Search Results for author: Tianlin Shi

Found 7 papers, 4 papers with code

Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

3 code implementations ICLR 2018 Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang

Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates.

reinforcement-learning

World of Bits: An Open-Domain Platform for Web-Based Agents

no code implementations ICML 2017 Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, Percy Liang

While simulated game environments have greatly accelerated research in reinforcement learning, existing environments lack the open-domain realism of tasks in computer vision or natural language processing, which operate on artifacts created by humans in natural, organic settings.

reinforcement-learning

Adversarial Learning for Neural Dialogue Generation

8 code implementations EMNLP 2017 Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, Dan Jurafsky

In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.

Dialogue Evaluation Dialogue Generation

Learning Where to Sample in Structured Prediction

1 code implementation9 May 2015 Tianlin Shi, Jacob Steinhardt, Percy Liang

In structured prediction, most inference algorithms allocate a homogeneous amount of computation to all parts of the output, which can be wasteful when different parts vary widely in terms of difficulty.

reinforcement-learning Structured Prediction

Max-margin Deep Generative Models

2 code implementations NeurIPS 2015 Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang

Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability.

A Reverse Hierarchy Model for Predicting Eye Fixations

no code implementations CVPR 2014 Tianlin Shi, Liang Ming, Xiaolin Hu

A number of psychological and physiological evidences suggest that early visual attention works in a coarse-to-fine way, which lays a basis for the reverse hierarchy theory (RHT).

Image Super-Resolution Saliency Detection

Online Bayesian Passive-Aggressive Learning

no code implementations12 Dec 2013 Tianlin Shi, Jun Zhu

Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning.

Bayesian Inference online learning +1

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