Search By Subject

Human-robot interaction is limited in large part by the challenge of writing correct specifications for robots. The research community wants alignment between humans' goals and robot behaviors, but this alignment is very hard to achieve. My research ...

Working Memory (WM) is a central component of cognition. It has direct impact not only on core cognitive processes, such as learning, comprehension, and reasoning, but also language-related processes, such as natural language understanding and referring ...

Visual Question Answering (VQA) can facilitate social convenience, which needs to study complex joint reasoning in the visual and language over external knowledge. Recently, Knowledge-Based VQA has attracted the attention of researchers. There are many ...

We report goals, paper submissions, keynotes, and organizations of this UserNLP workshop. User-centered NLP can fill these gaps by explicitly considering stylistic variations across individuals or groups of individuals and focusing on user-level ...

In recent years, deep learning and web of things (WoT) have become hot topics. The relevant research issues in deep learning have been in increasingly investigated and published. Therefore, the title of this workshop is ”the 2nd International Workshop ...

SocialNLP is a new inter-disciplinary area of natural language processing (NLP) and social computing. We consider three plausible directions of SocialNLP: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using ...

The First Workshop on Graph Learning aims to bring together researchers and practitioners from academia and industry to discuss recent advances and core challenges of graph learning. This workshop will be established as a platform for multiple ...

Expressing opinions and interacting with others on the Web has led to an abundance of online discourse: claims and viewpoints on controversial topics, their sources and contexts. This constitutes a valuable source of insights for studies into mis- / ...

Graph-structured data is omnipresent in various fields, such as biology, chemistry, social media and transportation. Learning informative graph representations are crucial in effectively completing downstream graph-related tasks such as node/graph ...

The vulnerability of deep neural network (DNN) models has been verified by the existence of adversarial examples. By exploiting slight changes to input examples, the generated adversarial examples can easily cause well trained DNN models make wrong ...

Recent Transformer-based approaches to NLG like GPT-2 can generate syntactically coherent original texts. However, these generated texts have serious flaws. One of them is a global discourse incoherence. We present an approach to estimate the quality of ...

In recent years, machine learning (ML) technologies have experienced swift developments and attracted extensive attention from both academia and industry. The applications of ML are extended to multiple domains, from computer vision, text processing, to ...

In the current era, data are generated by almost every electronic devices. These data are often produced continuously from many IoT devices resulting in an enormous corpus of data streams. To learn and model such data streams, an adaptive, robust and ...

Deep Learning Models such as Convolution Neural Networks (CNNs) have shown great potential in various applications. However, these techniques will face regulatory compliance challenges related to privacy of user data, especially when they are deployed ...

Artificial Neural Networks (ANNs) have drawn academy and industry attention for their ability to represent and solve complex problems. Researchers are studying how to distribute their computation to reduce their training time. However, the most common ...

We consider the problem of achieving fair classification in Federated Learning (FL) under data heterogeneity. Most of the approaches proposed for fair classification require diverse data that represent the different demographic groups involved. In ...

Prediction of molecular properties using molecular structures is an important problem in chem-/bioinformatics. In this work, we propose a molecular representation approach using the concept of functional groups in chemistry. The proposed molecular ...

Catastrophic overfitting is a phenomenon observed during Adversarial Training (AT) with the Fast Gradient Sign Method (FGSM) where the test robustness steeply declines over just one epoch in the training stage. Prior work has attributed this loss in ...

Deep visual models are fast surpassing human-level performance for various vision tasks, including object detection, increasing their use in day-to-day life applications. It is often the case that standard models that perform well when evaluated on the ...