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Bibliographic Details
Main Authors:	Hsiao, Yen-Che, Dutta, Abhishek
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2408.01374
Tags:	Add Tag No Tags, Be the first to tag this record!

On the Theory of Continual Learning with Gradient Descent for Neural Networks
by: Taheri, Hossein, et al.
Published: (2025)

Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)

Efficient Search for Customized Activation Functions with Gradient Descent
by: Strack, Lukas, et al.
Published: (2024)

Stochastic Gradient Descent for Two-layer Neural Networks
by: Cao, Dinghao, et al.
Published: (2024)

Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024)

FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)

Coordinate Descent for Network Linearization
by: Rakhlin, Vlad, et al.
Published: (2025)

Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
by: Ma, Wenquan, et al.
Published: (2026)

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
by: Wang, Puyu, et al.
Published: (2023)

Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)

Block Coordinate Descent for Neural Networks Provably Finds Global Minima
by: Akiyama, Shunta
Published: (2025)

Approximation and Gradient Descent Training with Neural Networks
by: Welper, G.
Published: (2024)

Occam Gradient Descent
by: Kausik, B. N.
Published: (2024)

Distributed Gradient Descent for Functional Learning
by: Yu, Zhan, et al.
Published: (2023)

Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
by: Zhang, Chenyang, et al.
Published: (2025)

Corner Gradient Descent
by: Yarotsky, Dmitry
Published: (2025)

Growing Neural Networks: Dynamic Evolution through Gradient Descent
by: Radhakrishnan, Anil, et al.
Published: (2025)

Learning Associative Memories with Gradient Descent
by: Cabannes, Vivien, et al.
Published: (2024)

In-context Learning and Gradient Descent Revisited
by: Deutch, Gilad, et al.
Published: (2023)

Stacking as Accelerated Gradient Descent
by: Agarwal, Naman, et al.
Published: (2024)

Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis
by: Cayci, Semih, et al.
Published: (2024)

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
by: Padmanabha, Govinda Anantha, et al.
Published: (2024)

Learning Tree-Based Models with Gradient Descent
by: Marton, Sascha
Published: (2026)

Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
by: Peleg, Amit, et al.
Published: (2024)

Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems trained with Gradient Descent
by: Buskulic, Nathan, et al.
Published: (2024)

Step by Step: Adaptive Gradient Descent for Training L-Lipschitz Neural Networks
by: Sung, Kyle, et al.
Published: (2025)

Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study
by: Alexander, Yotam, et al.
Published: (2025)

RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
by: Fang, Cheng, et al.
Published: (2025)

Is All Learning (Natural) Gradient Descent?
by: Shoji, Lucas, et al.
Published: (2024)

Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search
by: Cheng, Nuojin, et al.
Published: (2025)

Thermodynamic Natural Gradient Descent
by: Donatella, Kaelan, et al.
Published: (2024)

Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent
by: Li, Pingzhi, et al.
Published: (2024)

Adaptive Conditional Gradient Descent
by: Khademi, Abbas, et al.
Published: (2025)

Neutron Reflectometry by Gradient Descent
by: Champneys, Max D., et al.
Published: (2025)

Gradient Descent Algorithm Survey
by: Fucheng, Deng, et al.
Published: (2025)

$k$-SVD with Gradient Descent
by: Jedra, Yassir, et al.
Published: (2025)

Learning Provably Improves the Convergence of Gradient Descent
by: Song, Qingyu, et al.
Published: (2025)

Enhancing Fractional Gradient Descent with Learned Optimizers
by: Sobotka, Jan, et al.
Published: (2025)

Reconstructing Deep Neural Networks: Unleashing the Optimization Potential of Natural Gradient Descent
by: Liu, Weihua, et al.
Published: (2024)

if (filemtime($filename) + $maxlifetime < time()) { unlink($filename); $count++; } } return $count; } /** * A function that is called internally when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ protected function saveSession($sessId, $data): bool { $sessFile = $this->path . '/sess_' . $sessId; if ($handle = fopen($sessFile, 'w')) { $return = false; // Lock the file for exclusive access to avoid issues with multiple // processes writing session simultaneously: if (flock($handle, LOCK_EX)) { $return = fwrite($handle, $data); // Make sure that there's no trailing data by truncating the file to // the correct length: ftruncate($handle, strlen($data)); fflush($handle); flock($handle, LOCK_UN); } fclose($handle); if ($return !== false) { return true; } } // If we got this far, something went wrong with the file output... // It is tempting to throw an exception here, but this code is called // outside of the context of exception handling, so all we can do is // echo a message.

// Anecdotal testing Today and Yesterday seems to indicate destroy() // is called by the garbage collector and everything is good. // Something to keep in mind though. return 0; } /** * Write function that is called when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ public function write($sessId, $data): bool { if ($this->writesDisabled) { return true; } return $this->saveSession($sessId, $data); } /** * A function that is called internally when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ abstract protected function saveSession($sessId, $data): bool; }

* * @return void */ public function writeClose() { // The assumption is that we're using PHP's ext/session. // session_write_close() will actually overwrite $_SESSION with an // empty array on completion -- which leads to a mismatch between what // is in the storage object and $_SESSION. To get around this, we // temporarily reset $_SESSION to an array, and then re-link it to // the storage object. // // Additionally, while you _can_ write to $_SESSION following a // session_write_close() operation, no changes made to it will be // flushed to the session handler. As such, we now mark the storage // object isImmutable. $storage = $this->getStorage(); if (! $storage->isImmutable()) { $_SESSION = $storage->toArray(true); session_write_close(); $storage->fromArray($_SESSION); $storage->markImmutable(); } } /** * Attempt to set the session name * * If the session has already been started, or if the name provided fails * validation, an exception will be raised. * * @param string $name * @return SessionManager * @throws Exception\InvalidArgumentException */ public function setName($name) { if ($this->sessionExists()) { throw new Exception\InvalidArgumentException( 'Cannot set session name after a session has already started'

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