activity
20162020
most citedIntention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation

72 citations · 102 across the 7 of their papers we have counts for

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Showing 2018Show all

7 papers · 1 filter

cs.CL2018

Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification

Ruidan He, Wee Sun Lee, Hwee Tou Ng +1

We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our appr…

cs.RO2018

Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation

Peter Karkus, David Hsu, Wee Sun Lee

We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This…

cs.RO2018

PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians

Yuanfu Luo, Panpan Cai, Aniket Bera +3

This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is PORCA, a pedestrian motion prediction model that accounts f…

cs.CL2018

Exploiting Document Knowledge for Aspect-level Sentiment Classification

Ruidan He, Wee Sun Lee, Hwee Tou Ng +1

Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-leve…

cs.CV2018

Convolutional Sequence to Sequence Model for Human Dynamics

Chen Li, Zhen Zhang, Wee Sun Lee +1

Human motion modeling is a classic problem in computer vision and graphics. Challenges in modeling human motion include high dimensional prediction as well as extremely complicated…

cs.RO2018

Particle Filter Networks with Application to Visual Localization

Peter Karkus, David Hsu, Wee Sun Lee

Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particl…