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Found 53 papers, 13 papers with code
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification
Global sensitivity analysis (GSA) aims to detect influential input factors that lead a model to arrive at a certain decision and is a significant approach for mitigating the computational burden of processing high dimensional data.
Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models
The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution.
Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset
We exploit the Morpho-MNIST causal dataset as a case study for exploring our proposed methods for generating counterfacutl explantions.
Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning
The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams.
Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting
To this end, we fuse the spatiotemporal features from the AIS messages with probabilistic features engineered from historical AIS data referring to potential routes and destinations.
Pyrus Base: An Open Source Python Framework for the RoboCup 2D Soccer Simulation
To conquer the challenges of C++ base codes and provide a powerful baseline for developing machine learning concepts, we introduce Pyrus, the first Python base code for SS2D.
Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023
The RoboCup competitions hold various leagues, and the Soccer Simulation 2D League is a major one among them.
Evolutionary Augmentation Policy Optimization for Self-supervised Learning
We also compare optimal SSL solutions found by our evolutionary search mechanism and show the effect of batch size in the pretext task on two visual datasets.
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels
To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry highlighting the changes in the vessel's moving pattern which tends to indicate fishing activity.
CYRUS Soccer Simulation 2D Team Description Paper 2022
Soccer Simulation 2D League is one of the major leagues of RoboCup competitions.
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data.
Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning
The oceans are a source of an impressive mixture of complex data that could be used to uncover relationships yet to be discovered.
Survey of Generative Methods for Social Media Analysis
This survey draws a broad-stroke, panoramic picture of the State of the Art (SoTA) of the research in generative methods for the analysis of social media data.
SGORNN: Combining Scalar Gates and Orthogonal Constraints in Recurrent Networks
Our experimental results on the addition problem confirm that our combination of orthogonal and scalar gated RNNs are able to outperform both predecessor models on long sequences using only a single RNN cell.
Continuous Control with Deep Reinforcement Learning for Autonomous Vessels
We designed our model based on Deep Deterministic Policy Gradient, local view maker, and planner.
Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation
Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion detection and analysis, based on the Plutchik/Ekman approach to emotion detection and trend detection.
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
Time-series forecasting is one of the most active research topics in artificial intelligence.
COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processing
The COVID-19 pandemic has affected people's lives in many ways.
SemEval-2020 Task 5: Counterfactual Recognition
Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not.
Learn Faster and Forget Slower via Fast and Stable Task Adaptation
We empirically show that compared to prevailing fine-tuning practices, FAST learns the target task faster and forgets the source task slower.
Analyzing the Impact of Foursquare and Streetlight Data with Human Demographics on Future Crime Prediction
Finding the factors contributing to criminal activities and their consequences is essential to improve quantitative crime research.
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective.
Ranked #1 on
Unsupervised Domain Adaptation
on Office-Home
(Avg accuracy metric)
Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning
The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade.
Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation
One of the most critical steps for trajectory data mining is segmentation.
Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments
Experimental results show that the proposed method enhanced the performance of VVN by 55. 31 on average for long-distance missions.
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
We present an approach to explain the decisions of black box models for image classification.
Performance of a Deep Neural Network at Detecting North Atlantic Right Whale Upcalls
Passive acoustics provides a powerful tool for monitoring the endangered North Atlantic right whale ($Eubalaena$ $glacialis$), but robust detection algorithms are needed to handle diverse and variable acoustic conditions and differences in recording techniques and equipment.
Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples
We use several state-of-the-art embedding techniques including BERT, FastText, and Doc2VecC for the text representation learning and WaveNet and VGG-ish models for audio encoding.
Unsupervised Behavior Change Detection in Multidimensional Data Streams for Maritime Traffic Monitoring
The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities.
Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities
In addition, we identify ten female figures from different professions and racial backgrounds who have experienced harassment on Twitter.
Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation
Research into automated systems for detecting and classifying marine mammals in acoustic recordings is expanding internationally due to the necessity to analyze large collections of data for conservation purposes.
Efficient Neural Task Adaptation by Maximum Entropy Initialization
Transferring knowledge from one neural network to another has been shown to be helpful for learning tasks with few training examples.
Learning to Learn with Conditional Class Dependencies
Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning.
When a Tweet is Actually Sexist. A more Comprehensive Classification of Different Online Harassment Categories and The Challenges in NLP
Sexism is very common in social media and makes the boundaries of freedom tighter for feminist and female users.
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection.
Ranked #2 on
Acoustic Novelty Detection
on A3Lab PASCAL CHiME
2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements
In the advent of big data era, interactive visualization of large data sets consisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+), is an indispensable tool for data exploratory analysis.
How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques
While sexism has been considered as a category of hateful speech in the literature, there is no comprehensive definition and category of sexism attracting natural language processing techniques.
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
To tackle this problem, we apply the Knowledge Distillation technique to distill Deep Neural Networks into decision trees in order to attain good performance and interpretability simultaneously.
Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs
In our text generation approach, we generate new tweets by replacing words using data acquired from ConceptNet relations in order to increase the size of our training set, this method is very helpful with frustratingly small datasets, preserves the label and increases diversity.
On feature selection and evaluation of transportation mode prediction strategies
Since the number of features that may be used to predict a user transportation mode can be substantial, finding a subset of features that maximizes a performance measure is worth investigating.
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning
In this paper, we propose a regression system to infer the emotion intensity of a tweet.
Predicting Crime Using Spatial Features
Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime.
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports.
Interpretable Deep Convolutional Neural Networks via Meta-learning
We attempt to address this challenge by proposing a technique called CNN-INTE to interpret deep Convolutional Neural Networks (CNN) via meta-learning.
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing.
Studying Positive Speech on Twitter
In fully automated studies, we tested two approaches: unsupervised statistical analysis, and supervised text classification based on distributed word representation.
Reflexive Regular Equivalence for Bipartite Data
Reflexive regular equivalence can also use the structure of transitivities -- in a network sense -- the contribution of which is controlled by the algorithm's only free-parameter, $\alpha$.
Topic Modelling and Event Identification from Twitter Textual Data
The tremendous growth of social media content on the Internet has inspired the development of the text analytics to understand and solve real-life problems.
