Search Results for author: Tian Tian

Found 18 papers, 5 papers with code

FINETUNA: Fine-tuning Accelerated Molecular Simulations

no code implementations2 May 2022 Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, Zachary Ulissi

Finally, we demonstrate a technique for leveraging the interactive functionality built in to VASP to efficiently compute single point calculations within our online active learning framework without the significant startup costs.

Active Learning Transfer Learning

What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?

1 code implementation12 Feb 2022 Hangwei Qian, Tian Tian, Chunyan Miao

Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations.

Activity Recognition Contrastive Learning +1

Dynamic Window-level Granger Causality of Multi-channel Time Series

no code implementations14 Jun 2020 Zhiheng Zhang, Wen-Bo Hu, Tian Tian, Jun Zhu

In this paper, we present the dynamic window-level Granger causality method (DWGC) for multi-channel time series data.

Time Series

VFlow: More Expressive Generative Flows with Variational Data Augmentation

1 code implementation ICML 2020 Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian

Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.

Ranked #21 on Image Generation on CIFAR-10 (bits/dimension metric)

Density Estimation Image Generation +2

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

3 code implementations7 Mar 2019 Kenny Young, Tian Tian

With the representation learning problem simplified, we can perform experiments with significantly less computational expense.

Atari Games reinforcement-learning +1

Semi-crowdsourced Clustering with Deep Generative Models

1 code implementation NeurIPS 2018 Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang

We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset.

Variational Inference

D\'etection des mots non-standards dans les tweets avec des r\'eseaux de neurones (Detecting non-standard words in tweets with neural networks)

no code implementations JEPTALNRECITAL 2017 Tian Tian, Isabelle Tellier, Marco Dinarelli, Pedro Cardoso

Dans cet article, nous proposons un mod{\`e}le pour d{\'e}tecter dans les textes g{\'e}n{\'e}r{\'e}s par des utilisateurs (en particulier les tweets), les mots non-standards {\`a} corriger.

SENTS

Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification

no code implementations COLING 2016 Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, Katia Sycara

Due to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information.

General Classification

Joint Embeddings of Hierarchical Categories and Entities

no code implementations12 May 2016 Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, Katia Sycara

Due to the lack of structured knowledge applied in learning distributed representation of categories, existing work cannot incorporate category hierarchies into entity information.~We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases.

Machine Comprehension Based on Learning to Rank

no code implementations11 May 2016 Tian Tian, Yuezhang Li

Machine comprehension plays an essential role in NLP and has been widely explored with dataset like MCTest.

Learning-To-Rank Reading Comprehension

Jointly Modeling Topics and Intents with Global Order Structure

no code implementations7 Dec 2015 Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang

Modeling document structure is of great importance for discourse analysis and related applications.

Max-Margin Majority Voting for Learning from Crowds

no code implementations NeurIPS 2015 Tian Tian, Jun Zhu

Learning-from-crowds aims to design proper aggregation strategies to infer the unknown true labels from the noisy labels provided by ordinary web workers.

Bayesian Inference

Etiquetage morpho-syntaxique de tweets avec des CRF

no code implementations JEPTALNRECITAL 2015 Tian Tian, Dinarelli Marco, Tellier Isabelle, Cardoso Pedro

Nous nous int{\'e}ressons dans cet article {\`a} l{'}apprentissage automatique d{'}un {\'e}tiqueteur mopho-syntaxique pour les tweets en anglais.

Cannot find the paper you are looking for? You can Submit a new open access paper.