3 citations · 5 across the 4 of their papers we have counts for
4 papers
When Do LLM Preferences Predict Downstream Behavior?
Katarina Slama, Alexandra Souly, Dishank Bansal +3
Preference-driven behavior in LLMs may be a necessary precondition for AI misalignment such as sandbagging: models cannot strategically pursue misaligned goals unless their behavio…
TaskMet: Task-Driven Metric Learning for Model Learning
Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam +1
Deep learning models are often deployed in downstream tasks that the training procedure may not be aware of. For example, models solely trained to achieve accurate predictions may…
-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception
Dhaivat Bhatt, Kaustubh Mani, Dishank Bansal +3
While modern deep neural networks are performant perception modules, performance (accuracy) alone is insufficient, particularly for safety-critical robotic applications such as sel…
Design, Analysis & Prototyping of a Semi-Automated Staircase-Climbing Rehabilitation Robot
Siddharth Jha, Himanshu Chaudhary, Swapnil Satardey +6
In this paper, we describe the mechanical design, system overview, integration and control techniques associated with SKALA, a unique large-sized robot for carrying a person with p…