Search Results for author:
Found 26 papers, 1 papers with code
Advancing AI with Integrity: Ethical Challenges and Solutions in Neural Machine Translation
Additionally, the paper probes the distribution of responsibility between AI systems and humans, underscoring the essential role of human oversight in upholding NMT ethical standards.
Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda
Depression is a global burden and one of the most challenging mental health conditions to control.
Fast Training of NMT Model with Data Sorting
The Transformer model has revolutionized Natural Language Processing tasks such as Neural Machine Translation, and many efforts have been made to study the Transformer architecture, which increased its efficiency and accuracy.
Shared Latent Space by Both Languages in Non-Autoregressive Neural Machine Translation
Latent variable modeling in non-autoregressive neural machine translation (NAT) is a promising approach to mitigate the multimodality problem.
Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility
Autonomous mobility is emerging as a new disruptive mode of urban transportation for moving cargo and passengers.
Advanced Scaling Methods for VNF deployment with Reinforcement Learning
Network function virtualization (NFV) and software-defined network (SDN) have become emerging network paradigms, allowing virtualized network function (VNF) deployment at a low cost.
Building a Parallel Corpus and Training Translation Models Between Luganda and English
Neural machine translation (NMT) has achieved great successes with large datasets, so NMT is more premised on high-resource languages.
Partitioning Image Representation in Contrastive Learning
In this paper, we introduce a new representation, partitioned representation, which can learn both common and unique features of the anchor and positive samples in contrastive learning.
End-to-End Training for Back-Translation with Categorical Reparameterization Trick
Back-translation is an effective semi-supervised learning framework in neural machine translation (NMT).
Systematic Review for AI-based Language Learning Tools
The Second Language Acquisition field has been significantly impacted by a greater emphasis on individualized learning and rapid developments in artificial intelligence (AI).
Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains
Therefore, we propose several sequential deep learning models to learn time-series patterns and sequential patterns of the virtual network functions (VNFs) in the chain with variable lengths.
Adversarial Training with Contrastive Learning in NLP
In this work, we propose adversarial training with contrastive learning (ATCL) to adversarially train a language processing task using the benefits of contrastive learning.
Deep Neural Networks and End-to-End Learning for Audio Compression
We apply a reparametrization trick for the Bernoulli distribution for the discrete representations, which allows smooth backpropagation.
Reinforcement Learning of Graph Neural Networks for Service Function Chaining
In the management of computer network systems, the service function chaining (SFC) modules play an important role by generating efficient paths for network traffic through physical servers with virtualized network functions (VNF).
Graph Neural Network based Service Function Chaining for Automatic Network Control
Moreover, the GNN based model can be applied to a new network topology without re-designing and re-training.
Active Search for Nearest Neighbors
In pattern recognition or machine learning, it is a very fundamental task to find nearest neighbors of a given point.
Network Intrusion Detection based on LSTM and Feature Embedding
Growing number of network devices and services have led to increasing demand for protective measures as hackers launch attacks to paralyze or steal information from victim systems.
Self-Knowledge Distillation in Natural Language Processing
Since deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks, and in some cases, they are even outperforming humans.
Alpha-Integration Pooling for Convolutional Neural Networks
$\alpha$I-pooling is a general pooling method including max-pooling and arithmetic average-pooling as a special case, depending on the parameter $\alpha$.
Disentangling Latent Factors of Variational Auto-Encoder with Whitening
The proposed method is applied to the latent variables of variational auto-encoder (VAE), although it can be applied to any generative models with latent variables.
Gradient Acceleration in Activation Functions
Based on this explanation, we propose a new technique for activation functions, {\em gradient acceleration in activation function (GAAF)}, that accelerates gradients to flow even in the saturation area.
Understanding Dropout as an Optimization Trick
Based on this explanation, we propose a new technique for activation functions, {\em gradient acceleration in activation function (GAAF)}, that accelerates gradients to flow even in the saturation area.
Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory
LSTM transforms the input and the previous hidden states to the next states with the affine transformation, multiplication operations and a nonlinear activation function, which makes a good data representation for a given task.
Fine-Grained Attention Mechanism for Neural Machine Translation
Neural machine translation (NMT) has been a new paradigm in machine translation, and the attention mechanism has become the dominant approach with the state-of-the-art records in many language pairs.
A Neural Knowledge Language Model
Current language models have a significant limitation in the ability to encode and decode factual knowledge.
Context-Dependent Word Representation for Neural Machine Translation
Based on this observation, in this paper we propose to contextualize the word embedding vectors using a nonlinear bag-of-words representation of the source sentence.
