Reconciling modern machine learning practice and the bias-variance trade-off

28 December 2018

Papers citing "Reconciling modern machine learning practice and the bias-variance trade-off"

50 / 315 papers shown

Title
Symmetry Teleportation for Accelerated Optimization B. Zhao Nima Dehmamy Robin Walters Rose Yu ODL 23 20 0 21 May 2022
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization C. Linse T. Martinetz SSL VLM 14 4 0 18 May 2022
Deep learning of quantum entanglement from incomplete measurements Dominik Koutný L. Ginés M. Moczała-Dusanowska Sven Höfling Christian Schneider Ana Predojevic M. Ježek 24 28 0 03 May 2022
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation Jimmy Ba Murat A. Erdogdu Taiji Suzuki Zhichao Wang Denny Wu Greg Yang MLT 42 121 0 03 May 2022
Benign Overfitting in Time Series Linear Models with Over-Parameterization Shogo H. Nakakita Masaaki Imaizumi AI4TS 27 5 0 18 Apr 2022
Concentration of Random Feature Matrices in High-Dimensions Zhijun Chen Hayden Schaeffer Rachel A. Ward 22 6 0 14 Apr 2022
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets Johannes Gasteiger Muhammed Shuaibi Anuroop Sriram Stephan Günnemann Zachary W. Ulissi C. L. Zitnick Abhishek Das AI4TS MLAU 39 66 0 06 Apr 2022
Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves David Bosch Ashkan Panahi Ayça Özçelikkale Devdatt Dubhash MLT 24 2 0 06 Apr 2022
Random matrix analysis of deep neural network weight matrices M. Thamm Max Staats B. Rosenow 35 12 0 28 Mar 2022
The Mathematics of Artificial Intelligence Gitta Kutyniok 19 0 0 16 Mar 2022
Vision-Based Manipulators Need to Also See from Their Hands Kyle Hsu Moo Jin Kim Rafael Rafailov Jiajun Wu Chelsea Finn 34 44 0 15 Mar 2022
Generalization Through The Lens Of Leave-One-Out Error Gregor Bachmann Thomas Hofmann Aurelien Lucchi 55 7 0 07 Mar 2022
Estimation under Model Misspecification with Fake Features Martin Hellkvist Ayça Özçelikkale Anders Ahlén 27 11 0 07 Mar 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong Cengiz Pehlevan BDL MLT 28 26 0 01 Mar 2022
Amortized Proximal Optimization Juhan Bae Paul Vicol Jeff Z. HaoChen Roger C. Grosse ODL 27 14 0 28 Feb 2022
Robust Training under Label Noise by Over-parameterization Sheng Liu Zhihui Zhu Qing Qu Chong You NoLa OOD 27 106 0 28 Feb 2022
Benefit of Interpolation in Nearest Neighbor Algorithms Yue Xing Qifan Song Guang Cheng 14 28 0 23 Feb 2022
Overparametrization improves robustness against adversarial attacks: A replication study Ali Borji AAML 19 1 0 20 Feb 2022
Deep Ensembles Work, But Are They Necessary? Taiga Abe E. Kelly Buchanan Geoff Pleiss R. Zemel John P. Cunningham OOD UQCV 44 60 0 14 Feb 2022
HARFE: Hard-Ridge Random Feature Expansion Esha Saha Hayden Schaeffer Giang Tran 38 14 0 06 Feb 2022
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions Aaron Mishkin Arda Sahiner Mert Pilanci OffRL 77 30 0 02 Feb 2022
Interplay between depth of neural networks and locality of target functions Takashi Mori Masakuni Ueda 25 0 0 28 Jan 2022
To what extent should we trust AI models when they extrapolate? Roozbeh Yousefzadeh Xuenan Cao 27 5 0 27 Jan 2022
On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations M. Virgolin Saverio Fracaros CML 26 36 0 22 Jan 2022
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape Devansh Bisla Jing Wang A. Choromańska 25 34 0 20 Jan 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 26 1,180 0 14 Jan 2022
Benign Overfitting in Adversarially Robust Linear Classification Jinghui Chen Yuan Cao Quanquan Gu AAML SILM 34 10 0 31 Dec 2021
Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition Sofia Broomé Ernest Pokropek Boyu Li Hedvig Kjellström 21 7 0 22 Dec 2021
Approximation of functions with one-bit neural networks C. S. Güntürk Weilin Li 19 8 0 16 Dec 2021
SCORE: Approximating Curvature Information under Self-Concordant Regularization Adeyemi Damilare Adeoye Alberto Bemporad 20 4 0 14 Dec 2021
Multi-scale Feature Learning Dynamics: Insights for Double Descent Mohammad Pezeshki Amartya Mitra Yoshua Bengio Guillaume Lajoie 61 25 0 06 Dec 2021
On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons Fangshuo Liao Anastasios Kyrillidis 43 16 0 05 Dec 2021
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models Tri Dao Beidi Chen Kaizhao Liang Jiaming Yang Zhao Song Atri Rudra Christopher Ré 33 75 0 30 Nov 2021
Tight bounds for minimum l1-norm interpolation of noisy data Guillaume Wang Konstantin Donhauser Fanny Yang 79 20 0 10 Nov 2021
There is no Double-Descent in Random Forests Sebastian Buschjäger K. Morik 17 8 0 08 Nov 2021
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks A. Shevchenko Vyacheslav Kungurtsev Marco Mondelli MLT 41 13 0 03 Nov 2021
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model A. Bodin N. Macris 37 13 0 22 Oct 2021
Conditioning of Random Feature Matrices: Double Descent and Generalization Error Zhijun Chen Hayden Schaeffer 35 12 0 21 Oct 2021
Behavioral Experiments for Understanding Catastrophic Forgetting Samuel J. Bell Neil D. Lawrence 35 4 0 20 Oct 2021
A-Optimal Active Learning Tue Boesen E. Haber 29 0 0 18 Oct 2021
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks R. Entezari Hanie Sedghi O. Saukh Behnam Neyshabur MoMe 39 217 0 12 Oct 2021
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting Chengyu Dong Liyuan Liu Jingbo Shang NoLa AAML 56 18 0 07 Oct 2021
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications Ziqiao Wang Yongyi Mao FedML MLT 37 22 0 07 Oct 2021
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 119 356 0 04 Oct 2021
Learning through atypical "phase transitions" in overparameterized neural networks Carlo Baldassi Clarissa Lauditi Enrico M. Malatesta R. Pacelli Gabriele Perugini R. Zecchina 26 26 0 01 Oct 2021
Powerpropagation: A sparsity inducing weight reparameterisation Jonathan Richard Schwarz Siddhant M. Jayakumar Razvan Pascanu P. Latham Yee Whye Teh 92 54 0 01 Oct 2021
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective Adhyyan Narang Vidya Muthukumar A. Sahai SILM AAML 36 1 0 27 Sep 2021
Is the Number of Trainable Parameters All That Actually Matters? A. Chatelain Amine Djeghri Daniel Hesslow Julien Launay Iacopo Poli 51 7 0 24 Sep 2021
Learning the hypotheses space from data through a U-curve algorithm Diego Marcondes Adilson Simonis Junior Barrera 19 1 0 08 Sep 2021
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning Yehuda Dar Vidya Muthukumar Richard G. Baraniuk 34 71 0 06 Sep 2021