47 citations · 72 across the 4 of their papers we have counts for
4 papers
Extremely Simple Activation Shaping for Out-of-Distribution Detection
Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok +1
The separation between training and deployment of machine learning models implies that not all scenarios encountered in deployment can be anticipated during training, and therefore…
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer
Arjun Ashok, K J Joseph, Vineeth Balasubramanian
In class-incremental learning, the model is expected to learn new classes continually while maintaining knowledge on previous classes. The challenge here lies in preserving the mod…
Learning Modular Structures That Generalize Out-of-Distribution
Arjun Ashok, Chaitanya Devaguptapu, Vineeth Balasubramanian
Out-of-distribution (O.O.D.) generalization remains to be a key challenge for real-world machine learning systems. We describe a method for O.O.D. generalization that, through trai…
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi +37
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benc…