100 citations · 135 across the 24 of their papers we have counts for
7 papers · 1 filter
RoleQ: Role-Aligned Scales for Converting Language Models to Ternary
Johannes Knittel, Alec Helbling, Hanspeter Pfister
Weight quantization below four bits shrinks memory and bandwidth at a perplexity that looks acceptable, but often at the cost of factual knowledge and task accuracy. Ternary weight…
ARMOR: Manifold-Oriented Training for Adversarially Robust Aerial Object Detection under Data Scarcity
Haoran Wang, Matthew Lau, Alec Helbling +7
Aerial object detection is increasingly deployed in real-world applications, but models remain vulnerable to physical, universal adversarial patches that cause them to miss objects…
ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments
Mansi Phute, Alexander Greenhalgh, Matthew Hull +8
Adversarial attacks against vision models like object detectors are often evaluated under limited conditions, leaving their performance under-characterized. Bridging simulation and…
Flow Reasoning Models: Turning Flows Into Efficient Recurrent Reasoners
Alec Helbling, Andrey Bryutkin, Mauro Martino +3
Structured reasoning requires making and revising interdependent decisions to reach a globally consistent solution. Existing architectures struggle with this: autoregressive models…
What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching
Alec Helbling, Sebastian Gutierrez Hernandez, Benjamin Hoover +2
Recent work has shown that models flow matching models can be trained without explicit time conditioning, challenging the standard view that the interpolation time is needed to dis…
Beyond a Single Frame: Multi-Frame Spatially Grounded Reasoning Across Volumetric MRI
Lama Moukheiber, Caleb M. Yeung, Haotian Xue +3
Spatial reasoning and visual grounding are core capabilities for vision-language models (VLMs), yet most medical VLMs produce predictions without transparent reasoning or spatial e…
LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data
Vivek Anand, Alec Helbling, Mark A. Davenport +3
Learning the intrinsic dimensionality of subjective perceptual spaces such as taste, smell, or aesthetics from ordinal data is a challenging problem. We introduce LORE (Low Rank Or…