Search Results for author: Wei-Cheng Chang

Found 19 papers, 11 papers with code

Extreme Zero-Shot Learning for Extreme Text Classification

1 code implementation16 Dec 2021 Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon

To learn the semantic embeddings of instances and labels with raw text, we propose to pre-train Transformer-based encoders with self-supervised contrastive losses.

Classification Multi Label Text Classification +2

Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction

4 code implementations ICLR 2022 Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S Dhillon

We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.

Extreme Multi-Label Classification Language Modelling +4

Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification

1 code implementation NeurIPS 2021 Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon

Despite leveraging pre-trained transformer models for text representation, the fine-tuning procedure of transformer models on large label space still has lengthy computational time even with powerful GPUs.

Multi Label Text Classification Multi-Label Text Classification +1

Label Disentanglement in Partition-based Extreme Multilabel Classification

no code implementations NeurIPS 2021 Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon

Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e. g., millions or more).

Classification Disentanglement +2

PECOS: Prediction for Enormous and Correlated Output Spaces

no code implementations12 Oct 2020 Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon

In this paper, we propose the Prediction for Enormous and Correlated Output Spaces (PECOS) framework, a versatile and modular machine learning framework for solving prediction problems for very large output spaces, and apply it to the eXtreme Multilabel Ranking (XMR) problem: given an input instance, find and rank the most relevant items from an enormous but fixed and finite output space.

Kernel Stein Generative Modeling

no code implementations6 Jul 2020 Wei-Cheng Chang, Chun-Liang Li, Youssef Mroueh, Yiming Yang

NCK is crucial for successful inference with SVGD in high dimension, as it adapts the kernel to the noise level of the score estimate.

Bayesian Inference

Pre-training Tasks for Embedding-based Large-scale Retrieval

no code implementations ICLR 2020 Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar

We consider the large-scale query-document retrieval problem: given a query (e. g., a question), return the set of relevant documents (e. g., paragraphs containing the answer) from a large document corpus.

Information Retrieval Link Prediction

XL-Editor: Post-editing Sentences with XLNet

no code implementations19 Oct 2019 Yong-Siang Shih, Wei-Cheng Chang, Yiming Yang

While neural sequence generation models achieve initial success for many NLP applications, the canonical decoding procedure with left-to-right generation order (i. e., autoregressive) in one-pass can not reflect the true nature of human revising a sentence to obtain a refined result.

Style Transfer Text Style Transfer

Taming Pretrained Transformers for Extreme Multi-label Text Classification

1 code implementation7 May 2019 Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong, Yiming Yang, Inderjit Dhillon

However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.

Classification Extreme Multi-Label Classification +6

Implicit Kernel Learning

no code implementations26 Feb 2019 Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos

While learning the kernel in a data driven way has been investigated, in this paper we explore learning the spectral distribution of kernel via implicit generative models parametrized by deep neural networks.

Text Generation

Contextual Encoding for Translation Quality Estimation

1 code implementation WS 2018 Junjie Hu, Wei-Cheng Chang, Yuexin Wu, Graham Neubig

In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach.


The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints

1 code implementation1 Jun 2017 Po-Wei Wang, Wei-Cheng Chang, J. Zico Kolter

In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints.

Learning Word Embeddings

MMD GAN: Towards Deeper Understanding of Moment Matching Network

2 code implementations NeurIPS 2017 Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos

In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN.

Data-driven Random Fourier Features using Stein Effect

no code implementations23 May 2017 Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabas Poczos

Large-scale kernel approximation is an important problem in machine learning research.

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

19 code implementations21 Mar 2017 Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.

Multivariate Time Series Forecasting Time Series +1

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