2.4k citations · 2.7k across the 35 of their papers we have counts for
37 papers
Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
Shahin Atakishiyev, Housam K. B. Babiker, Jiayi Dai +8
Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a language model predicts the next…
Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation
Junming Su, Zhiqiang Shen, Peng Cao +2
The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate…
Adaptive Mix for Semi-Supervised Medical Image Segmentation
Zhiqiang Shen, Peng Cao, Junming Su +2
Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, blending two or more images to generate strong-perturbed samples for strong-weak ps…
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images
Qingshan Hou, Shuai Cheng, Peng Cao +4
Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and…
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement
Qingshan Hou, Peng Cao, Jiaqi Wang +3
Retinal fundus images have been applied for the diagnosis and screening of eye diseases, such as Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). However, both low-qualit…
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Zhiqiang Shen, Peng Cao, Hua Yang +3
Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models te…
Deep Temporal Modelling of Clinical Depression through Social Media Text
Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan +1
We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Symptoms Detection (DSD)…
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach
Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan +1
A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD…
Named Entity Recognition for Partially Annotated Datasets
Michael Strobl, Amine Trabelsi, Osmar Zaiane
The most common Named Entity Recognizers are usually sequence taggers trained on fully annotated corpora, i.e. the class of all words for all entities is known. Partially annotated…
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
Nouha Dziri, Sivan Milton, Mo Yu +2
Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallucination. In this work, we investigate t…
FREDA: Flexible Relation Extraction Data Annotation
Michael Strobl, Amine Trabelsi, Osmar Zaiane
To effectively train accurate Relation Extraction models, sufficient and properly labeled data is required. Adequately labeled data is difficult to obtain and annotating such data…
UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking
Abhishek Dhankar, Osmar R. Zaïane, Francois Bolduc
Identifying fake news is a very difficult task, especially when considering the multiple modes of conveying information through text, image, video and/or audio. We attempted to tac…
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
Chenyang Huang, Hao Zhou, Osmar R. Zaïane +2
How do we perform efficient inference while retaining high translation quality? Existing neural machine translation models, such as Transformer, achieve high performance, but they…
Simulated Annealing for Emotional Dialogue Systems
Chengzhang Dong, Chenyang Huang, Osmar Zaïane +1
Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions. In this study, we consider the task of expressing a…
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Haonan Wang, Peng Cao, Jiaqi Wang +1
Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to mo…
A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language
Nawshad Farruque, Randy Goebel, Osmar Zaiane
We analyze the process of creating word embedding feature representations designed for a learning task when annotated data is scarce, for example, in depressive language detection…
STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text
Nawshad Farruque, Randy Goebel, Osmar Zaiane +1
We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data s…
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
Nawshad Farruque, Chenyang Huang, Osmar Zaiane +1
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. We choose ou…
Building a Competitive Associative Classifier
Nitakshi Sood, Osmar Zaiane
With the huge success of deep learning, other machine learning paradigms have had to take back seat. Yet other models, particularly rule-based, are more readable and explainable an…
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)
Anandh Perumal, Chenyang Huang, Amine Trabelsi +1
In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining…
A multi-component framework for the analysis and design of explainable artificial intelligence
S. Atakishiyev, H. Babiker, N. Farruque +6
The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learnin…
U-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Xuebin Qin, Zichen Zhang, Chenyang Huang +3
In this paper, we design a simple yet powerful deep network architecture, U-Net, for salient object detection (SOD). The architecture of our U-Net is a two-level nested U-s…
Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning
Abhishek Nan, Anandh Perumal, Osmar R. Zaiane
Algorithmic trading, due to its inherent nature, is a difficult problem to tackle; there are too many variables involved in the real world which make it almost impossible to have r…
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
Chenyang Huang, Amine Trabelsi, Xuebin Qin +2
Emotion detection in text is an important task in NLP and is essential in many applications. Most of the existing methods treat this task as a problem of single-label multi-class t…