Results 81 to 90 of about 118,488 (274)

Predicting Computer Engineering students' dropout in Cuban Higher Education with pre-enrollment and early performance data

Journal of Technology and Science Education, 2020
We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their ...
Niurys Lázaro Alvarez, Zoraida Callejas, David Griol   +2 more
doaj   +1 more source

The geometry of representational drift in natural and artificial neural networks.

PLoS Computational Biology, 2022
Neurons in sensory areas encode/represent stimuli. Surprisingly, recent studies have suggested that, even during persistent performance, these representations are not stable and change over the course of days and weeks.
Kyle Aitken, Marina Garrett, Shawn Olsen, Stefan Mihalas   +3 more
doaj   +1 more source

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

Advanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu, Wanpeng Zou, Yuekai Li, Jiayi Wang, Mingxuan Cai, Hongmin Cai   +5 more
wiley   +1 more source

Incremental Dilations Using CNN for Brain Tumor Classification

Applied Sciences, 2020
Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis.
Sanjiban Sekhar Roy, Nishant Rodrigues, Y-h. Taguchi   +2 more
doaj   +1 more source

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

, 2015
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common ...
Gal, Y, Ghahramani, Z
openaire   +4 more sources

Fabrication of High‐Density Multimodal Neural Probes Based on Heterogeneously Integrated CMOS

Advanced Science, EarlyView.
A chiplet‐based methodology democratizes active neural probe development on standard bulk CMOS services. This yields the first probe combining high‐density electrophysiology (416 electrodes) with calcium imaging (832 photodiodes) and complete on‐chip signal processing across 13 shanks.
Ju Hee Mun, Miji Kim, Wooyeon Shin, Yongjun Park, Kanghwan Kim, Il‐Joo Cho, Jae Won Shim, Min Soo Kim, Sunwoo Lee, Jeongjin Kim, Changhyuk Lee   +10 more
wiley   +1 more source

DropKAN: Dropout Kolmogorov–Arnold Networks

IEEE Access
We propose DropKAN (Dropout Kolmogorov—Arnold Networks), a regularization method that introduces dropout masks at the edge level within Kolmogorov—Arnold Networks (KANs) layers, randomly masking a subset of activation outputs in the ...
Mohammed Ghaith Altarabichi
doaj   +1 more source

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion

Advanced Science, EarlyView.
ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong, Ji Ma, Yidan Lu, Jiahui Zhang, Wanyue Li, Yeke Chen, Teng Zhang, Xuechao Chen, Zhangguo Yu, Peng Lu   +9 more
wiley   +1 more source

PAIR: Reconstructing Single‐Cell Open‐Chromatin Landscapes for Transcription Factor Regulome Mapping

Advanced Science, EarlyView.
scATAC‐seq analysis is often constrained by limited sequencing depth, extreme sparsity, and pervasive technical missingness. PAIR is a probabilistic framework that restores scATAC‐seq accessibility profiles by directly modeling the native cell–peak bipartite structure of chromatin accessibility.
Yanchi Su, Qi Qi, Yi Fan, Yubo Wang, Gaoyang Hao, Ka‐Chun Wong, Yunhe Wang, Xiangtao Li   +7 more
wiley   +1 more source

Predictive Models for Imbalanced Data: A School Dropout Perspective

Education Sciences, 2019
Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets.
Thiago M. Barros, Plácido A. Souza Neto, Ivanovitch Silva, Luiz Affonso Guedes   +3 more
doaj   +1 more source