Overview Modern machine learning models are often highly overparameterized. The prime examples of late are neural network architectures that can achieve state-of-the-art performance while having many more parameters than the number of training examples. Despite these developments, the

2 mentions: @yasamanbb@HanieSedghi
Keywords: icml
Date: 2021/05/04 03:18

Referring Tweets

@yasamanbb Consider submission to our ICML 2021 workshop: Overparameterization: Pitfalls & Opportunities t.co/bH1hgAq1Xj focused specifically on the role of overparameterization in machine learning. Organized by @HanieSedghi @QuanquanGu @aminkarbasi & myself. Deadline: June 21
@HanieSedghi We are excited to invite you to our #ICML2021 workshop: Overparametrization: Pitfalls & Opportunities t.co/oxORoZrMFj We have a great lineup of invited speakers & look forward to your submissions. Submission Deadline: June1st w/ @yasamanbb @aminkarbasi @QuanquanGu t.co/hYecZgdUxY

Related Entries

Read more Josh Tobin - Troubleshooting Deep Neural Networks - YouTube
2 users, 0 mentions 2019/07/07 08:18
Read more [1810.05148] Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processescontact a...
0 users, 1 mentions 2020/01/25 02:20
Read more GitHub - shunsukesaito/PIFu: This repository contains the code for the paper "PIFu: Pixel-Aligned Im...
3 users, 0 mentions 2020/02/27 03:50
Read more [2006.10540] Infinite attention: NNGP and NTK for deep attention networksopen searchopen navigation ...
0 users, 1 mentions 2020/07/22 18:52
Read more GitHub - ARISE-Initiative/robosuite: robosuite: A Modular Simulation Framework and Benchmark for Rob...
0 users, 1 mentions 2020/11/09 18:52