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Found 24 papers, 6 papers with code
Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by Content-Based Classification of Image Blocks
Recent advances in capsule endoscopy systems have introduced new methods and capabilities.
Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background
By increasing the volume of telemedicine information, the need for medical image compression has become more important.
Left ventricle segmentation By modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference
Deep neural networks have shown great achievements in solving complex problems.
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge
We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA).
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images
The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network.
A Knowledge Hunting Framework for Common Sense Reasoning
We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge.
Ranked #65 on
Coreference Resolution
on Winograd Schema Challenge
ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks
Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, application of Convolutional Neural Networks for watermarking have recently emerged.
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision.
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG
Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG.
Ranked #36 on
Coreference Resolution
on Winograd Schema Challenge
Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age.
Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features
Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease.
Gland Segmentation in Histopathological Images by Deep Neural Network
Histology method is vital in the diagnosis and prognosis of cancers and many other diseases.
Localization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks
One of the routine examinations that are used for prenatal care in many countries is ultrasound imaging.
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales
Intracranial tumors are groups of cells that usually grow uncontrollably.
Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning
Reduction of the number of channels could reduce the complexity of brain-computer-interface devices.
An Analysis of Dataset Overlap on Winograd-Style Tasks
The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR).
ADEPT: An Adjective-Dependent Plausibility Task
A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors.
Not-so fine-tuning: Measures of Common Sense for Language Models
Language models built using semi-supervised machine learning on large corpora of natural language have very quickly enveloped the fields of natural language generation and understanding.
An Application of Pseudo-Log-Likelihoods to Natural Language Scoring
We identify a practical cost for our method and model: high GPU-time for natural language evaluation.
DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection
The proposed method accelerates the convergence of the genetic algorithm and increases the system's performance.
Dynamic-Pix2Pix: Noise Injected cGAN for Modeling Input and Target Domain Joint Distributions with Limited Training Data
Helping the cGAN learn the target distribution from noise input results in a better model generalization during the test time and allows the model to fit almost perfectly to the target domain distribution.
Debiasing should be Good and Bad: Measuring the Consistency of Debiasing Techniques in Language Models
Debiasing methods that seek to mitigate the tendency of Language Models (LMs) to occasionally output toxic or inappropriate text have recently gained traction.
WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Experts
The Winograd Schema Challenge (WSC) serves as a prominent benchmark for evaluating machine understanding.
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries
Our results emphasize the challenge posed by EvoGrad: Even the best performing LLM, GPT-3. 5, achieves an accuracy of 65. 0% with an average error depth of 7. 2, a stark contrast to human performance of 92.
