13 citations · 16 across the 4 of their papers we have counts for
6 papers
Structured Pruning Adapters
Lukas Hedegaard, Aman Alok, Juby Jose +1
Adapters are a parameter-efficient alternative to fine-tuning, which augment a frozen base network to learn new tasks. Yet, the inference of the adapted model is often slower than…
Design Considerations For Hypothesis Rejection Modules In Spoken Language Understanding Systems
Aman Alok, Rahul Gupta, Shankar Ananthakrishnan
Spoken Language Understanding (SLU) systems typically consist of a set of machine learning models that operate in conjunction to produce an SLU hypothesis. The generated hypothesis…
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification
Manoj Kumar, Varun Kumar, Hadrien Glaude +3
Practical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. Recent works towards mitigating this pro…
Automatic Discovery of Novel Intents & Domains from Text Utterances
Nikhita Vedula, Rahul Gupta, Aman Alok +1
One of the primary tasks in Natural Language Understanding (NLU) is to recognize the intents as well as domains of users' spoken and written language utterances. Most existing rese…
Towards classification parity across cohorts
Aarsh Patel, Rahul Gupta, Mukund Harakere +3
Recently, there has been a lot of interest in ensuring algorithmic fairness in machine learning where the central question is how to prevent sensitive information (e.g. knowledge a…
One-vs-All Models for Asynchronous Training: An Empirical Analysis
Rahul Gupta, Aman Alok, Shankar Ananthakrishnan
Any given classification problem can be modeled using multi-class or One-vs-All (OVA) architecture. An OVA system consists of as many OVA models as the number of classes, providing…