Search Results for author:
Found 120 papers, 43 papers with code
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data
Anomaly detection in real-world scenarios poses challenges due to dynamic and often unknown anomaly distributions, requiring robust methods that operate under an open-world assumption.
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery
In the first step, CDCT uses a Latent Diffusion Model (LDM) to generate a counterfactual trajectory dataset.
Data Collection of Real-Life Knowledge Work in Context: The RLKWiC Dataset
Over the years, various approaches have been employed to enhance the productivity of knowledge workers, from addressing psychological well-being to the development of personal knowledge assistants.
ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generation
We present ObjBlur, a novel curriculum learning approach to improve layout-to-image generation models, where the task is to produce realistic images from layouts composed of boxes and labels.
A Study in Dataset Pruning for Image Super-Resolution
We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model.
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Deep learning models have proven to be effective for this task by mapping time series data to high-level representation for prediction.
Ranked #1 on
Crop Classification
on CropHarvest - Brazil
CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction
The process of cyber mapping gives insights in relationships among financial entities and service providers.
Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
In this work, we assess the impact of missing temporal and static EO sources in trained models across four datasets with classification and regression tasks.
Latent Dataset Distillation with Diffusion Models
In response to these limitations, the concept of distilling the information on a dataset into a condensed set of (synthetic) samples, namely a distilled dataset, emerged.
TaylorShift: Shifting the Complexity of Self-Attention from Squared to Linear (and Back) using Taylor-Softmax
The quadratic complexity of the attention mechanism represents one of the biggest hurdles for processing long sequences using Transformers.
Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing
In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in Remote Sensing.
Modular Graph Extraction for Handwritten Circuit Diagram Images
However, apart from printed legacy schematics, hand-drawn circuit diagrams are still used today in the educational domain, where they serve as an easily accessible mean for trainees and students to learn drawing this type of diagrams.
Real-time Traffic Object Detection for Autonomous Driving
In this research, we assess the robustness of our previously proposed, highly efficient pedestrian detector LSFM on well-established autonomous driving benchmarks, including diverse weather conditions and nighttime scenes.
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task.
Diffusion Models, Image Super-Resolution And Everything: A Survey
Diffusion Models (DMs) have disrupted the image Super-Resolution (SR) field and further closed the gap between image quality and human perceptual preferences.
Class Conditional Time Series Generation with Structured Noise Space GAN
This paper introduces Structured Noise Space GAN (SNS-GAN), a novel approach in the field of generative modeling specifically tailored for class-conditional generation in both image and time series data.
Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation
Thus, Q-Seg emerges as a viable alternative for real-world applications using available quantum hardware, particularly in scenarios where the lack of labeled data and computational runtime are critical.
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification
This paper proposes a training strategy Medi-CAT to overcome the underfitting and overfitting phenomena in medical imaging datasets.
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content
Following the need for an optimal language model competent in extracting useful patterns from social media text, the key goal of this paper is to train language models in such a way that they learn to derive generalized patterns.
PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction
We conduct a comprehensive evaluation of the algorithm across various client and privacy settings, and demonstrate its capability to achieve comparable performance and privacy guarantees to standalone DP, even when accommodating an increasing number of participating clients.
Segment Anything for Microscopy
We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data.
Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision Transformers
This benchmark provides a standardized baseline across the landscape of efficiency-oriented transformers and our framework of analysis, based on Pareto optimality, reveals surprising insights.
Ranked #264 on
Image Classification
on ImageNet
Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions.
Dynamic Attention-Guided Diffusion for Image Super-Resolution
To address this, we introduce "You Only Diffuse Areas" (YODA), a dynamic attention-guided diffusion method for image SR. YODA selectively focuses on spatial regions using attention maps derived from the low-resolution image and the current time step in the diffusion process.
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance.
Ranked #2 on
Crop Classification
on CropHarvest - Togo
Towards Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories
This paper presents a retrospective overview of a decade of research in our department towards self-organizing personal knowledge assistants in evolving corporate memories.
DWA: Differential Wavelet Amplifier for Image Super-Resolution
This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR).
DartsReNet: Exploring new RNN cells in ReNet architectures
We present new Recurrent Neural Network (RNN) cells for image classification using a Neural Architecture Search (NAS) approach called DARTS.
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution
This paper presents a novel Diffusion-Wavelet (DiWa) approach for Single-Image Super-Resolution (SISR).
From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification
In this work, two very prominent GAN-based architectures were evaluated in the context of private time series classification.
On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches
Climate change has increased the severity and frequency of weather disasters all around the world.
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction
In this study, we developed a GAN model to generate synthetic time series to balance our limited recorded time series data and improve the accuracy of a data-driven model for combined sewer flow prediction.
Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images
This paper describes an approach for extracting both the electrical components (including their terminals and describing texts) as well their interconnections (including junctions and wire hops) by the means of instance segmentation and keypoint extraction.
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving
Autonomous driving systems rely heavily on the underlying perception module which needs to be both performant and efficient to allow precise decisions in real-time.
Ranked #1 on
Pedestrian Detection
on TJU-Ped-traffic
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques.
Privacy Meets Explainability: A Comprehensive Impact Benchmark
This work is the first to investigate the impact of private learning techniques on generated explanations for DL-based models.
Explainable Artificial Intelligence (XAI)
Privacy Preserving
+1
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain
Generative models are designed to address the data scarcity problem.
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area.
Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification
Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation.
A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance.
FiN: A Smart Grid and Power Line Communication Dataset
To overcome this, power line communication (PLC) has emerged as a potential solution for reliable monitoring of the low-voltage grid.
DT2I: Dense Text-to-Image Generation from Region Descriptions
Our results demonstrate the capability of our approach to generate plausible images of complex scenes using region captions.
DocXClassifier: High Performance Explainable Deep Network for Document Image Classification
Our approach achieves a new peak performance in image-based classification on two popular document datasets, namely RVL-CDIP and Tobacco3482, with a top-1 classification accuracy of 94. 17% and 95. 57% on the two datasets, respectively.
Ranked #1 on
Document Image Classification
on Tobacco-3482
Less is More: Proxy Datasets in NAS approaches
One of our surprising findings is that in most cases we can reduce the amount of training data to 25\%, consequently reducing search time to 25\%, while at the same time maintaining the same accuracy as if training on the full dataset.
F2DNet: Fast Focal Detection Network for Pedestrian Detection
However, the current two-stage detectors are inefficient as they do bounding box regression in multiple steps i. e. in region proposal networks and bounding box heads.
Ranked #2 on
Pedestrian Detection
on Caltech
(using extra training data)
Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training
The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results.
Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis
However, qualitative aspects provide deeper insights into the impact of a scientific research artifact and make it possible to focus on relevant literature free from bias associated with quantitative aspects.
TimeREISE: Time-series Randomized Evolving Input Sample Explanation
Deep neural networks are one of the most successful classifiers across different domains.
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting
End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-world applications.
Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods
However, due to the lack of transparency the use of these networks is hampered in the areas with safety critical areas.
Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability
The recent developments in the machine learning domain have enabled the development of complex multivariate probabilistic forecasting models.
ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions
This work presents ExAID (Explainable AI for Dermatology), a novel framework for biomedical image analysis, providing multi-modal concept-based explanations consisting of easy-to-understand textual explanations supplemented by visual maps justifying the predictions.
Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
To this end, we propose a search-and-learning approach that leverages pretrained language models but inserts the missing slots to improve the semantic coverage.
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification
With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders.
Violence Detection in Videos
To address this issue, an approach to automatically detect violent content in videos is proposed in this work.
LIVECell—A large-scale dataset for label-free live cell segmentation
Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena.
Ranked #1 on
Cell Segmentation
on LIVECell
Cell Segmentation
Cultural Vocal Bursts Intensity Prediction
+4
Spatial Transformer Networks for Curriculum Learning
Curriculum learning is a bio-inspired training technique that is widely adopted to machine learning for improved optimization and better training of neural networks regarding the convergence rate or obtained accuracy.
DeepCeNS: An end-to-end Pipeline for Cell and Nucleus Segmentation in Microscopic Images
To address the aforementioned challenges, DeepCeNS is proposed in this paper to detect and segment cells and nucleus in microscopic images.
Ranked #1 on
Cell Segmentation
on EVICAN
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks
Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve the classifier's performance.
AudioCLIP: Extending CLIP to Image, Text and Audio
AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90. 07% on the UrbanSound8K and 97. 15% on the ESC-50 datasets.
Ranked #1 on
Environmental Sound Classification
on ESC-50
Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction
These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread.
Combining Transformer Generators with Convolutional Discriminators
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks.
XAI Handbook: Towards a Unified Framework for Explainable AI
The field of explainable AI (XAI) has quickly become a thriving and prolific community.
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
In this paper, we propose a method for attribute controlled image synthesis from layout which allows to specify the appearance of individual objects without affecting the rest of the image.
Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis
Moreover, the possibility to intervene and guide models in case of misbehaviour is identified as a major step towards successful deployment of AI as DL-based DSS and beyond.
Confidence-Aware Learning Assistant
(2) With the help of 20 participants, we observed that correct answer rates of questions were increased by 14% and 17% by giving feedback about correct answers without confidence and incorrect answers with confidence, respectively.
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification
The classification of time-series data is pivotal for streaming data and comes with many challenges.
Adversarial Text-to-Image Synthesis: A Review
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area.
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks
Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e. g., minimization of the cross-entropy loss).
Ranked #89 on
Image Classification
on CIFAR-100
(using extra training data)
Going Beyond Classification Accuracy Metrics in Model Compression
With the rise in edge-computing devices, there has been an increasing demand to deploy energy and resource-efficient models.
Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems
Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable.
The Person Index Challenge: Extraction of Persons from Messy, Short Texts
First, we give a formal definition of the problem and describe a procedure to generate ground truth data for future evaluations.
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis
We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal.
Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching
In this work, we study an RNN-based architecture and address some of these issues by proposing a hybrid sequence-to-sequence architecture named HS2S, utilizing a dual mask propagation strategy that allows incorporating the information obtained from correspondence matching.
Benchmarking adversarial attacks and defenses for time-series data
This paves the way for future research in the direction of adversarial attacks and defenses, particularly for time-series data.
Training Deep Neural Networks Without Batch Normalization
The main purpose of this work is to determine if it is possible to train networks effectively when batch normalization is removed through adaption of the training process.
Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
For glaucoma classification we achieved AUC equal to 0. 874 which is 2. 7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA.
G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection
Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD).
Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection
Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina.
P2ExNet: Patch-based Prototype Explanation Network
Deep learning methods have shown great success in several domains as they process a large amount of data efficiently, capable of solving complex classification, forecast, segmentation, and other tasks.
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists.
Interpreting Deep Models through the Lens of Data
Identification of input data points relevant for the classifier (i. e. serve as the support vector) has recently spurred the interest of researchers for both interpretability as well as dataset debugging.
ImpactCite: An XLNet-based method for Citation Impact Analysis
Therefore, citation impact analysis (which includes sentiment and intent classification) enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and impact.
Ranked #1 on
Citation Intent Classification
on SciCite
(using extra training data)
Explaining AI-based Decision Support Systems using Concept Localization Maps
We evaluated our proposed method on SCDB as well as a real-world dataset called CelebA.
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN
The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently.
Multivariate Time Series Forecasting
Probabilistic Time Series Forecasting
+1
Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory
Video Object Segmentation (VOS) is an active research area of the visual domain.
DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks
Computational Fluid Dynamics (CFD) simulation by the numerical solution of the Navier-Stokes equations is an essential tool in a wide range of applications from engineering design to climate modeling.
ESResNet: Environmental Sound Classification Based on Visual Domain Models
Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years.
Ranked #5 on
Environmental Sound Classification
on UrbanSound8K
(using extra training data)
TSInsight: A local-global attribution framework for interpretability in time-series data
We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty.
P $\approx$ NP, at least in Visual Question Answering
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets.
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification
Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages.
A Hybrid Approach and Unified Framework for Bibliographic Reference Extraction
DeepBiRD was evaluated on two different datasets to demonstrate the generalization of this approach.
SentiCite: An Approach for Publication Sentiment Analysis
This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e. g., reference to a dataset, reading reference.
A Robust Hybrid Approach for Textual Document Classification
Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7. 7% on 20 Newsgroups and 6. 6% on BBC news datasets.
ProbAct: A Probabilistic Activation Function for Deep Neural Networks
We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features
This indicates a vital gap between the explainability provided by the systems and the novice user.
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it.
Generative Adversarial Network
Probabilistic Time Series Forecasting
+3
Interactive Concept Mining on Personal Data -- Bootstrapping Semantic Services
Semantic services (e. g. Semantic Desktops) are still afflicted by a cold start problem: in the beginning, the user's personal information sphere, i. e. files, mails, bookmarks, etc., is not represented by the system.
KINN: Incorporating Expert Knowledge in Neural Networks
The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series domains like anomaly detection, natural disaster management, predictive maintenance and healthcare.
Fusion Strategies for Learning User Embeddings with Neural Networks
In this paper, we run an experiment on movie ratings data, where we analyze the effect on embedding quality caused by several fusion strategies in neural networks.
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series
In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series.
Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications
Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing too much runtime performance.
The Focus-Aspect-Polarity Model for Predicting Subjective Noun Attributes in Images
Subjective visual interpretation is a challenging yet important topic in computer vision.
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA
We show that our metric is effective in providing a more fine-grained evaluation both on the quantitative and qualitative level.
Overcoming Missing and Incomplete Modalities with Generative Adversarial Networks for Building Footprint Segmentation
This assumption limits the applications of multi-modal models since in practice the data collection process is likely to generate data with missing, incomplete or corrupted modalities.
Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors
ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes.
Towards Semantically Enhanced Data Understanding
In the field of machine learning, data understanding is the practice of getting initial insights in unknown datasets.
Context Spaces as the Cornerstone of a Near-Transparent & Self-Reorganizing Semantic Desktop
Existing Semantic Desktops are still reproached for being too complicated to use or not scaling well.
What do Deep Networks Like to See?
To do this, an autoencoder (AE) was fine-tuned on gradients from a pre-trained classifier with fixed parameters.
Ranked #818 on
Image Classification
on ImageNet
Adversarial Defense based on Structure-to-Signal Autoencoders
We analyze their robustness against several white-box and gray-box scenarios on the large ImageNet dataset.
TSViz: Demystification of Deep Learning Models for Time-Series Analysis
This is a step towards making informed/explainable decisions in the domain of time-series, powered by deep learning.
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
In this paper, we address the problem of preserving semantic segmentation boundaries in high resolution satellite imagery by introducing a new cascaded multi-task loss.
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations.
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27, 000 labeled and geo-referenced images.
Judging a Book By its Cover
Book covers communicate information to potential readers, but can that same information be learned by computers?
Ranked #1 on
Genre classification
on Book Cover Dataset
A Novel Approach for Data-Driven Automatic Site Recommendation and Selection
The existing approaches for site selection (commonly used by economists) are manual, subjective, and not scalable, especially to Big Data.
An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia
We show the scalability of the algorithm by running it against a SPARQL endpoint loaded with > 7. 9 billion triples.
A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents
The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e. g., English, Russian, Arabic, Urdu, etc.
Symbol Grounding Association in Multimodal Sequences with Missing Elements
We evaluated the proposed extension in the following scenarios: missing elements in one modality (visual or audio) and missing elements in both modalities (visual and sound).
DeXpression: Deep Convolutional Neural Network for Expression Recognition
The proposed architecture achieves 99. 6% for CKP and 98. 63% for MMI, therefore performing better than the state of the art using CNNs.
Ranked #1 on
Facial Expression Recognition (FER)
on MMI
