277 citations · 936 across the 59 of their papers we have counts for
6 papers · 1 filter
On the Importance of Critical Period in Multi-stage Reinforcement Learning
Junseok Park, Inwoo Hwang, Min Whoo Lee +4
The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plastic…
Toward a Human-Level Video Understanding Intelligence
Yu-Jung Heo, Minsu Lee, Seongho Choi +5
We aim to develop an AI agent that can watch video clips and have a conversation with human about the video story. Developing video understanding intelligence is a significantly ch…
Learning task-agnostic representation via toddler-inspired learning
Kwanyoung Park, Junseok Park, Hyunseok Oh +2
One of the inherent limitations of current AI systems, stemming from the passive learning mechanisms (e.g., supervised learning), is that they perform well on labeled datasets but…
Constructing Hierarchical Q&A Datasets for Video Story Understanding
Yu-Jung Heo, Kyoung-Woon On, Seongho Choi +5
Video understanding is emerging as a new paradigm for studying human-like AI. Question-and-Answering (Q&A) is used as a general benchmark to measure the level of intelligence for v…
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals
Sungtae Lee, Sang-Woo Lee, Jinyoung Choi +2
Recently, reinforcement learning has been successfully applied to the logical game of Go, various Atari games, and even a 3D game, Labyrinth, though it continues to have problems i…
Generating Images Part by Part with Composite Generative Adversarial Networks
Hanock Kwak, Byoung-Tak Zhang
Image generation remains a fundamental problem in artificial intelligence in general and deep learning in specific. The generative adversarial network (GAN) was successful in gener…