21 citations · 32 across the 12 of their papers we have counts for
13 papers
Teaching a Robot Dog New Tricks: Diverse Quadruped Skills via Combined Reinforcement and Imitation Learning with Adversarial Task Selection
Lemon Foxmere, Anthony Furman, Yizheng Du +3
Reinforcement Learning (RL) has enabled legged robots to perform a range of skills in single-task settings. However, applications such as farm robotics or space exploration require…
Open Vocabulary Compositional Explanations for Neuron Alignment
Biagio La Rosa, Leilani H. Gilpin
Neurons are the fundamental building blocks of deep neural networks, and their interconnections allow AI to achieve unprecedented results. Motivated by the goal of understanding ho…
Guaranteed Optimal Compositional Explanations for Neurons
Biagio La Rosa, Leilani H. Gilpin
Compositional explanations are a family of methods that aim to describe the spatial alignment between neurons' receptive field activations and concepts through logical rules, typic…
Follow My Lead: Logical Fallacy Classification with Knowledge-Augmented LLMs
Olivia Peiyu Wang, Tashvi Bansal, Ryan Bai +2
Large Language Models (LLMs) suffer from critical reasoning gaps, including a tendency to hallucinate and poor accuracy in classifying logical fallacies. This limitation stems from…
Explore the Loss space with Hill-ADAM
Meenakshi Manikandan, Leilani Gilpin
This paper introduces Hill-ADAM. Hill-ADAM is an optimizer with its focus towards escaping local minima in prescribed loss landscapes to find the global minimum. Hill-ADAM escapes…
VFSI: Validity First Spatial Intelligence for Constraint-Guided Traffic Diffusion
Kargi Chauhan, Leilani H. Gilpin
Modern diffusion models generate realistic traffic simulations but systematically violate physical constraints. In a large-scale evaluation of SceneDiffuser++, a state-of-the-art t…
Right this way: Can VLMs Guide Us to See More to Answer Questions?
Li Liu, Diji Yang, Sijia Zhong +4
In question-answering scenarios, humans can assess whether the available information is sufficient and seek additional information if necessary, rather than providing a forced answ…
Slug Mobile: Test-Bench for RL Testing
Jonathan Wellington Morris, Vishrut Shah, Alex Besanceney +2
Sim-to real gap in Reinforcement Learning is when a model trained in a simulator does not translate to the real world. This is a problem for Autonomous Vehicles (AVs) as vehicle dy…
Autonomous Driving with Spiking Neural Networks
Rui-Jie Zhu, Ziqing Wang, Leilani Gilpin +1
Autonomous driving demands an integrated approach that encompasses perception, prediction, and planning, all while operating under strict energy constraints to enhance scalability…
A novel post-hoc explanation comparison metric and applications
Shreyan Mitra, Leilani Gilpin
Explanatory systems make the behavior of machine learning models more transparent, but are often inconsistent. To quantify the differences between explanatory systems, this paper p…
Towards a fuller understanding of neurons with Clustered Compositional Explanations
Biagio La Rosa, Leilani H. Gilpin, Roberto Capobianco
Compositional Explanations is a method for identifying logical formulas of concepts that approximate the neurons' behavior. However, these explanations are linked to the small spec…
Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations
Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam +2
Large language models (LLMs) such as ChatGPT have demonstrated superior performance on a variety of natural language processing (NLP) tasks including sentiment analysis, mathematic…
Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification
Sharan Subramanian, Leilani H. Gilpin
The application of Artificial Intelligence in the medical market brings up increasing concerns but aids in more timely diagnosis of silent progressing diseases like Diabetic Retino…