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17 papers · ranked by Valyu relevance

01
PPubMed10 Jan 2022
Mini-batch optimization enables training of ODE models on large-scale datasets
Paul Stapor, Leonard Schmiester, Christoph Wierling, Simon Merkt + 4 more
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become…
92relevance
02
BbioRxiv25 Jul 2022
A calibration framework to improve mechanistic forecasts with hybrid dynamic models
Victor Boussange, Pau Vilimelis Aceituno, Frank Schäfer, Loïc Pellissier
Process-based, dynamic models are essential for extrapolating beyond current trends and anticipating biodiversity responses to global change. However, their practical adoption for forecasting purposes remains limited due to difficulties in calibrating them against data and structural inaccuracies in their mathematical…
82relevance
03
AarXiv13 Sep 2024
Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent
Hideyuki Umeda, Hideaki Iiduka
Gradient Descent Authors: ['Hideyuki Umeda' 'Hideaki Iiduka'] The performance of mini-batch stochastic gradient descent (SGD) strongly depends on setting the batch size and learning rate to minimize the empirical loss in training the deep neural network. In this paper, we present theoretical analyses of mini-batch SGD…
82relevance
04
AarXiv2023
Mini-Batch Optimization of Contrastive Loss
Jaewoong Cho, Kartik K. Sreenivasan, Keon Lee, Kyunghoo Mun + 6 more
'Soheun Yi' 'Jeong-Gwan Lee' 'Anna Lee' 'Jy-yong Sohn' 'Dimitris Papailiopoulos' 'Kangwook Lee'] | Jaewoong Cho∗ | Kartik Sreenivasan∗ | | Keon Lee | | --- | --- | --- | --- | | KRAFTON | University of Wisconsin-Madison | | KRAFTON | | Kyunghoo Mun | Soheun Yi | Jeong-Gwan Lee | Anna Lee | | KRAFTON | Seoul National…
81relevance
05
AarXiv5 Jun 2024
Discrete error dynamics of mini-batch gradient descent for least squares regression
Jackie Lok, Rishi Sonthalia, Elizaveta Rebrova
regression Authors: ['Jackie Lok' 'Rishi Sonthalia' 'Elizaveta Rebrova'] We study the discrete dynamics of mini-batch gradient descent for least squares regression when sampling without replacement. We show that the dynamics and generalization error of mini-batch gradient descent depends on a sample cross-covariance…
80relevance
06
AarXiv2023
Can we learn better with hard samples?
Subin Sahayam, John Zakkam, Umarani Jayaraman
In deep learning, mini-batch training is commonly used to optimize network parameters. However, the traditional mini-batch method may not learn the under-represented samples and complex patterns in the data, leading to a longer time for generalization. To address this problem, a variant of the traditional algorithm has…
80relevance
07
PPubMed2 Feb 2026
Uncovering Latent Structures: A Bayesian Approach to Estimating Q-Matrix and Attribute Hierarchies in Cognitive Diagnostic Models
Xue Wang, Yinghan Chen, Shiyu Wang
Cognitive diagnostic models (CDMs) provide fine-grained diagnostic feedback by modeling the relationship between latent attributes and item responses. Two key components required for CDM implementation are the Q-matrix, which links items to attributes, and the attribute hierarchy, which defines prerequisite…
80relevance
08
PPubMed2 Nov 2023
Towards accelerating model parallelism in distributed deep learning systems Towards accelerating model parallelism in distributed deep learning systems
Hyeonseong Choi, Byung Hyun Lee, Se Young Chun, Jaehwan Lee + 1 more
'Elena Loli Piccolomini'] Modern deep neural networks cannot be often trained on a single GPU due to large model size and large data size. Model parallelism splits a model for multiple GPUs, but making it scalable and seamless is challenging due to different information sharing among GPUs with communication overhead.…
73relevance
09
PPubMed14 Jan 2025
<i> VFL-Cafe </i> : Communication-Efficient Vertical Federated Learning via Dynamic Caching and Feature Selection
Jiahui Zhou, Han Liang, Tian Wu, Xiaoxi Zhang + 3 more
'Chee Wei Tan' 'Jun Chen'] Vertical Federated Learning (VFL) is a promising category of Federated Learning that enables collaborative model training among distributed parties with data privacy protection. Due to its unique training architecture, a key challenge of VFL is high communication cost due to transmitting…
72relevance
10
CchemRxiv3 Oct 2023
FAME.AL: Site-of-metabolism prediction with active learning
Ya Chen, Thomas Seidel, Roxane Axel Jacob, Steffen Hirte + 6 more
The ability to determine and predict metabolically labile atom positions in a molecule (also called “sites of metabolism” or “SoMs”) is of high interest to the design and optimization of bioactive compounds such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have…
71relevance
11
PPubMed5 Apr 2024
Advanced disk herniation computer aided diagnosis system
Maad Ebrahim, Mohammad Alsmirat, Mahmoud Al-Ayyoub
Over recent years, researchers and practitioners have encountered massive and continuous improvements in the computational resources available for their use. This allowed the use of resource-hungry Machine learning (ML) algorithms to become feasible and practical. Moreover, several advanced techniques are being used to…
69relevance
12
BbioRxiv29 Jul 2023
Deep Batch Active Learning for Drug Discovery
Michael Bailey, Saeed Moayedpour, Ruijiang Li, Alejandro Corrochano-Navarro + 10 more
A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are selected for testing based on their likelihood of improving model performance. To…
68relevance
13
BbioRxiv18 Sep 2023
Using recurrent neural networks to detect supernumerary chromosomes in fungal strains causing blast diseases
Nikesh Gyawali, Yangfan Hao, Guifang Lin, Jun Huang + 8 more
The genomes of the fungus Magnaporthe oryzae that causes blast diseases on diverse grass species, including major crop plants, have indispensable core-chromosomes and may contain one or more additional supernumerary chromosomes, also known as mini-chromosomes. The mini-chromosome is speculated to play a role in fungal…
65relevance
14
BbioRxiv3 Jul 2025
Optimizing automated sleep stage scoring of 5-second mini-epochs: a transfer learning study
Louise Frøstrup Follin, Julie Anja Engelhard Christensen, Janita Vevelstad, Hilde T. Juvodden + 6 more
Conventional sleep staging relies on 30-second epochs, potentially concealing transient sleep stage intrusion and reducing precision. Building on our previous study of mini-epochs, we investigated whether U-Sleep, an existing automatic deep learning-based sleep staging model with high performance in epochs, could be…
64relevance
15
CchemRxiv10 Jul 2025
LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields
Authors not listed
Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…
57relevance
16
BbioRxiv21 Oct 2021
A Biologically Plausible Model for Continual Learning using Synaptic Weight Attractors
Romik Ghosh, Dana Mastrovito, Stefan Mihalas
The human brain readily learns tasks in sequence without forgetting previous ones. Artificial neural networks (ANNs), on the other hand, need to be modified to achieve similar performance. While effective, many algorithms that accomplish this are based on weight importance methods that do not correspond to biological…
53relevance
17
CchemRxiv10 Jan 2022
The Effect of Chemical Representation on Active Machine Learning Towards Closed-Loop Optimization
Alexander Pomberger, Antonio Pedrina McCarthy, Ahmad Khan, Simon Sung + 4 more
Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices. Closed-loop optimization featuring active Machine Learning (ML) represents a powerful strategy for automating reaction optimization. However, the translation…
51relevance

Results for mini-batch learning

Mini-batch optimization enables training of ODE models on large-scale datasets

A calibration framework to improve mechanistic forecasts with hybrid dynamic models

Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent

Mini-Batch Optimization of Contrastive Loss

Discrete error dynamics of mini-batch gradient descent for least squares regression

Can we learn better with hard samples?

Uncovering Latent Structures: A Bayesian Approach to Estimating Q-Matrix and Attribute Hierarchies in Cognitive Diagnostic Models

Towards accelerating model parallelism in distributed deep learning systems Towards accelerating model parallelism in distributed deep learning systems

<i> VFL-Cafe </i> : Communication-Efficient Vertical Federated Learning via Dynamic Caching and Feature Selection

FAME.AL: Site-of-metabolism prediction with active learning

Advanced disk herniation computer aided diagnosis system

Deep Batch Active Learning for Drug Discovery

Using recurrent neural networks to detect supernumerary chromosomes in fungal strains causing blast diseases

Optimizing automated sleep stage scoring of 5-second mini-epochs: a transfer learning study

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

A Biologically Plausible Model for Continual Learning using Synaptic Weight Attractors

The Effect of Chemical Representation on Active Machine Learning Towards Closed-Loop Optimization

Mini-batch optimization enables training of ODE models on large-scale datasets

A calibration framework to improve mechanistic forecasts with hybrid dynamic models

Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent

Mini-Batch Optimization of Contrastive Loss

Discrete error dynamics of mini-batch gradient descent for least squares regression

Can we learn better with hard samples?

Uncovering Latent Structures: A Bayesian Approach to Estimating Q-Matrix and Attribute Hierarchies in Cognitive Diagnostic Models

Towards accelerating model parallelism in distributed deep learning systems Towards accelerating model parallelism in distributed deep learning systems

<i> VFL-Cafe </i> : Communication-Efficient Vertical Federated Learning via Dynamic Caching and Feature Selection

FAME.AL: Site-of-metabolism prediction with active learning

Advanced disk herniation computer aided diagnosis system

Deep Batch Active Learning for Drug Discovery

Using recurrent neural networks to detect supernumerary chromosomes in fungal strains causing blast diseases

Optimizing automated sleep stage scoring of 5-second mini-epochs: a transfer learning study

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

A Biologically Plausible Model for Continual Learning using Synaptic Weight Attractors

The Effect of Chemical Representation on Active Machine Learning Towards Closed-Loop Optimization