Results 31 to 40 of about 195,059 (253)

Perceptual learning via modification of cortical top-down signals. [PDF]

PLoS Computational Biology, 2007
The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background ...
Roland Schäfer, Eleni Vasilaki, Walter Senn   +2 more
doaj   +1 more source

Learning with Instance-Dependent Label Noise: A Sample Sieve Approach

CoRR, 2020
Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literature (with several recent exceptions) of learning with noisy labels focuses on the case when the label noise is independent of features. Practically, annotations errors tend to
Hao Cheng 0012, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun 0001, Yang Liu 0018   +5 more
openaire   +3 more sources

Feature selection for speech emotion recognition in Spanish and Basque: on the use of machine learning to improve human-computer interaction. [PDF]

PLoS ONE, 2014
Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection ...
Andoni Arruti, Idoia Cearreta, Aitor Alvarez, Elena Lazkano, Basilio Sierra   +4 more
doaj   +1 more source

Learning efficient representations of environmental priors in working memory.

PLoS Computational Biology, 2023
Experience shapes our expectations and helps us learn the structure of the environment. Inference models render such learning as a gradual refinement of the observer's estimate of the environmental prior.
Tahra L Eissa, Zachary P Kilpatrick
doaj   +1 more source

Instance-Dependent PU Learning by Bayesian Optimal Relabeling

CoRR, 2018
When learning from positive and unlabelled data, it is a strong assumption that the positive observations are randomly sampled from the distribution of $X$ conditional on $Y = 1$, where X stands for the feature and Y the label. Most existing algorithms are optimally designed under the assumption.
Fengxiang He, Tongliang Liu, Geoffrey I. Webb, Dacheng Tao   +3 more
openaire   +2 more sources

A Learning-Based Optimal Decision Scenario for an Inventory Problem under a Price Discount Policy

Systems, 2023
This paper aims to design an inventory model for a retail enterprise with a profit maximization objective using the opportunity for a price discount facility given by a supplier. In the profit maximization objective, the demand should be increased.
Alaa Fouad Momena, Mostafijur Rahaman, Rakibul Haque, Shariful Alam, Sankar Prasad Mondal   +4 more
doaj   +1 more source

Federated Learning with Instance-Dependent Noisy Label

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Accepted by ICASSP ...
Lei Wang 0199, Jieming Bian, Jie Xu 0001
openaire   +2 more sources

On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Sample-efficient offline reinforcement learning (RL) with linear function approximation has been studied extensively recently. Much of the prior work has yielded instance-independent rates that hold even for the worst-case realization of problem instances.
Thanh Nguyen-Tang, Ming Yin, Sunil Gupta 0001, Svetha Venkatesh, Raman Arora   +4 more
openaire   +2 more sources

Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Vision Transformers (ViT) have shown their competitive advantages performance-wise compared to convolutional neural networks (CNNs) though they often come with high computational costs. To this end, previous methods explore different attention patterns by limiting a fixed number of spatially nearby tokens to accelerate the ViT's multi-head self ...
Cong Wei 0001, Brendan Duke, Ruowei Jiang, Parham Aarabi, Graham W. Taylor, Florian Shkurti   +5 more
openaire   +2 more sources

Guidelines for Pediatric Radiotherapy Simulation: A Report From the Children's Oncology Group Radiation Oncology Discipline

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Pediatric radiation therapy presents unique challenges compared to adult treatments, including those of immobilization, potential need for sedation, and the critical importance of accurate, reproducible positioning. Additionally, heightened attention to imaging doses is necessary to minimize long‐term toxicity in survivors.
Parham Alaei, Stephen Mihalcik, Sujith Baliga, Young Kwok, Dana Casey, Eric Ehler, Christiane Burton, John C. Breneman, Daphne A. Haas‐Kogan, Suzanne L. Wolden, Arnold C. Paulino, Nadia N. Laack, Natia Esiashvili, Sarah Milgrom, Christine Hill‐Kayser, Thomas J. Fitzgerald, John A. Kalapurakal, Chia‐Ho Hua   +17 more
wiley   +1 more source