Search Results for author:
Found 15 papers, 8 papers with code
Wide & Deep Learning for Recommender Systems
Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
Ranked #2 on
Click-Through Rate Prediction
on Bing News
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
(i) We develop SLATEQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates.
Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
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.
Multi-modal Discriminative Model for Vision-and-Language Navigation
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.
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
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.
Transferable Representation Learning in Vision-and-Language Navigation
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
RecSim: A Configurable Simulation Platform for Recommender Systems
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users.
Generalized Natural Language Grounded Navigation via Environment-agnostic Multitask Learning
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
VALAN: Vision and Language Agent Navigation
VALAN is a lightweight and scalable software framework for deep reinforcement learning based on the SEED RL architecture.
Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps
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.
Learning to Represent Image and Text with Denotation Graph
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.
A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus
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.
On the Evaluation of Vision-and-Language Navigation Instructions
Vision-and-Language Navigation wayfinding agents can be enhanced by exploiting automatically generated navigation instructions.
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
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.
