Search Results for author:
Found 143 papers, 50 papers with code
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
Within the field of multimodal DFER, recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders.
Ranked #1 on
Dynamic Facial Expression Recognition
on MAFW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+2
Geometric Deep Learning for Computer-Aided Design: A Survey
Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and engineers approach and enhance the design process.
Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder
To effectively trade-off communication, computation, and inference accuracy, we design a reward function and formulate the offloading problem of CNN inference as a maximization problem with the goal of maximizing the average inference accuracy and throughput over the long term.
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation
In anomaly detection, identification of anomalies across diverse product categories is a complex task.
Improving Unimodal Inference with Multimodal Transformers
Interestingly, we also observe that optimization of the unimodal branches improves the multimodal branch, compared to a similar multimodal model trained from scratch.
On Pixel-level Performance Assessment in Anomaly Detection
Anomaly detection methods have demonstrated remarkable success across various applications.
Variational Inference for GARCH-family Models
The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling.
Cryptocurrency Portfolio Optimization by Neural Networks
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation.
Convolutional autoencoder-based multimodal one-class classification
We show that such regularizers improve performance.
Curiosity-Driven Reinforcement Learning based Low-Level Flight Control
Curiosity is one of the main motives in many of the natural creatures with measurable levels of intelligence for exploration and, as a result, more efficient learning.
On Feature Diversity in Energy-based Models
Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches.
Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos
We introduce the first audio-visual dataset for traffic anomaly detection taken from real-world scenes, called MAVAD, with a diverse range of weather and illumination conditions.
Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition
This paper studies the computational offloading of video action recognition in edge computing.
Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement
The increasing prevalence of gigapixel resolutions has presented new challenges for crowd counting.
Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval
Using a discriminative representation obtained by supervised deep learning methods showed promising results on diverse Content-Based Image Retrieval (CBIR) problems.
Online Non-Destructive Moisture Content Estimation of Filter Media During Drying Using Artificial Neural Networks
Moisture content (MC) estimation is important in the manufacturing process of drying bulky filter media products as it is the prerequisite for drying optimization.
Variational Voxel Pseudo Image Tracking
Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor.
Uncertainty-Aware AB3DMOT by Variational 3D Object Detection
Autonomous driving needs to rely on high-quality 3D object detection to ensure safe navigation in the world.
Adaptive Parameterization of Deep Learning Models for Federated Learning
While this addresses limitations related to distributed data, it incurs a communication overhead as the model parameters or gradients need to be exchanged regularly during training.
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks
In this paper, we introduce PromptMix, a method for artificially boosting the size of existing datasets, that can be used to improve the performance of lightweight networks.
WLD-Reg: A Data-dependent Within-layer Diversity Regularizer
At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy.
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications
The proposed Edge Intelligence framework consists of the proposed effectiveness encoding and effectiveness decoding.
Attention-based Feature Compression for CNN Inference Offloading in Edge Computing
We design a feature compression module based on the channel attention method in CNN, to compress the intermediate data by selecting the most important features.
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
It is shown that the model selection with distributed inference HALP can significantly improve service reliability compared to the conventional stand-alone computation.
Bayesian Learning for Neural Networks: an algorithmic survey
The last decade witnessed a growing interest in Bayesian learning.
Structured Pruning Adapters
To improve on this, we propose Structured Pruning Adapters (SPAs), a family of compressing, task-switching network adapters, that accelerate and specialize networks using tiny parameter sets and structured pruning.
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey
X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography.
Recognition of Defective Mineral Wool Using Pruned ResNet Models
Considering the low quantity of the dataset, cross-validation and augmentation methods are used during the training.
Manifold Gaussian Variational Bayes on the Precision Matrix
We propose an optimization algorithm for Variational Inference (VI) in complex models.
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots
Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series.
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
To solve the maximization problem, we propose a graph reinforcement learning-based early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios.
Learning Diversified Feature Representations for Facial Expression Recognition in the Wild
Diversity of the features extracted by deep neural networks is important for enhancing the model generalization ability and accordingly its performance in different learning tasks.
Facial Expression Recognition
Facial Expression Recognition (FER)
Layer Ensembles
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and computing the difference in those predictions.
Efficient CNN with uncorrelated Bag of Features pooling
In this paper, we propose an approach that builds on top of BoF pooling to boost its efficiency by ensuring that the items of the learned dictionary are non-redundant.
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training
JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings.
HyperTime: Implicit Neural Representation for Time Series
We show how these networks can be leveraged for the imputation of time series, with applications on both univariate and multivariate data.
Efficient High-Resolution Deep Learning: A Survey
Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos.
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification
In addition, as the market evolves through time, it is necessary to update the existing models or train new ones when new data is made available.
Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
To reduce the computation time and communication overhead, we propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks of convolutional layers.
Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing.
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
Images and video frames captured by cameras placed throughout smart cities are often transmitted over the network to a server to be processed by deep neural networks for various tasks.
Variational Neural Networks
Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input.
VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images
In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT).
Dynamic Split Computing for Efficient Deep Edge Intelligence
Split computing is a paradigm where a DNN is split into two sections; the first section is executed on the end device, and the output is transmitted to the edge server where the final section is executed.
Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning
Our Quasi Black-box Variational Inference (QBVI) framework is readily applicable to a wide class of Bayesian inference problems and is of simple implementation as the updates of the variational posterior do not involve gradients with respect to the model parameters, nor the prescription of the Fisher information matrix.
Generalized Reference Kernel for One-class Classification
Focusing on small-scale one-class classification, our analysis and experimental results show that the new formulation provides approaches to regularize, adjust the rank, and incorporate additional information into the kernel itself, leading to improved one-class classification accuracy.
Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch
We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs) in PyTorch, a class of Neural Networks designed specifically for efficient inference in both online and batch processing scenarios.
Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives
Applying machine learning tools to digitized image archives has a potential to revolutionize quantitative research of visual studies in humanities and social sciences.
Continual Spatio-Temporal Graph Convolutional Networks
Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition.
Ranked #7 on
Skeleton Based Action Recognition
on Kinetics-Skeleton dataset
(GFLOPS per prediction metric)
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets
The prediction of financial markets is a challenging yet important task.
Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data
Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change.
Non-Linear Spectral Dimensionality Reduction Under Uncertainty
In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives.
Reducing Redundancy in the Bottleneck Representation of the Autoencoders
We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
Self-Attention Neural Bag-of-Features
In this work, we propose several attention formulations for multivariate sequence data.
Self-attention fusion for audiovisual emotion recognition with incomplete data
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition.
Ranked #1 on
Facial Emotion Recognition
on RAVDESS
Continual Transformers: Redundancy-Free Attention for Online Inference
Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time.
Ranked #4 on
Online Action Detection
on TVSeries
Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction
Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis.
Fighting Money Laundering with Statistics and Machine Learning
On the other hand, suspicious behavior flagging is characterized by non-disclosed features and hand-crafted risk indices.
Learning to ignore: rethinking attention in CNNs
Based on this idea, we propose to reformulate the attention mechanism in CNNs to learn to ignore instead of learning to attend.
Robust channel-wise illumination estimation
We test this approach on the proposed method and show that it can indeed be used to avoid several extreme error cases and, thus, improves the practicality of the proposed technique.
Constructing Neural Network-Based Models for Simulating Dynamical Systems
Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control.
On Quantitative Evaluations of Counterfactuals
As counterfactual examples become increasingly popular for explaining decisions of deep learning models, it is essential to understand what properties quantitative evaluation metrics do capture and equally important what they do not capture.
Improving Neural Network Generalization via Promoting Within-Layer Diversity
Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one.
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series
Industrial processes are monitored by a large number of various sensors that produce time-series data.
Remote Multilinear Compressive Learning with Adaptive Compression
By developing compressive sensing and learning models that can operate with an adaptive compression rate, we can maximize the informational content throughput of the whole application.
Bilinear Input Normalization for Neural Networks in Financial Forecasting
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network.
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving
Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand.
Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting
Financial market analysis, especially the prediction of movements of stock prices, is a challenging problem.
Multi-Exit Vision Transformer for Dynamic Inference
In this work, we propose seven different architectures for early exit branches that can be used for dynamic inference in Vision Transformer backbones.
Automatic Main Character Recognition for Photographic Studies
Identifying the main character in images plays an important role in traditional photographic studies and media analysis, but the task is performed manually and can be slow and laborious.
Learning distinct features helps, provably
We study the diversity of the features learned by a two-layer neural network trained with the least squares loss.
Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation
In this paper, we propose a method which learns an optimized compact network topology for real-time facial expression recognition utilizing localized facial landmark features.
Facial Expression Recognition
Facial Expression Recognition (FER)
Continual 3D Convolutional Neural Networks for Real-time Processing of Videos
We introduce Continual 3D Convolutional Neural Networks (Co3D CNNs), a new computational formulation of spatio-temporal 3D CNNs, in which videos are processed frame-by-frame rather than by clip.
Ranked #42 on
Action Classification
on Charades
Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems
This means that the methods can achieve a speed-up of $40$-$60\%$ by restricting operation to near objects while not sacrificing much in performance.
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead
Deploying deep learning models in time-critical applications with limited computational resources, for instance in edge computing systems and IoT networks, is a challenging task that often relies on dynamic inference methods such as early exiting.
Graph-Embedded Subspace Support Vector Data Description
In this paper, we propose a novel subspace learning framework for one-class classification.
Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning
Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustment of inference time.
Knowledge Distillation By Sparse Representation Matching
Knowledge Distillation refers to a class of methods that transfers the knowledge from a teacher network to a student network.
ECINN: Efficient Counterfactuals from Invertible Neural Networks
Counterfactual examples identify how inputs can be altered to change the predicted class of a classifier, thus opening up the black-box nature of, e. g., deep neural networks.
Visualising Deep Network's Time-Series Representations
Despite the popularisation of machine learning models, more often than not, they still operate as black boxes with no insight into what is happening inside the model.
Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm
The World Health Organization (WHO) has provided guidelines on how to reduce the spread of the virus and one of the most important measures is social distancing.
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification.
Ensembling object detectors for image and video data analysis
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data.
AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset
In order to do so, first a dataset that fully describes the operation of an industrial robot performing automated screwdriving must be available.
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY
During the last decade, neural networks have been intensively used to tackle various problems and they have often led to state-of-the-art results.
Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer
Automatic classification of speech commands has revolutionized human computer interactions in robotic applications.
Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition
Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph.
On the spatial attention in Spatio-Temporal Graph Convolutional Networks for skeleton-based human action recognition
Graph convolutional networks (GCNs) achieved promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a spatio-temporal graph.
Evaluating data augmentation for financial time series classification
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage.
Augmenting transferred representations for stock classification
In this paper we show that using transfer learning can help with this task, by pre-training a model to extract universal features on the full universe of stocks of the S$\&$P500 index and then transferring it to another model to directly learn a trading rule.
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition
In this paper, we propose a temporal attention module (TAM) for increasing the efficiency in skeleton-based action recognition by selecting the most informative skeletons of an action at the early layers of the network.
Performance Indicator in Multilinear Compressive Learning
Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i. e. tensors.
Graph Embedding with Data Uncertainty
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines.
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Computational color constancy is a preprocessing step used in many camera systems.
Attention-based Neural Bag-of-Features Learning for Sequence Data
In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on relevant sources of information for the given learning objective.
Probabilistic Color Constancy
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC).
Self-Organized Operational Neural Networks with Generative Neurons
However, Greedy Iterative Search (GIS) method, which is the search method used to find optimal operators in ONNs takes many training sessions to find a single operator set per layer.
Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol
Domain Adaptation is the process of alleviating distribution gaps between data from different domains.
Ranked #27 on
Domain Adaptation
on Office-31
Saliency-based Weighted Multi-label Linear Discriminant Analysis
The proposed method is based on a probabilistic model for defining the weights of individual samples in a weighted multi-label LDA approach.
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification
Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification.
Not all domains are equally complex: Adaptive Multi-Domain Learning
The most common approach in multi-domain learning is to form a domain agnostic model, the parameters of which are shared among all domains, and learn a small number of extra domain-specific parameters for each individual new domain.
Ellipsoidal Subspace Support Vector Data Description
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification.
Supervised Domain Adaptation using Graph Embedding
Getting deep convolutional neural networks to perform well requires a large amount of training data.
Subset Sampling For Progressive Neural Network Learning
Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data.
Multilinear Compressive Learning with Prior Knowledge
Extensive experiments demonstrate that the proposed knowledge transfer method can effectively train MCL models to compressively sense and synthesize better features for the learning tasks with improved performances, especially when the complexity of the learning task increases.
Incremental Fast Subclass Discriminant Analysis
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA).
Automatic image-based identification and biomass estimation of invertebrates
We use this database to test the classification accuracy i. e. how well the species identity of a specimen can be predicted from images taken by the machine.
Text-to-image synthesis method evaluation based on visual patterns
Visual concepts are determined by clustering the real image representations, and are subsequently used to evaluate the similarity of the generated images to the real ones by classifying them to the closest visual concept.
INTEL-TAU: A Color Constancy Dataset
In this paper, we describe a new large dataset for illumination estimation.
Few-Shot Camera-Adaptive Color Constancy
Image Declipping
+1
Null Space Analysis for Class-Specific Discriminant Learning
In this paper, we carry out null space analysis for Class-Specific Discriminant Analysis (CSDA) and formulate a number of solutions based on the analysis.
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose.
Bag of Color Features For Color Constancy
To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention.
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem.
Multilinear Compressive Learning
Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of measurements.
Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
Speed-up and multi-view extensions to Subclass Discriminant Analysis
We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process.
Machine Learning Based Analysis of Finnish World War II Photographers
In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives.
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification.
Feature Engineering for Mid-Price Prediction with Deep Learning
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB.
3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images
Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales.
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies.
Deep Adaptive Input Normalization for Time Series Forecasting
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success.
Operational Neural Networks
In order to address this drawback and also to accomplish a more generalized model over the convolutional neurons, this study proposes a novel network model, called Operational Neural Networks (ONNs), which can be heterogeneous and encapsulate neurons with any set of operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
However, combining existing BoF formulations with deep feature extractors pose significant challenges: the distribution of the input features is not stationary, tuning the hyper-parameters of the model can be especially difficult and the normalizations involved in the BoF model can cause significant instabilities during the training process.
Class Mean Vector Component and Discriminant Analysis
The kernel matrix used in kernel methods encodes all the information required for solving complex nonlinear problems defined on data representations in the input space using simple, but implicitly defined, solutions.
Probabilistic Class-Specific Discriminant Analysis
In this paper we formulate a probabilistic model for class-specific discriminant subspace learning.
Using Deep Learning for price prediction by exploiting stationary limit order book features
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems.
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis.
Progressive Operational Perceptron with Memory
Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model in the traditional Multilayer Perceptron (MLP) and this model can mimic the synaptic connections of the biological neurons that have nonlinear neurochemical behaviours.
Multi-layer Kernel Ridge Regression for One-class Classification
In this paper, a multi-layer architecture (in a hierarchical fashion) by stacking various Kernel Ridge Regression (KRR) based Auto-Encoder for one-class classification is proposed and is referred as MKOC.
Heterogeneous Multilayer Generalized Operational Perceptron
Previously, Generalized Operational Perceptron (GOP) was proposed to extend conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons.
Weighted Linear Discriminant Analysis based on Class Saliency Information
In this paper, we propose a new variant of Linear Discriminant Analysis to overcome underlying drawbacks of traditional LDA and other LDA variants targeting problems involving imbalanced classes.
Subspace Support Vector Data Description
The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space.
Deep Multi-view Learning to Rank
We study the problem of learning to rank from multiple information sources.
Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
Multilinear Class-Specific Discriminant Analysis
There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques.
Improving Efficiency in Convolutional Neural Network with Multilinear Filters
The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices.
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders.
Neural Class-Specific Regression for face verification
Face verification is a problem approached in the literature mainly using nonlinear class-specific subspace learning techniques.
Human experts vs. machines in taxa recognition
Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts ($\overline{CE}=13. 8\%$).
Probabilistic Saliency Estimation
In this paper, we model the salient object detection problem under a probabilistic framework encoding the boundary connectivity saliency cue and smoothness constraints in an optimization problem.
Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval
In this paper, the problem of multi-view embedding from different visual cues and modalities is considered.
