1.6k citations · 1.7k across the 13 of their papers we have counts for
7 papers · 1 filter
Large Language Models with Controllable Working Memory
Daliang Li, Ankit Singh Rawat, Manzil Zaheer +5
Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP), owing to their excellent understanding and generation abilities. Remarkably…
When does mixup promote local linearity in learned representations?
Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit +3
Mixup is a regularization technique that artificially produces new samples using convex combinations of original training points. This simple technique has shown strong empirical p…
Decoupled Context Processing for Context Augmented Language Modeling
Zonglin Li, Ruiqi Guo, Sanjiv Kumar
Language models can be augmented with a context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have…
A Unified Framework for Optimization-Based Graph Coarsening
Manoj Kumar, Anurag Sharma, Sandeep Kumar
Graph coarsening is a widely used dimensionality reduction technique for approaching large-scale graph machine learning problems. Given a large graph, graph coarsening aims to lear…
Decentralized and stable matching in Peer-to-Peer energy trading
Nitin Singha, V Shreyas, Sandeep Kumar
In peer-to-peer (P2P) energy trading, a secured infrastructure is required to manage trade and record monetary transactions. A central server/authority can be used for this. But th…
ELM: Embedding and Logit Margins for Long-Tail Learning
Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat +1
Long-tail learning is the problem of learning under skewed label distributions, which pose a challenge for standard learners. Several recent approaches for the problem have propose…