Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks

10 February 2022

Papers citing "Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks"

23 / 23 papers shown

Title
Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees Gautam Chandrasekaran Adam R. Klivans Lin Lin Lee Konstantinos Stavropoulos OOD 40 0 0 22 Feb 2025
On the Hardness of Learning One Hidden Layer Neural Networks Shuchen Li Ilias Zadik Manolis Zampetakis 21 2 0 04 Oct 2024
Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks Judah Goldfeder Quinten Roets Gabe Guo John Wright Hod Lipson 33 1 0 27 Sep 2024
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization Holger Boche Vít Fojtík Adalbert Fono Gitta Kutyniok 35 0 0 12 Aug 2024
Learning Neural Networks with Sparse Activations Pranjal Awasthi Nishanth Dikkala Pritish Kamath Raghu Meka 36 2 0 26 Jun 2024
Hardness of Learning Neural Networks under the Manifold Hypothesis B. Kiani Jason Wang Melanie Weber 34 2 0 03 Jun 2024
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning Noah Golowich Ankur Moitra Dhruv Rohatgi OffRL 32 4 0 04 Apr 2024
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents Yatin Dandi Emanuele Troiani Luca Arnaboldi Luca Pesce Lenka Zdeborová Florent Krzakala MLT 61 25 0 05 Feb 2024
Looped Transformers are Better at Learning Learning Algorithms Liu Yang Kangwook Lee Robert D. Nowak Dimitris Papailiopoulos 24 24 0 21 Nov 2023
Polynomial-Time Solutions for ReLU Network Training: A Complexity Classification via Max-Cut and Zonotopes Yifei Wang Mert Pilanci 26 3 0 18 Nov 2023
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods C. Caramanis Dimitris Fotakis Alkis Kalavasis Vasilis Kontonis Christos Tzamos 11 5 0 08 Oct 2023
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials Ilias Diakonikolas D. Kane 24 4 0 24 Jul 2023
Most Neural Networks Are Almost Learnable Amit Daniely Nathan Srebro Gal Vardi 18 0 0 25 May 2023
Algorithmic Decorrelation and Planted Clique in Dependent Random Graphs: The Case of Extra Triangles Guy Bresler Chenghao Guo Yury Polyanskiy 34 1 0 17 May 2023
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy Amit Daniely Nathan Srebro Gal Vardi 12 4 0 15 Feb 2023
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 164 67 0 27 Oct 2022
Magnitude and Angle Dynamics in Training Single ReLU Neurons Sangmin Lee Byeongsu Sim Jong Chul Ye MLT 94 6 0 27 Sep 2022
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions Sitan Chen Sinho Chewi Jungshian Li Yuanzhi Li Adil Salim Anru R. Zhang DiffM 132 246 0 22 Sep 2022
Learning (Very) Simple Generative Models Is Hard Sitan Chen Jungshian Li Yuanzhi Li 17 9 0 31 May 2022
Learning ReLU networks to high uniform accuracy is intractable Julius Berner Philipp Grohs F. Voigtlaender 32 4 0 26 May 2022
$Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete$ Training Fully Connected Neural Networks is $\exists\mathbb{R}$ -Complete Daniel Bertschinger Christoph Hertrich Paul Jungeblut Tillmann Miltzow Simon Weber OffRL 54 30 0 04 Apr 2022
Size and Depth Separation in Approximating Benign Functions with Neural Networks Gal Vardi Daniel Reichman T. Pitassi Ohad Shamir 21 7 0 30 Jan 2021
From Local Pseudorandom Generators to Hardness of Learning Amit Daniely Gal Vardi 109 30 0 20 Jan 2021