implementations • 18 Nov 2020 • BoWen Zhang, Hexiang Hu, Joonseok Lee, Ming Zhao, Sheide Chammas, Vihan Jain, Eugene Ie, Fei Sha
Identifying a short segment in a long video that semantically matches a text query is a challenging task that has important application potentials in language-based video search, browsing, and navigation.
1 code implementation • 23 Oct 2020 • Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, Eugene Ie
Summarization is the task of compressing source document(s) into coherent and succinct passages.
3 code implementations • EMNLP 2020 • Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie, Jason Baldridge
We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset.
Ranked #5 on
Vision and Language Navigation
on RxR
no code implementations • EMNLP 2020 • BoWen Zhang, Hexiang Hu, Vihan Jain, Eugene Ie, Fei Sha
Recent progresses have leveraged the ideas of pre-training (from language modeling) and attention layers in Transformers to learn representation from datasets containing images aligned with linguistic expressions that describe the images.
no code implementations • 21 Aug 2020 • Sayali Kulkarni, Shailee Jain, Mohammad Javad Hosseini, Jason Baldridge, Eugene Ie, Li Zhang
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations.
no code implementations • 13 Jun 2020 • Zhiyun Lu, Eugene Ie, Fei Sha
Many methods have been proposed to quantify the predictive uncertainty associated with the outputs of deep neural networks.
1 code implementation • ACL 2020 • Wang Zhu, Hexiang Hu, Jiacheng Chen, Zhiwei Deng, Vihan Jain, Eugene Ie, Fei Sha
To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially.
1 code implementation • ECCV 2020 • Xin Eric Wang, Vihan Jain, Eugene Ie, William Yang Wang, Zornitsa Kozareva, Sujith Ravi
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
4 code implementations • 10 Jan 2020 • Harsh Mehta, Yoav Artzi, Jason Baldridge, Eugene Ie, Piotr Mirowski
These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn.
Ranked #7 on
Vision and Language Navigation
on Touchdown Dataset
1 code implementation • 6 Dec 2019 • Larry Lansing, Vihan Jain, Harsh Mehta, Haoshuo Huang, Eugene Ie
VALAN is a lightweight and scalable software framework for deep reinforcement learning based on the SEED RL architecture.
no code implementations • 25 Sep 2019 • Xin Wang, Vihan Jain, Eugene Ie, William Wang, Zornitsa Kozareva, Sujith Ravi
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
no code implementations • CONLL 2019 • Daniel Gillick, Sayali Kulkarni, Larry Lansing, Alessandro Presta, Jason Baldridge, Eugene Ie, Diego Garcia-Olano
We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search.
1 code implementation • 11 Sep 2019 • Eugene Ie, Chih-Wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, Craig Boutilier
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users.
no code implementations • ICCV 2019 • Haoshuo Huang, Vihan Jain, Harsh Mehta, Alexander Ku, Gabriel Magalhaes, Jason Baldridge, Eugene Ie
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 environments to achieve navigation goals.
Ranked #115 on
Vision and Language Navigation
on VLN Challenge
1 code implementation • 11 Jul 2019 • Gabriel Ilharco, Vihan Jain, Alexander Ku, Eugene Ie, Jason Baldridge
We address fundamental flaws in previously used metrics and show how Dynamic Time Warping (DTW), a long known method of measuring similarity between two time series, can be used for evaluation of navigation agents.
no code implementations • WS 2019 • Haoshuo Huang, Vihan Jain, Harsh Mehta, Jason Baldridge, Eugene Ie
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 environment to achieve prescribed navigation goals.
3 code implementations • 29 May 2019 • Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Morgane Lustman, Vince Gatto, Paul Covington, Jim McFadden, Tushar Chandra, Craig Boutilier
(i) We develop SLATEQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates.
no code implementations • ACL 2019 • Vihan Jain, Gabriel Magalhaes, Alexander Ku, Ashish Vaswani, Eugene Ie, Jason Baldridge
We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths.
no code implementations • 19 Dec 2013 • Samy Bengio, Jeff Dean, Dumitru Erhan, Eugene Ie, Quoc Le, Andrew Rabinovich, Jonathon Shlens, Yoram Singer
Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy.