Search Results for author: Janghwan Lee

Found 6 papers, 1 papers with code

Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment

no code implementations3 Jul 2024 Janghwan Lee, Seongmin Park, Sukjin Hong, Minsoo Kim, Du-Seong Chang, Jungwook Choi

The rapid advancement of large language models (LLMs) has facilitated their transformation into conversational chatbots that can grasp contextual nuances and generate pertinent sentences, closely mirroring human values through advanced techniques such as instruction tuning and reinforcement learning from human feedback (RLHF).

Chatbot Computational Efficiency +2

Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization

no code implementations9 Nov 2023 Janghwan Lee, Minsoo Kim, SeungCheol Baek, Seok Joong Hwang, Wonyong Sung, Jungwook Choi

Large Language Models (LLMs) are proficient in natural language processing tasks, but their deployment is often restricted by extensive parameter sizes and computational demands.

Computational Efficiency Quantization

SHAPNN: Shapley Value Regularized Tabular Neural Network

no code implementations15 Sep 2023 Qisen Cheng, Shuhui Qu, Janghwan Lee

Additionally, prediction with explanation serves as a regularizer, which improves the model's performance.

Continual Learning valid

SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving

no code implementations12 May 2023 Minjae Lee, Seongmin Park, Hyungmin Kim, Minyong Yoon, Janghwan Lee, Jun Won Choi, Nam Sung Kim, Mingu Kang, Jungwook Choi

3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and latency requirements.

3D Object Detection Autonomous Driving +2

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