11 citations · 13 across the 10 of their papers we have counts for
10 papers
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…
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…
Spatio-Temporal Pruning for Compressed Spiking Large Language Models
Yi Jiang, Malyaban Bal, Brian Matejek +3
Large Language Models (LLMs) present significant challenges for deployment in energy-constrained environments due to their large model sizes and high inference latency. Spiking Neu…
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding
Trilok Padhi, Ramneet Kaur, Adam D. Cobb +7
We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for multi-modal large language models (LLMs). Existing state-of-the-art UQ methods rely on co…
Melanoma Detection with Uncertainty Quantification
SangHyuk Kim, Edward Gaibor, Brian Matejek +1
Early detection of melanoma is crucial for improving survival rates. Current detection tools often utilize data-driven machine learning methods but often overlook the full integrat…
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI
Ramneet Kaur, Colin Samplawski, Adam D. Cobb +8
In this paper, we present a dynamic semantic clustering approach inspired by the Chinese Restaurant Process, aimed at addressing uncertainty in the inference of Large Language Mode…
Resource-Constrained Heuristic for Max-SAT
Brian Matejek, Daniel Elenius, Cale Gentry +2
We propose a resource-constrained heuristic for instances of Max-SAT that iteratively decomposes a larger problem into smaller subcomponents that can be solved by optimized solvers…
Direct Amortized Likelihood Ratio Estimation
Adam D. Cobb, Brian Matejek, Daniel Elenius +2
We introduce a new amortized likelihood ratio estimator for likelihood-free simulation-based inference (SBI). Our estimator is simple to train and estimates the likelihood ratio us…
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
Donglai Wei, Kisuk Lee, Hanyu Li +13
Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. However, due t…
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Toufiq Parag, Fabian Tschopp, William Grisaitis +6
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed…