Search Results for author:
Found 11 papers, 8 papers with code
Federated Continual Learning for Text Classification via Selective Inter-client Transfer
The objective of Federated Continual Learning (FCL) is to improve deep learning models over life time at each client by (relevant and efficient) knowledge transfer without sharing data.
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents.
TopicBERT for Energy Efficient Document Classification
Our work thus focuses on optimizing the computational cost of fine-tuning for document classification.
Neural Topic Modeling with Continual Lifelong Learning
To address the problem, we propose a lifelong learning framework for neural topic modeling that can continuously process streams of document collections, accumulate topics and guide future topic modeling tasks by knowledge transfer from several sources to better deal with the sparse data.
Explainable and Discourse Topic-aware Neural Language Understanding
Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics.
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019.
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers
in topic modeling, (2) A novel lifelong learning mechanism into neural topic modeling framework to demonstrate continuous learning in sequential document collections and minimizing catastrophic forgetting.
Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Though word embeddings and topics are complementary representations, several past works have only used pre-trained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i. e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost.
Document Informed Neural Autoregressive Topic Models with Distributional Prior
Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion.
