617 citations · 1.8k across the 34 of their papers we have counts for
54 papers
Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth
Filipe de Avila Belbute-Peres, J. Zico Kolter
Neural networks with sinusoidal activations have been proposed as an alternative to networks with traditional activation functions. Despite their promise, particularly for learning…
Characterizing Datapoints via Second-Split Forgetting
Pratyush Maini, Saurabh Garg, Zachary C. Lipton +1
Researchers investigating example hardness have increasingly focused on the dynamics by which neural networks learn and forget examples throughout training. Popular metrics derived…
Understanding the Covariance Structure of Convolutional Filters
Asher Trockman, Devin Willmott, J. Zico Kolter
Neural network weights are typically initialized at random from univariate distributions, controlling just the variance of individual weights even in highly-structured operations l…
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Ameya Joshi, Minh Pham, Minsu Cho +4
Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require aug…
Deep Equilibrium Optical Flow Estimation
Shaojie Bai, Zhengyang Geng, Yash Savani +1
Many recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms by encouraging iterative refinements toward a…
Patches Are All You Need?
Asher Trockman, J. Zico Kolter
Although convolutional networks have been the dominant architecture for vision tasks for many years, recent experiments have shown that Transformer-based models, most notably the V…