270 citations · 646 across the 26 of their papers we have counts for
11 papers · 1 filter
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss +4
Transformer-based Language Models have become ubiquitous in Natural Language Processing (NLP) due to their impressive performance on various tasks. However, expensive training as w…
Auto-Transfer: Learning to Route Transferrable Representations
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss +3
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be diffic…
Local Explanations for Reinforcement Learning
Ronny Luss, Amit Dhurandhar, Miao Liu
Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood b…
AI Explainability 360: Impact and Design
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.…
When Stability meets Sufficiency: Informative Explanations that do not Overwhelm
Ronny Luss, Amit Dhurandhar
Recent studies evaluating various criteria for explainable artificial intelligence (XAI) suggest that fidelity, stability, and comprehensibility are among the most important metric…
Enhancing Simple Models by Exploiting What They Already Know
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
There has been recent interest in improving performance of simple models for multiple reasons such as interpretability, robust learning from small data, deployment in memory constr…