2 citations · 7 across the 12 of their papers we have counts for
7 papers · 1 filter
Hidden Trajectories Don't Lie: Correcting Corruption Drift for Test-Time Adaptation
Amirhossein Sabour, Amir Khasahmadi, Mengliu Zhao
Test-time adaptation (TTA) adapts pretrained models to distribution shifts encountered during deployment. However, most existing methods rely on the weight representations of the f…
HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation
Nikita Klimenko, Hesam Salehipour, Parham Eftekhar +2
In this work, we propose HypergraphFormer, a novel and efficient approach to floor plan generation based on learning hypergraph representations with a large language model (LLM). T…
Majority of the Bests: Improving Best-of-N via Bootstrapping
Amin Rakhsha, Kanika Madan, Tianyu Zhang +2
Sampling multiple outputs from a Large Language Model (LLM) and selecting the most frequent (Self-consistency) or highest-scoring (Best-of-N) candidate is a popular approach to ach…
Aligning Constraint Generation with Design Intent in Parametric CAD
Evan Casey, Tianyu Zhang, Shu Ishida +6
We adapt alignment techniques from reasoning LLMs to the task of generating engineering sketch constraints found in computer-aided design (CAD) models. Engineering sketches consist…
e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration
Hyunmin Cheong, Mohammadmehdi Ataei, Amir Hosein Khasahmadi +1
Deep generative models have recently shown success in solving complex engineering design problems where models predict solutions that address the design requirements specified as i…
Group-disentangled Representation Learning with Weakly-Supervised Regularization
Linh Tran, Amir Hosein Khasahmadi, Aditya Sanghi +1
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning. We investigate…
Robust Representation Learning via Perceptual Similarity Metrics
Saeid Asgari Taghanaki, Kristy Choi, Amir Khasahmadi +1
A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream task, without overfitting to spurious inpu…