activity
20122024
most citedImproving Generalization and Stability of Generative Adversarial Networks

82 citations · 257 across the 28 of their papers we have counts for

collaborators

26 papers

cs.AI2023

LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying

Thommen George Karimpanal, Laknath Buddhika Semage, Santu Rana +4

Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plau…

cs.CV2023

Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction

Hung Tran, Vuong Le, Svetha Venkatesh +1

Humans are highly adaptable, swiftly switching between different modes to progressively handle different tasks, situations and contexts. In Human-object interaction (HOI) activitie…

cs.AI2023

Memory-Augmented Theory of Mind Network

Dung Nguyen, Phuoc Nguyen, Hung Le +3

Social reasoning necessitates the capacity of theory of mind (ToM), the ability to contextualise and attribute mental states to others without having access to their internal cogni…

cs.CV2022

Video Dialog as Conversation about Objects Living in Space-Time

Hoang-Anh Pham, Thao Minh Le, Vuong Le +2

It would be a technological feat to be able to create a system that can hold a meaningful conversation with humans about what they watch. A setup toward that goal is presented as a…

cs.LG201982 cited

Improving Generalization and Stability of Generative Adversarial Networks

Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh

Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not be…

cs.LG20161 cited

Multilevel Anomaly Detection for Mixed Data

Kien Do, Truyen Tran, Svetha Venkatesh

Anomalies are those deviating from the norm. Unsupervised anomaly detection often translates to identifying low density regions. Major problems arise when data is high-dimensional…