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Works180 from public data
- 24,193
- 4,891
- 1,544
- Cross-domain sentiment classification via spectral feature alignment837
This work develops a general solution to sentiment classification when the authors do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain and proposes a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge.
- Transfer learning via dimensionality reduction727
A new dimensionality reduction method is proposed to find a latent space, which minimizes the distance between distributions of the data in different domains in a latentspace, which can be treated as a bridge of transferring knowledge from the source domain to the target domain.
- 631
- 554
- Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms448
A novel deep learning model, named coupled multi-layer attentions, where each layer consists of a couple of attentions with tensor operators that are learned interactively to dually propagate information between aspect terms and opinion terms.
- Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis431
A novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction and is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance.
- AdaRNN389
This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data and proposes Temporal Distribution Characterization to better characterize the distribution information in the TS.
- Heterogeneous Transfer Learning for Image Classification385
This paper proposes a heterogeneous transfer learning framework for knowledge transfer between text and images by enriching the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method.
- Supervised Representation Learning: Transfer Learning with Deep Autoencoders372
This paper proposes a supervised representation learning method based on deep autoencoders for transfer learning that consists of an embedding layer and a label encoding layer that minimize the difference between domains explicitly and encode label information in learning the representation.
- 312
- HYDRA: Massively Compositional Model for Cross-Project Defect Prediction295
Improvements of HYDRA over other baseline approaches in terms of F1-score and when inspecting the top 20 percent lines of code are substantial, and in most cases the improvements are significant and have large effect sizes across the 29 datasets.
- Multilingual Jailbreak Challenges in Large Language Models283
Experimental results show that ChatGPT fine-tuned with such data can achieve a substantial reduction in unsafe content generation, and a novel self-defense framework that automatically generates multilingual training data for safety fine-tuning is proposed.
- 253
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- Cross-Domain Co-Extraction of Sentiment and Topic Lexicons213
Experimental results show that the proposed domain adaptation framework for sentiment- and topic- lexicon co-extraction can extract precise lexicons in the target domain without any annotation.
- Distant Domain Transfer Learning203
Inspired by the cognitive process of human where two seemingly unrelated concepts can be connected by learning intermediate concepts gradually, a Selective Learning Algorithm (SLA) is proposed to solve the DDTL problem with supervised autoencoder or supervised convolutional autoen coder as a base model for handling different types of inputs.
- Adaptive Transfer Learning202
An Adaptive Transfer learning algorithm based on Gaussian Processes (AT-GP), which can be used to adapt the transfer learning schemes by automatically estimating the similarity between a source and a target task, is proposed.
- Short and sparse text topic modeling via self-aggregation201
A novel model integrating topic modeling with short text aggregation during topic inference is presented, founded on general topical affinity of texts rather than particular heuristics, making the model readily applicable to various short texts.
- Hybrid Heterogeneous Transfer Learning through Deep Learning201
A deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross- domain correspondences.
- Heterogeneous Domain Adaptation for Multiple Classes161
An efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions, to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain.
- Multi-task Learning145
A similarity measure is developed that can quantify transference among tasks and use this quantity to both better understand the optimization dynamics of multi-task learning as well as improve overall learning performance.
- 137
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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