Zobrazeno 1 - 10
of 11 051
pro vyhledávání: '"A, Yuda"'
The development of Large Language Models (LLMs) has created transformative opportunities for the financial industry, especially in the area of financial trading. However, how to integrate LLMs with trading systems has become a challenge. To address t
Externí odkaz:
http://arxiv.org/abs/2412.04856
Self-improvement is a mechanism in Large Language Model (LLM) pre-training, post-training and test-time inference. We explore a framework where the model verifies its own outputs, filters or reweights data based on this verification, and distills the
Externí odkaz:
http://arxiv.org/abs/2412.02674
Autor:
Jiang, Qing, Luo, Gen, Yang, Yuqin, Xiong, Yuda, Chen, Yihao, Zeng, Zhaoyang, Ren, Tianhe, Zhang, Lei
Perception and understanding are two pillars of computer vision. While multimodal large language models (MLLM) have demonstrated remarkable visual understanding capabilities, they arguably lack accurate perception abilities, e.g. the stage-of-the-art
Externí odkaz:
http://arxiv.org/abs/2411.18363
Autor:
Ren, Tianhe, Chen, Yihao, Jiang, Qing, Zeng, Zhaoyang, Xiong, Yuda, Liu, Wenlong, Ma, Zhengyu, Shen, Junyi, Gao, Yuan, Jiang, Xiaoke, Chen, Xingyu, Song, Zhuheng, Zhang, Yuhong, Huang, Hongjie, Gao, Han, Liu, Shilong, Zhang, Hao, Li, Feng, Yu, Kent, Zhang, Lei
In this paper, we introduce DINO-X, which is a unified object-centric vision model developed by IDEA Research with the best open-world object detection performance to date. DINO-X employs the same Transformer-based encoder-decoder architecture as Gro
Externí odkaz:
http://arxiv.org/abs/2411.14347
Autor:
An, Yuda, Yi, Shushu, Mao, Bo, Li, Qiao, Zhang, Mingzhe, Zhou, Ke, Xiao, Nong, Sun, Guangyu, Wang, Xiaolin, Luo, Yingwei, Zhang, Jie
Compute Express Link (CXL) serves as a rising industry standard, delivering high-speed cache-coherent links to a variety of devices, including host CPUs, computational accelerators, and memory devices. It is designed to promote system scalability, en
Externí odkaz:
http://arxiv.org/abs/2411.08312
We study the problem of assigning items to agents so as to maximize the \emph{weighted} Nash Social Welfare (NSW) under submodular valuations. The best-known result for the problem is an $O(nw_{\max})$-approximation due to Garg, Husic, Li, Vega, and
Externí odkaz:
http://arxiv.org/abs/2411.02942
Autor:
Zheng, Yujian, Qiu, Yuda, Jin, Leyang, Ma, Chongyang, Huang, Haibin, Zhang, Di, Wan, Pengfei, Han, Xiaoguang
Single-view 3D hair reconstruction is challenging, due to the wide range of shape variations among diverse hairstyles. Current state-of-the-art methods are specialized in recovering un-braided 3D hairs and often take braided styles as their failure c
Externí odkaz:
http://arxiv.org/abs/2409.16863
We consider the hybrid reinforcement learning setting where the agent has access to both offline data and online interactive access. While Reinforcement Learning (RL) research typically assumes offline data contains complete action, reward and transi
Externí odkaz:
http://arxiv.org/abs/2406.07253
Predicting user preferences and sequential dependencies based on historical behavior is the core goal of sequential recommendation. Although attention-based models have shown effectiveness in this field, they often struggle with inference inefficienc
Externí odkaz:
http://arxiv.org/abs/2406.02638
Learning from human preference data has emerged as the dominant paradigm for fine-tuning large language models (LLMs). The two most common families of techniques -- online reinforcement learning (RL) such as Proximal Policy Optimization (PPO) and off
Externí odkaz:
http://arxiv.org/abs/2406.01462