High Efficiency
High efficiency in various computational domains is a central research theme, aiming to minimize resource consumption (time, memory, energy) while maintaining or improving performance. Current efforts focus on developing novel algorithms and architectures, such as optimized Thompson sampling for reinforcement learning, sparse attention mechanisms for transformers, and efficient model compression techniques, to achieve this goal across diverse applications including natural language processing, computer vision, and robotics. These advancements are crucial for deploying complex AI models on resource-constrained devices and for accelerating scientific discovery in data-intensive fields.
Papers
V-LoRA: An Efficient and Flexible System Boosts Vision Applications with LoRA LMM
Liang Mi, Weijun Wang, Wenming Tu, Qingfeng He, Rui Kong, Xinyu Fang, Yazhu Dong, Yikang Zhang, Yunchun Li, Meng Li, Haipeng Dai, Guihai Chen, Yunxin Liu
Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems
Sourav Modak, Anthony Stein
EmbodiedRAG: Dynamic 3D Scene Graph Retrieval for Efficient and Scalable Robot Task Planning
Meghan Booker, Grayson Byrd, Bethany Kemp, Aurora Schmidt, Corban Rivera
Tiny Learning-Based MPC for Multirotors: Solver-Aware Learning for Efficient Embedded Predictive Control
Babak Akbari, Justin Frank, Melissa Greeff
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment
Ge Yang, Changyi He, Jinyang Guo, Jianyu Wu, Yifu Ding, Aishan Liu, Haotong Qin, Pengliang Ji, Xianglong Liu
SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity
Kunyun Wang, Jieru Zhao, Shuo Yang, Wenchao Ding, Minyi Guo
Aerodynamics and Sensing Analysis for Efficient Drone-Based Parcel Delivery
Avishkar Seth, Alice James, Endrowednes Kuantama, Subhas Mukhopadhyay, Richard Han
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning
Kaiyan Zhao, Yiming Wang, Yuyang Chen, Xiaoguang Niu, Yan Li, Leong Hou U
Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models
Zhengmian Hu, Heng Huang
Conditional GAN for Enhancing Diffusion Models in Efficient and Authentic Global Gesture Generation from Audios
Yongkang Cheng, Mingjiang Liang, Shaoli Huang, Gaoge Han, Jifeng Ning, Wei Liu
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs
Yifei Zhang, Hao Zhu, Aiwei Liu, Han Yu, Piotr Koniusz, Irwin King
A Robust and Efficient Visual-Inertial Initialization with Probabilistic Normal Epipolar Constraint
Changshi Mu, Daquan Feng, Qi Zheng, Yuan Zhuang
LArctan-SKAN: Simple and Efficient Single-Parameterized Kolmogorov-Arnold Networks using Learnable Trigonometric Function
Zhijie Chen, Xinglin Zhang
FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing
Chao Li, Zhicheng Xu, Bo Wen, Ruibin Mao, Can Li, Thomas Kämpfe, Kai Ni, Xunzhao Yin
From Efficiency to Equity: Measuring Fairness in Preference Learning
Shreeyash Gowaikar, Hugo Berard, Rashid Mushkani, Shin Koseki
Enhancing pretraining efficiency for medical image segmentation via transferability metrics
Gábor Hidy, Bence Bakos, András Lukács
Taipan: Efficient and Expressive State Space Language Models with Selective Attention
Chien Van Nguyen, Huy Huu Nguyen, Thang M. Pham, Ruiyi Zhang, Hanieh Deilamsalehy, Puneet Mathur, Ryan A. Rossi, Trung Bui, Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen