most citedBi-Encoder Contrastive Learning for Fingerprint and Iris Biometrics

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Re-Evaluating Continual Learning with Few-Shot Adaptation

Amogh Inamdar, Matthew So, Vici Milenia +1

Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks. The standard measure of stability (i.e.,…

cs.CL2026

ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking

Xiao Yu, Ruize Xu, Chengyuan Xue +6

A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a list of job posts. While rece…

cs.LG2025

Exploring Human-AI Conceptual Alignment through the Prism of Chess

Semyon Lomasov, Judah Goldfeder, Mehmet Hamza Erol +5

Do AI systems truly understand human concepts or merely mimic surface patterns? We investigate this through chess, where human creativity meets precise strategic concepts. Analyzin…

cs.LG2025

Generating Auxiliary Tasks with Reinforcement Learning

Judah Goldfeder, Matthew So, Hod Lipson

Auxiliary Learning (AL) is a form of multi-task learning in which a model trains on auxiliary tasks to boost performance on a primary objective. While AL has improved generalizatio…

cs.CV2025★ 1 cited

Bi-Encoder Contrastive Learning for Fingerprint and Iris Biometrics

Matthew So, Judah Goldfeder, Mark Lis +1

There has been a historic assumption that the biometrics of an individual are statistically uncorrelated. We test this assumption by training Bi-Encoder networks on three verificat…