Search Results for author:
Found 9 papers, 4 papers with code
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets
The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets.
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics.
Ranked #1 on
Document Classification
on SciDocs (MeSH)
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives
Contrastive self-supervised training objectives enabled recent successes in image representation pretraining by learning to contrast input-input pairs of augmented images as either similar or dissimilar.
Data-Efficient Pretraining via Contrastive Self-Supervision
For natural language processing `text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on increasingly larger `task-external' data.
Self-supervised Contrastive Zero to Few-shot Learning from Small, Long-tailed Text data
We thus approach pretraining from a miniaturisation perspective, such as not to require massive external data sources and models, or learned translations from continuous input embeddings to discrete labels.
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-)supervised training.
Explainable Artificial Intelligence (XAI)
Model Compression
+1
MoRTy: Unsupervised Learning of Task-specialized Word Embeddings by Autoencoding
Word embeddings have undoubtedly revolutionized NLP.
Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs
Comments on web news contain controversies that manifest as inter-group agreement-conflicts.
Common Round: Application of Language Technologies to Large-Scale Web Debates
Web debates play an important role in enabling broad participation of constituencies in social, political and economic decision-taking.
