(*: equal contribution, ___: intern that I have mentored)
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
2019 Conference on Empirical Methods in Natural Language Processing (EMNLP)
pdf / code
Jointly extracting aspects and sentiments for sentiment classification requires considerable labeled sentences which are highly labor-intensive. We innovatively alleviate the problem via unsupervised domain adaptation from a sufficiently labeled domain. We propose a novel selective adversarial learning method to learn correlation vectors between aspects and sentiments and attentively transfer them across domains.
From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for
Histopathology Cancer Image Classification
Jiezhang Cao, Xinjuan Fan, Xiaoying Lou, Hailing Liu, Jinlong Hou, Xiao Han, Jianhua Yao, Qingyao Wu,
22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
This work is the first to empower digital pathology image classification directly based on microscopy images. Specifically, we resort to unsupervised domain adaptation from whole slide images to remedy the lack of annotated microscopy images. The proposed resolves intra-domain discrepancy and class imbalance via entropy minimization and sample re-weighting, respectively, besides inter-domain discrepancy.
We devote to conquering a critical challenge in meta-learning, namely task uncertainty and heterogeneity, where tasks may be originated from wildly different distributions. We propose a highly-motivated meta-learning algorithm with hierarchical task clustering. It not only alleviates task heterogeneity via knowledge customization to different clusters of tasks, but also preserves knowledge generalization among a cluster of similar tasks.
This work improves spatial-temporal prediction tasks like traffic prediction for those cities with only limited training data in a short period. The improvement is attributed to the knowledge transferred from other cities with sufficient data covering long periods. We first introduce the meta-learning paradigm into spatial-temporal prediction, and formulate the transferable knowledge as both short-term and long-term spatial-temporal patterns which are represented as model parameters and an explicit memory, respectively.
We aim at identifying sentiment towards aspect terms in a sentence, while annotating sentences in this case is prohibitively expensive. Innovatively, we leverage knowledge from more easily accessible sentences whose sentiment is annotated to aspect categories. We propose a multi-granularity alignment network to achieve domain adaptation, which resolves both aspect granularity inconsistency and feature discrepancy between domains.
This work is the pioneer in automatically identifying an effective multitask model for a multitask problem, empowered by a groundbreaking learning to multitask framework.
This work opens a new door to improve transfer learning effectiveness. We propose a groundbreaking learning to transfer framework to automatically optimize what and how to transfer across domains, by taking advantage of previous transfer learning experiences.
We are dedicated to improve cross-domain sentiment classification, from the perspectives of discovering domain-invariant emotion words of higher quality for knowledge transfer as well as capturing domain-specific emotion words for sentiment classification. The proposed hierarchical attention transfer network achieves the two goals with a hierarchical attention mechanism and a non-pivots network, respectively.
Though contextual bandit effectively solves the exploitation-exploration dilemma in recommendation systems, it suffers from over-exploration in the cold-start scenario. This work is the first to alleviate the problem by transferring knowledge from other domains. We propose a transferable contextual bandit policy which transfers observations to improve user interests estimation for exploitation and thus accelerates the exploration.transfer network achieves the two goals with a hierarchical attention mechanism and a non-pivots network, respectively.
Highly motivated by human beings' capabilities to reflect on transfer learning experiences, we propose a novel transfer learning framework to learn meta-knowledge from historical transfer learning experiences and apply the meta-knowledge to automatically optimize what to transfer in the future.
This work focuses on cross-domain sentiment classification, e.g., sentiment classification of book reviews by transferring knowledge from electronics product reviews. The key here is to identify domain-invariant emotion words as the transferable knowledge. We are the first to automatically learn domain-invariant emotion words by introducing an end-to-end adversarial memory network and offer a direct visualization of them.
We devote to address the problems of overfitting and high-variance gradients, when training deep neural networks on high dimension but low sample size data such as genetic data for phenotype prediction in bioinformatics. We propose a deep neural pursuit network which alleviates overfitting by selecting a subset of features and reduces variance by averaging the gradients over multiple dropouts.
This work provides a theoretical analysis and guarantee for the scalable heterogeneous translated hashing method which is proposed to build the correspondence between heterogeneous domains.
We propose the first principled approach to transfer knowledge between domains, each of which comprises multiple modalities of datasets. We conduct a case study of air quality prediction -- borrowing knowledge from the cities with sufficient annotations and data to the cities with either scarce annotations or insufficient data in any modality. The proposed method formulates the transferable knowledge as semantically related dictionaries for multiple modalities learned from a source domain and labeled examples.
This work first transfers knowledge from posts in the social media side to sensors in the physical world to improve ubiquitous computing tasks such as activity recognition. We propose a co-regularized heterogeneous transfer learning model to discover the transferable feature representations that bridge two domains in heterogeneous representation structures, co-regularized by both correspondence and labels.
Knowledge transfer between domains that lie in heterogeneous feature spaces but have no access to explicit correspondence is almost impossible. This work is the pioneer in using hashing to build the correspondence between such domains. The proposed method simultaneously learns hash functions embedding heterogeneous domains into different Hamming spaces, and a translator aligning these spaces.
The source code of this website is adapted from both this and this page.