On the Origins of the Block Structure Phenomenon in Neural Network
Representations

On the Origins of the Block Structure Phenomenon in Neural Network Representations

15 February 2022

Thao Nguyen

Simon Kornblith

Papers citing "On the Origins of the Block Structure Phenomenon in Neural Network Representations"

19 / 19 papers shown

Title
Layers at Similar Depths Generate Similar Activations Across LLM Architectures Christopher Wolfram Aaron Schein 81 2 0 03 Apr 2025
Identifying Sub-networks in Neural Networks via Functionally Similar Representations Tian Gao Amit Dhurandhar Karthikeyan N. Ramamurthy Dennis L. Wei 70 0 0 21 Oct 2024
Similarity of Neural Network Models: A Survey of Functional and Representational Measures Max Klabunde Tobias Schumacher M. Strohmaier Florian Lemmerich 119 73 0 10 May 2023
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth Yihe Dong Jean-Baptiste Cordonnier Andreas Loukas 91 383 0 05 Mar 2021
What is being transferred in transfer learning? Behnam Neyshabur Hanie Sedghi Chiyuan Zhang 81 519 0 26 Aug 2020
Shortcut Learning in Deep Neural Networks Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 198 2,044 0 16 Apr 2020
Universality and individuality in neural dynamics across large populations of recurrent networks Niru Maheswaranathan Alex H. Williams Matthew D. Golub Surya Ganguli David Sussillo 59 145 0 19 Jul 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 159 743 0 19 Mar 2019
Transfusion: Understanding Transfer Learning for Medical Imaging M. Raghu Chiyuan Zhang Jon M. Kleinberg Samy Bengio MedIm 75 982 0 14 Feb 2019
Deep learning generalizes because the parameter-function map is biased towards simple functions Guillermo Valle Pérez Chico Q. Camargo A. Louis MLT AI4CE 75 231 0 22 May 2018
Semantic Adversarial Examples Hossein Hosseini Radha Poovendran GAN AAML 89 199 0 16 Mar 2018
To understand deep learning we need to understand kernel learning M. Belkin Siyuan Ma Soumik Mandal 55 418 0 05 Feb 2018
Detecting Cancer Metastases on Gigapixel Pathology Images Yun-Hui Liu Krishna Gadepalli Mohammad Norouzi George E. Dahl Timo Kohlberger ... Phil Q. Nelson G. Corrado J. Hipp L. Peng Martin C. Stumpe MedIm LM&MA 65 641 0 03 Mar 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 314 4,624 0 10 Nov 2016
Deep Learning for Identifying Metastatic Breast Cancer Dayong Wang A. Khosla Rishab Gargeya H. Irshad Andrew H. Beck MedIm 67 940 0 18 Jun 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 312 7,971 0 23 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 943 16,931 0 16 Feb 2016
Some Improvements on Deep Convolutional Neural Network Based Image Classification Andrew G. Howard VLM 130 433 0 19 Dec 2013
Algorithms for Learning Kernels Based on Centered Alignment Corinna Cortes M. Mohri Afshin Rostamizadeh 65 544 0 02 Mar 2012