62 citations · 84 across the 6 of their papers we have counts for
4 papers · 1 filter
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Weili Nie, Zhiding Yu, Lei Mao +3
Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models e…
Shallow Univariate ReLu Networks as Splines: Initialization, Loss Surface, Hessian, & Gradient Flow Dynamics
Justin Sahs, Ryan Pyle, Aneel Damaraju +4
Understanding the learning dynamics and inductive bias of neural networks (NNs) is hindered by the opacity of the relationship between NN parameters and the function represented. W…
An Improved Semi-Supervised VAE for Learning Disentangled Representations
Weili Nie, Zichao Wang, Ankit B. Patel +1
Learning interpretable and disentangled representations is a crucial yet challenging task in representation learning. In this work, we focus on semi-supervised disentanglement lear…
Semi-Supervised StyleGAN for Disentanglement Learning
Weili Nie, Tero Karras, Animesh Garg +4
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: diffi…