2 code implementations • 27 Nov 2023 • Sicong Leng, Yang Zhou, Mohammed Haroon Dupty, Wee Sun Lee, Sam Conrad Joyce, Wei Lu
We make multiple contributions to initiate research on this task.
no code implementations • NeurIPS 2020 • Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee
In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications.
1 code implementation • 12 Oct 2022 • Zichen Liu, Siyi Li, Wee Sun Lee, Shuicheng Yan, Zhongwen Xu
Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout.
no code implementations • 1 Oct 2022 • Zirui Zhao, Wee Sun Lee, David Hsu
Natural language generally describes objects and spatial relations with compositionality and ambiguity, two major obstacles to effective language grounding.
1 code implementation • 1 May 2022 • Yang Zhou, Wee Sun Lee
This ignores the context of entity pairs and the label correlations between the none class and pre-defined classes, leading to sub-optimal predictions.
Document-level Relation Extraction
Emotion Classification
+2
no code implementations • 17 Mar 2022 • Mohammed Haroon Dupty, Wee Sun Lee
Individualization refers to artificially distinguishing a node in the graph and refinement is the propagation of this information to other nodes through message passing.
1 code implementation • 2 Mar 2022 • Yong Liang Goh, Wee Sun Lee, Xavier Bresson, Thomas Laurent, Nicholas Lim
This paper exemplifies the integration of entropic regularized optimal transport techniques as a layer in a deep reinforcement learning network.
no code implementations • 25 Feb 2022 • Wei Gao, David Hsu, Wee Sun Lee
To solve these issues, we present Context Hierarchy IRL(CHIRL), a new IRL algorithm that exploits the context to scale up IRL and learn reward functions of complex behaviors.
1 code implementation • 3 Feb 2022 • Dixant Mittal, Siddharth Aravindan, Wee Sun Lee
Depending upon the smoothness of the action-value function, one approach to overcoming this issue is through online learning, where information is interpolated among similar states; Policy Gradient Search provides a practical algorithm to achieve this.
no code implementations • 29 Sep 2021 • Siddharth Aravindan, Dixant Mittal, Wee Sun Lee
These layers rely on Gaussian dropouts and are inserted in between the layers of the deep neural network model to help facilitate variational Thompson sampling.
1 code implementation • ICLR 2022 • Mohammed Haroon Dupty, Yanfei Dong, Wee Sun Lee
Message passing Graph Neural Networks (GNNs) are known to be limited in expressive power by the 1-WL color-refinement test for graph isomorphism.
1 code implementation • 5 Jul 2021 • Muhammad Rizki Maulana, Wee Sun Lee
Ensemble and auxiliary tasks are both well known to improve the performance of machine learning models when data is limited.
no code implementations • 25 Apr 2021 • Xiao Ma, David Hsu, Wee Sun Lee
Manipulating deformable objects, such as ropes and clothing, is a long-standing challenge in robotics, because of their large degrees of freedom, complex non-linear dynamics, and self-occlusion in visual perception.
no code implementations • 7 Feb 2021 • Siddharth Aravindan, Wee Sun Lee
We derive a variational Thompson sampling approximation for DQNs which uses a deep network whose parameters are perturbed by a learned variational noise distribution.
no code implementations • 10 Dec 2020 • Hieu Le Trung, Yiqing Xu, Wee Sun Lee
Designing a network to learn a molecule structure given its physical/chemical properties is a hard problem, but is useful for drug discovery tasks.
no code implementations • 19 Oct 2020 • Mohammed Haroon Dupty, Wee Sun Lee
In this paper, we propose to combine these approaches to learn better node and graph representations.
1 code implementation • 6 Aug 2020 • Xiao Ma, Siwei Chen, David Hsu, Wee Sun Lee
This paper presents Contrastive Variational Reinforcement Learning (CVRL), a model-based method that tackles complex visual observations in DRL.
2 code implementations • 12 Jun 2020 • Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, Jiashi Feng
In this work, we study performance degradation of GCNs by experimentally examining how stacking only TRANs or PROPs works.
no code implementations • 29 Feb 2020 • Xuan Su, Wee Sun Lee, Zhen Zhang
We propose a new sampling-based approach for approximate inference in filtering problems.
1 code implementation • ICLR 2020 • Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee, Nan Ye
The particle filter maintains a belief using learned discriminative update, which is trained end-to-end for decision making.
no code implementations • 22 Nov 2019 • Mohammed Haroon Dupty, Zhen Zhang, Wee Sun Lee
We address the problem of Visual Relationship Detection (VRD) which aims to describe the relationships between pairs of objects in the form of triplets of (subject, predicate, object).
3 code implementations • ACL 2019 • Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier
Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
1 code implementation • 3 Jun 2019 • Zhen Zhang, Fan Wu, Wee Sun Lee
Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks.
1 code implementation • 30 May 2019 • Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee
Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data.
no code implementations • 29 May 2019 • Panpan Cai, Yuanfu Luo, Aseem Saxena, David Hsu, Wee Sun Lee
LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both.
no code implementations • 28 May 2019 • Peter Karkus, Xiao Ma, David Hsu, Leslie Pack Kaelbling, Wee Sun Lee, Tomas Lozano-Perez
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems.
no code implementations • 26 Apr 2019 • Robert Pinsler, Peter Karkus, Andras Kupcsik, David Hsu, Wee Sun Lee
Our key observation is that experience can be directly generalized over target contexts.
1 code implementation • EMNLP 2018 • Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier
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.
no code implementations • COLING 2018 • Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier
First, we propose a method for target representation that better captures the semantic meaning of the opinion target.
no code implementations • 17 Jul 2018 • 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.
1 code implementation • Robotics: Science and Systems 2018 • Jue Kun Li, David Hsu, Wee Sun Lee
This paper introduces Push-Net, a deep recurrent neural network model, which enables a robot to push objects of unknown physical properties for re-positioning and re-orientation, using only visual camera images as input.
1 code implementation • ACL 2018 • Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier
Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification.
no code implementations • 30 May 2018 • Yuanfu Luo, Panpan Cai, Aniket Bera, David Hsu, Wee Sun Lee, Dinesh Manocha
Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time.
Robotics
2 code implementations • 23 May 2018 • 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.
1 code implementation • CVPR 2018 • Chen Li, Zhen Zhang, Wee Sun Lee, Gim Hee Lee
Human motion modeling is a classic problem in computer vision and graphics.
1 code implementation • 17 Feb 2018 • Panpan Cai, Yuanfu Luo, David Hsu, Wee Sun Lee
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost.
2 code implementations • 16 Oct 2017 • Wei Gao, David Hsu, Wee Sun Lee, ShengMei Shen, Karthikk Subramanian
How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information?
no code implementations • ICML 2017 • Andrew Wrigley, Wee Sun Lee, Nan Ye
We propose a new approximate inference algorithm for graphical models, tensor belief propagation, based on approximating the messages passed in the junction tree algorithm.
3 code implementations • ACL 2017 • Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier
Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space.
2 code implementations • NeurIPS 2017 • Peter Karkus, David Hsu, Wee Sun Lee
It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in a network learning architecture.
no code implementations • 6 Dec 2016 • Peter Karkus, Andras Kupcsik, David Hsu, Wee Sun Lee
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different "contexts".
1 code implementation • NeurIPS 2013 • Nan Ye, Adhiraj Somani, David Hsu, Wee Sun Lee
We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy.
no code implementations • 30 Mar 2016 • Nguyen Viet Cuong, Nan Ye, Wee Sun Lee
This suggests we should use a Lipschitz utility for AL if robustness is required.
no code implementations • 16 Feb 2016 • Min Chen, Emilio Frazzoli, David Hsu, Wee Sun Lee
We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks.
no code implementations • NeurIPS 2015 • Zhan Wei Lim, David Hsu, Wee Sun Lee
Adaptive stochastic optimization optimizes an objective function adaptively under uncertainty.
no code implementations • NeurIPS 2013 • Xinhua Zhang, Wee Sun Lee, Yee Whye Teh
For the representer theorem to hold, the linear functionals are required to be bounded in the RKHS, and we show that this is true for a variety of commonly used RKHS and invariances.
no code implementations • NeurIPS 2013 • Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, Hai Leong Chieu
We introduce a new objective function for pool-based Bayesian active learning with probabilistic hypotheses.
no code implementations • 27 Jun 2012 • Yi Wang, Kok Sung Won, David Hsu, Wee Sun Lee
Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over them.