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Found 23 papers, 13 papers with code
Predictive Authoring for Brazilian Portuguese Augmentative and Alternative Communication
We tested different approaches to representing a pictogram for prediction: as a word (using pictogram captions), as a concept (using a dictionary definition), and as a set of synonyms (using related terms).
Distinction Maximization Loss: Efficiently Improving Out-of-Distribution Detection and Uncertainty Estimation by Replacing the Loss and Calibrating
In this paper, we propose training deterministic neural networks using our DisMax loss, which works as a drop-in replacement for the usual SoftMax loss (i. e., the combination of the linear output layer, the SoftMax activation, and the cross-entropy loss).
ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Language
Sign language is an essential resource enabling access to communication and proper socioemotional development for individuals suffering from disabling hearing loss.
Multi-Cue Adaptive Emotion Recognition Network
Expressing and identifying emotions through facial and physical expressions is a significant part of social interaction.
On the Impact of Interpretability Methods in Active Image Augmentation Method
The results show that all methods achieve similar performance at the ending of training, but when combining ADA with GradCam, the U-Net model presented an impressive fast convergence.
Training Aware Sigmoidal Optimizer
We compared the proposed approach with commonly used adaptive learning rate schedules such as Adam, RMSProp, and Adagrad.
DQN-AF: Deep Q-Network based Adaptive Forwarding Strategy for Named Data Networking
This paper proposes an adaptive forwarding strategy based on deep reinforcement learning with Deep Q-Network, which analyzes the NDN router interface metrics without creating signaling overhead or harming the design principles from the NDN architecture, besides showing significant performance gains compared to the standard strategies.
Networking and Internet Architecture
KutralNet: A Portable Deep Learning Model for Fire Recognition
The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops.
Morphological Skip-Gram: Using morphological knowledge to improve word representation
In this work, we propose a new method for training word embeddings, and its goal is to replace the FastText bag of character n-grams for a bag of word morphemes through the morphological analysis of the word.
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the principle of maximum entropy.
Distantly-Supervised Neural Relation Extraction with Side Information using BERT
Considering that this method outperformed state-of-the-art baselines, in this paper, we propose a related approach to RESIDE also using additional side information, but simplifying the sentence encoding with BERT embeddings.
A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation
In this context, we investigated a method based on U-Net to detect the document edges and text regions in ID images.
AM-MobileNet1D: A Portable Model for Speaker Recognition
To address this demand, we propose a portable model called Additive Margin MobileNet1D (AM-MobileNet1D) to Speaker Identification on mobile devices.
Squeezed Deep 6DoF Object Detection Using Knowledge Distillation
We used the LINEMOD dataset to evaluate the proposed method, and the experimental results show that the proposed method reduces the memory requirement by almost 99\% in comparison to the original architecture with the cost of reducing half the accuracy in one of the metrics.
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum Entropy
Consequently, we propose IsoMax, a loss that is isotropic (distance-based) and produces high entropy (low confidence) posterior probability distributions despite still relying on cross-entropy minimization.
Improving Deep Image Clustering With Spatial Transformer Layers
Image clustering is an important but challenging task in machine learning.
Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition
The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons.
Heartbeat Anomaly Detection using Adversarial Oversampling
As in most health problems, the imbalance between examples and classes is predominant in this problem and affects the performance of the automated solution.
Squeezed Very Deep Convolutional Neural Networks for Text Classification
Most of the research in convolutional neural networks has focused on increasing network depth to improve accuracy, resulting in a massive number of parameters which restricts the trained network to platforms with memory and processing constraints.
Ranked #17 on
Sentiment Analysis
on Yelp Fine-grained classification
Additive Margin SincNet for Speaker Recognition
The Softmax loss function is a widely used function in deep learning methods, but it is not the best choice for all kind of problems.
Hierarchical Attentional Hybrid Neural Networks for Document Classification
Document classification is a challenging task with important applications.
Ranked #1 on
Text Classification
on Yelp-5
Simple Fast Convolutional Feature Learning
The quality of the features used in visual recognition is of fundamental importance for the overall system.
Enhancing Batch Normalized Convolutional Networks using Displaced Rectifier Linear Units: A Systematic Comparative Study
Besides, statistical significant performance assessments (p<0. 05) showed DReLU enhanced the test accuracy presented by ReLU in all scenarios.
