9 citations · 26 across the 11 of their papers we have counts for
14 papers
Norm-Scaling for Out-of-Distribution Detection
Deepak Ravikumar, Kaushik Roy
Out-of-Distribution (OoD) inputs are examples that do not belong to the true underlying distribution of the dataset. Research has shown that deep neural nets make confident mispred…
Process Knowledge-infused Learning for Suicidality Assessment on Social Media
Kaushik Roy, Manas Gaur, Qi Zhang +1
Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthca…
BERMo: What can BERT learn from ELMo?
Sangamesh Kodge, Kaushik Roy
We propose BERMo, an architectural modification to BERT, which makes predictions based on a hierarchy of surface, syntactic and semantic language features. We use linear combinatio…
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning
Adarsh Kumar Kosta, Malik Aqeel Anwar, Priyadarshini Panda +2
Present-day Deep Reinforcement Learning (RL) systems show great promise towards building intelligent agents surpassing human-level performance. However, the computational complexit…
Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems
Yinghan Long, Indranil Chakraborty, Gopalakrishnan Srinivasan +1
The ubiquitous use of IoT and machine learning applications is creating large amounts of data that require accurate and real-time processing. Although edge-based smart data process…
Knowledge-intensive Language Understanding for Explainable AI
Amit Sheth, Manas Gaur, Kaushik Roy +1
AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by inability to fully trust an AI system that it will n…