Zobrazeno 1 - 10
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pro vyhledávání: '"Kim, Sejin"'
We propose a novel offline reinforcement learning (offline RL) approach, introducing the Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation (DIAR) framework. We address two key challenges in offline RL: out-of-distribution samples a
Externí odkaz:
http://arxiv.org/abs/2410.11338
Effective long-term strategies enable AI systems to navigate complex environments by making sequential decisions over extended horizons. Similarly, reinforcement learning (RL) agents optimize decisions across sequences to maximize rewards, even witho
Externí odkaz:
http://arxiv.org/abs/2410.11324
Autor:
Kim, Sejin, Kim, Sundong
While significant progress has been made in task-specific applications, current models struggle with deep reasoning, generality, and adaptation -- key components of System-2 reasoning that are crucial for achieving Artificial General Intelligence (AG
Externí odkaz:
http://arxiv.org/abs/2410.07866
We address an inverse problem in modeling holographic superconductors. We focus our research on the critical temperature behavior depicted by experiments. We use a physics-informed neural network method to find a mass function $M(F^2)$, which is nece
Externí odkaz:
http://arxiv.org/abs/2410.06523
The effectiveness of AI model training hinges on the quality of the trajectory data used, particularly in aligning the model's decision with human intentions. However, in the human task-solving trajectories, we observe significant misalignments betwe
Externí odkaz:
http://arxiv.org/abs/2409.14191
This paper demonstrates that model-based reinforcement learning (model-based RL) is a suitable approach for the task of analogical reasoning. We hypothesize that model-based RL can solve analogical reasoning tasks more efficiently through the creatio
Externí odkaz:
http://arxiv.org/abs/2408.14855
Autor:
Lee, Hosung, Kim, Sejin, Lee, Seungpil, Hwang, Sanha, Lee, Jihwan, Lee, Byung-Jun, Kim, Sundong
This paper introduces ARCLE, an environment designed to facilitate reinforcement learning research on the Abstraction and Reasoning Corpus (ARC). Addressing this inductive reasoning benchmark with reinforcement learning presents these challenges: a v
Externí odkaz:
http://arxiv.org/abs/2407.20806
Autor:
Lee, Seungpil, Sim, Woochang, Shin, Donghyeon, Seo, Wongyu, Park, Jiwon, Lee, Seokki, Hwang, Sanha, Kim, Sejin, Kim, Sundong
The existing methods for evaluating the inference abilities of Large Language Models (LLMs) have been results-centric, making it difficult to assess the inference process. We introduce a new approach using the Abstraction and Reasoning Corpus (ARC) d
Externí odkaz:
http://arxiv.org/abs/2403.11793
In this work, we investigate an extended model of holographic superconductor by a non-linear electrodynamic interaction coupled to a complex scalar field. This non-linear interaction term can make a quantum phase transition at zero temperature with f
Externí odkaz:
http://arxiv.org/abs/2312.06321
We have constructed a generative artificial intelligence model to predict dual gravity solutions when provided with the input of holographic entanglement entropy. The model utilized in our study is based on the transformer algorithm, widely used for
Externí odkaz:
http://arxiv.org/abs/2311.01724