Search Results for author:
Found 23 papers, 4 papers with code
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention
Motivated by this, we introduce a new stochastic vision transformer that integrates uncertainty and distance awareness into self-supervised learning (SSL) pipelines.
AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
Feature attribution explains neural network outputs by identifying relevant input features.
Probabilistic Self-supervised Learning via Scoring Rules Minimization
In this paper, we propose a novel probabilistic self-supervised learning via Scoring Rule Minimization (ProSMIN), which leverages the power of probabilistic models to enhance representation quality and mitigate collapsing representations.
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning
Ensembling a neural network is a widely recognized approach to enhance model performance, estimate uncertainty, and improve robustness in deep supervised learning.
A Dual-Perspective Approach to Evaluating Feature Attribution Methods
We apply these metrics to mainstream attribution methods, offering a novel lens through which to analyze and compare feature attribution methods.
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning
Learning quality document embeddings is a fundamental problem in natural language processing (NLP), information retrieval (IR), recommendation systems, and search engines.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
However, existing methods for joint clustering and contrastive learning do not perform well on long-tailed data distributions, as majority classes overwhelm and distort the loss of minority classes, thus preventing meaningful representations to be learned.
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision
Learning from positive and unlabeled (PU) data is a setting where the learner only has access to positive and unlabeled samples while having no information on negative examples.
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models
One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced.
Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
In this work, we thus propose to tackle the issues of imbalanced datasets and model calibration in a PUL setting through an uncertainty-aware pseudo-labeling procedure (PUUPL): by boosting the signal from the minority class, pseudo-labeling expands the labeled dataset with new samples from the unlabeled set, while explicit uncertainty quantification prevents the emergence of harmful confirmation bias leading to increased predictive performance.
Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data
We introduce Self-GenomeNet, a novel contrastive self-supervised learning method for nucleotide-level genomic data, which substantially improves the quality of the learned representations and performance compared to the current state-of-the-art deep learning frameworks.
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images
We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images.
Deep Bregman Divergence for Contrastive Learning of Visual Representations
In this paper, we propose deep Bregman divergences for contrastive learning of visual representation where we aim to enhance contrastive loss used in self-supervised learning by training additional networks based on functional Bregman divergence.
Joint Debiased Representation Learning and Imbalanced Data Clustering
We simultaneously train two deep learning models, a deep representation network that captures the data distribution, and a deep clustering network that learns embedded features and performs clustering.
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
We design a new conditional GAN with two components: a generative model and a discriminative model to mitigate imbalanced data problem through selective weighted loss.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
We propose a new conditional generative refinement network with three components: a generative, a discriminative, and a refinement network to mitigate unbalanced data problem through ensemble learning.
Deep Learning for Medical Image Analysis
This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph. D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning algorithm.
Brain Abnormality Detection by Deep Convolutional Neural Network
In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network.
Deep Neural Network with l2-norm Unit for Brain Lesions Detection
Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations.
Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
In this paper, we propose a novel end-to-end trainable architecture for brain tumor semantic segmentation through conditional adversarial training.
