Work on scaling laws has found that large language models (LMs) show
pre...
We revisit the common practice of evaluating adaptation of Online Contin...
Online continual learning (OCL) research has primarily focused on mitiga...
Continual Learning (CL) aims to sequentially train models on streams of
...
Current evaluations of Continual Learning (CL) methods typically assume ...
Existing works in inexact machine unlearning focus on achieving
indistin...
There has been increasing interest in building deep hierarchy-aware
clas...
Multi-object tracking has seen a lot of progress recently, albeit with
s...
For an unknown (new) classification dataset, choosing an appropriate dee...
The exploding cost and time needed for data labeling and model training ...
Binarization is an extreme network compression approach that provides la...
Deep neural networks are highly effective at a range of computational ta...
Deep Neural Networks, while being unreasonably effective for several vis...
Sentiment analysis (SA) using code-mixed data from social media has seve...
In this paper we describe an end to end Neural Model for Named Entity
Re...