20 citations · 34 across the 6 of their papers we have counts for
6 papers
Representation Learning for Sequential Volumetric Design Tasks
Md Ferdous Alam, Yi Wang, Chin-Yi Cheng +1
Volumetric design, also called massing design, is the first and critical step in professional building design which is sequential in nature. As the volumetric design process requir…
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani +4
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting…
SimCURL: Simple Contrastive User Representation Learning from Command Sequences
Hang Chu, Amir Hosein Khasahmadi, Karl D. D. Willis +5
User modeling is crucial to understanding user behavior and essential for improving user experience and personalized recommendations. When users interact with software, vast amount…
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Saeid Asgari Taghanaki, Ali Gholami, Fereshte Khani +4
Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy. However, we demonstrate that BN co…
COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions
Peter J Bentley, Soo Ling Lim, Adam Gaier +1
Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To addre…
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Karl D. D. Willis, Pradeep Kumar Jayaraman, Hang Chu +8
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software. CAD designers build up these assemblies by alignin…