4 citations · 7 across the 7 of their papers we have counts for
7 papers
Neurosymbolic Language Reasoning as Satisfiability Modulo Theory
Hyunseok Oh, Sam Stern, Youngki Lee +1
Natural language understanding requires interleaving textual and logical reasoning, yet large language models often fail to perform such reasoning reliably. Existing neurosymbolic…
Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network
Hyunseok Oh, Youngki Lee
Spiking neural network (SNN) is studied in multidisciplinary domains to (i) enable order-of-magnitudes energy-efficient AI inference and (ii) computationally simulate neuro-scienti…
Papez: Resource-Efficient Speech Separation with Auditory Working Memory
Hyunseok Oh, Juheon Yi, Youngki Lee
Transformer-based models recently reached state-of-the-art single-channel speech separation accuracy; However, their extreme computational load makes it difficult to deploy them in…
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…
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents
Junseok Park, Kwanyoung Park, Hyunseok Oh +4
Critical periods are phases during which a toddler's brain develops in spurts. To promote children's cognitive development, proper guidance is critical in this stage. However, it i…
VECA : A Toolkit for Building Virtual Environments to Train and Test Human-like Agents
Kwanyoung Park, Hyunseok Oh, Youngki Lee
Building human-like agent, which aims to learn and think like human intelligence, has long been an important research topic in AI. To train and test human-like agents, we need an e…
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…