Search Results for author: Qianli Ma

Found 20 papers, 8 papers with code

CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning

no code implementations EMNLP 2021 Zhenxi Lin, Qianli Ma, Jiangyue Yan, Jieyu Chen

Metaphors are ubiquitous in natural language, and detecting them requires contextual reasoning about whether a semantic incongruence actually exists.

The Power of Points for Modeling Humans in Clothing

no code implementations ICCV 2021 Qianli Ma, Jinlong Yang, Siyu Tang, Michael J. Black

The geometry feature can be optimized to fit a previously unseen scan of a person in clothing, enabling the scan to be reposed realistically.

Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification

no code implementations ACL 2021 Haibin Chen, Qianli Ma, Zhenxi Lin, Jiangyue Yan

We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics.

Text Classification

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

1 code implementation NeurIPS 2021 Shaofei Wang, Marko Mihajlovic, Qianli Ma, Andreas Geiger, Siyu Tang

In contrast, we propose an approach that can quickly generate realistic clothed human avatars, represented as controllable neural SDFs, given only monocular depth images.

Meta-Learning

Embedding-based Product Retrieval in Taobao Search

no code implementations17 Jun 2021 Sen Li, Fuyu Lv, Taiwei Jin, Guli Lin, Keping Yang, Xiaoyi Zeng, Xiao-Ming Wu, Qianli Ma

We evaluate MGDSPR on Taobao Product Search with significant metrics gains observed in offline experiments and online A/B tests.

Learning Representations for Incomplete Time Series Clustering

1 code implementation AAAI 2021 Qianli Ma, Chuxin Chen, Sen Li, Garrison W. Cottrell

Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating train- ing manner.

Imputation Representation Learning +2

SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks

no code implementations CVPR 2021 Shunsuke Saito, Jinlong Yang, Qianli Ma, Michael J. Black

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar.

PLACE: Proximity Learning of Articulation and Contact in 3D Environments

1 code implementation12 Aug 2020 Siwei Zhang, Yan Zhang, Qianli Ma, Michael J. Black, Siyu Tang

To synthesize realistic human-scene interactions, it is essential to effectively represent the physical contact and proximity between the body and the world.

Learning Representations for Time Series Clustering

2 code implementations NeurIPS 2019 Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell

When applying seq2seq to time series clustering, obtaining a representation that effectively represents the temporal dynamics of the sequence, multi-scale features, and good clustering properties remains a challenge.

Anomaly Detection Representation Learning +2

Frontal Low-rank Random Tensors for Fine-grained Action Segmentation

1 code implementation3 Jun 2019 Yan Zhang, Krikamol Muandet, Qianli Ma, Heiko Neumann, Siyu Tang

In this paper, we propose an approach to representing high-order information for temporal action segmentation via a simple yet effective bilinear form.

Action Parsing Action Segmentation

Classification of lung nodules in CT images based on Wasserstein distance in differential geometry

no code implementations30 Jun 2018 Min Zhang, Qianli Ma, Chengfeng Wen, Hai Chen, Deruo Liu, Xianfeng GU, Jie He, Xiaoyin Xu

The Wasserstein distance between the nodules is calculated based on our new spherical optimal mass transport, this new algorithm works directly on sphere by using spherical metric, which is much more accurate and efficient than previous methods.

Computed Tomography (CT) General Classification +1

Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework

no code implementations13 Nov 2017 Qianli Ma, Lifeng Shen, Garrison W. Cottrell

As an efficient recurrent neural network (RNN) model, reservoir computing (RC) models, such as Echo State Networks, have attracted widespread attention in the last decade.

Time Series

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

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