1 citations · 1 across the 16 of their papers we have counts for
6 papers · 1 filter
Probing the 3D Object-Level Understanding of Pre-Trained Detection Transformers
Robin Kim, Colin Samplawski, Benjamin M. Marlin
Detection transformer models, including DETR and its extensions, learn to output a set of object-level embeddings that can be simultaneously decoded into 2D bounding boxes and clas…
Bayesian Adaptation Gym: A Benchmark for the Bayesian Low-Rank Adaptation of Multi-Modal Language Models
Colin Samplawski, Ramneet Kaur, Manoj Acharya +2
Large multi-modal language models are increasingly deployed in high-stakes domains, making well-calibrated uncertainty essential. Traditional Bayesian methods approximate posterior…
Formally Verified Code Synthesis for Structured Data Translation in a Medical Internet of Things
Colin Samplawski, Adam D. Cobb
In this work we present a LLM powered, evolutionary code synthesis system for structured data translation in a Medical Internet of Things settings. A key challenge in this domain i…
Breaking Bad: Interpretability-Based Safety Audits of State-of-the-Art LLMs
Krishiv Agarwal, Ramneet Kaur, Colin Samplawski +6
Effective safety auditing of large language models (LLMs) demands tools that go beyond black-box probing and systematically uncover vulnerabilities rooted in model internals. We pr…
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
Trilok Padhi, Ramneet Kaur, Krishiv Agarwal +9
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capabi…
Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design
Aurelien Ghiglino, Daniel Elenius, Anirban Roy +7
In this paper, we generate conceptual engineering designs of electric vertical take-off and landing (eVTOL) aircraft. We follow the paradigm of simulation-based inference (SBI), wh…