20 citations · 34 across the 8 of their papers we have counts for
10 papers
Federated Continual Learning for Text Classification via Selective Inter-client Transfer
Yatin Chaudhary, Pranav Rai, Matthias Schubert +2
In this work, we combine the two paradigms: Federated Learning (FL) and Continual Learning (CL) for text classification task in cloud-edge continuum. The objective of Federated Con…
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces
Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsit…
TopicBERT for Energy Efficient Document Classification
Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena +3
Prior research notes that BERT's computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissio…
Neural Topic Modeling with Continual Lifelong Learning
Pankaj Gupta, Yatin Chaudhary, Thomas Runkler +1
Lifelong learning has recently attracted attention in building machine learning systems that continually accumulate and transfer knowledge to help future learning. Unsupervised top…
Explainable and Discourse Topic-aware Neural Language Understanding
Yatin Chaudhary, Hinrich Schütze, Pankaj Gupta
Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics. While introducing topical semant…
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
Yatin Chaudhary, Pankaj Gupta, Hinrich Schütze
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a multi-dimensional and broad…
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers
Yatin Chaudhary, Pankaj Gupta, Thomas Runkler
Topic models such as LDA, DocNADE, iDocNADEe have been popular in document analysis. However, the traditional topic models have several limitations including: (1) Bag-of-words (BoW…
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
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 sparsi…
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
Pankaj Gupta, Yatin Chaudhary, Florian Buettner +1
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 Structur…
Document Informed Neural Autoregressive Topic Models with Distributional Prior
Pankaj Gupta, Yatin Chaudhary, Florian Buettner +1
We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts "artificial neural…