Search Results for author:
Found 23 papers, 8 papers with code
Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training
This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD).
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.
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.
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data
We find that denoising autoencoders applied to this task already outperform other approaches in the cell error detection rates as well as in the expected value rates.
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.
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.
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.
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
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.
RECol: Reconstruction Error Columns for Outlier Detection
Detecting outliers or anomalies is a common data analysis task.
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)
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.
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)
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.
An Overview of Computational Approaches for Interpretation Analysis
Second, what if the "beholder" is a computer model, i. e., how can we explain a computer model's point of view?
The Focus-Aspect-Polarity Model for Predicting Subjective Noun Attributes in Images
Subjective visual interpretation is a challenging yet important topic in computer vision.
Towards Semantically Enhanced Data Understanding
In the field of machine learning, data understanding is the practice of getting initial insights in unknown datasets.
Simplified SPARQL REST API - CRUD on JSON Object Graphs via URI Paths
Within the Semantic Web community, SPARQL is one of the predominant languages to query and update RDF knowledge.
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
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.
