Zobrazeno 1 - 10
of 1 861
pro vyhledávání: '"Liu, Yuhan"'
In multi-agent systems utilizing Large Language Models (LLMs), communication between agents traditionally relies on natural language. This communication often includes the full context of the query so far, which can introduce significant prefill-phas
Externí odkaz:
http://arxiv.org/abs/2411.02820
A Large-scale Time-aware Agents Simulation for Influencer Selection in Digital Advertising Campaigns
In the digital world, influencers are pivotal as opinion leaders, shaping the views and choices of their influencees. Modern advertising often follows this trend, where marketers choose appropriate influencers for product endorsements, based on thoro
Externí odkaz:
http://arxiv.org/abs/2411.01143
With the growing spread of misinformation online, research has increasingly focused on detecting and tracking fake news. However, an overlooked issue is that fake news does not naturally exist in social networks -- it often originates from distorted
Externí odkaz:
http://arxiv.org/abs/2410.19064
The impact of initial connectivity on learning has been extensively studied in the context of backpropagation-based gradient descent, but it remains largely underexplored in biologically plausible learning settings. Focusing on recurrent neural netwo
Externí odkaz:
http://arxiv.org/abs/2410.11164
Autoregressive models have demonstrated remarkable success in natural language processing. In this work, we design a simple yet effective autoregressive architecture for robotic manipulation tasks. We propose the Chunking Causal Transformer (CCT), wh
Externí odkaz:
http://arxiv.org/abs/2410.03132
In response to the increasing mental health challenges faced by college students, we sought to understand their perspectives on how AI applications, particularly Large Language Models (LLMs), can be leveraged to enhance their mental well-being. Throu
Externí odkaz:
http://arxiv.org/abs/2409.17572
This paper considers the problem of testing and estimation of change point where signals after the change point can be highly irregular, which departs from the existing literature that assumes signals after the change point to be piece-wise constant
Externí odkaz:
http://arxiv.org/abs/2409.08863
Autor:
Liu, Yuhan, Negaharipour, Shahriar
We propose an optimization technique for 3-D underwater object modeling from 2-D forward-scan sonar images at known poses. A key contribution, for objects imaged in the proximity of the sea surface, is to resolve the multipath artifacts due to the ai
Externí odkaz:
http://arxiv.org/abs/2409.06815
Autor:
Chang, Haonan, Boyalakuntla, Kowndinya, Liu, Yuhan, Zhang, Xinyu, Schramm, Liam, Boularias, Abdeslam
Solving storage problem: where objects must be accurately placed into containers with precise orientations and positions, presents a distinct challenge that extends beyond traditional rearrangement tasks. These challenges are primarily due to the nee
Externí odkaz:
http://arxiv.org/abs/2409.00499
Autor:
Liu, Yuhan, Acharya, Jayadev
We study quantum state testing where the goal is to test whether $\rho=\rho_0\in\mathbb{C}^{d\times d}$ or $\|\rho-\rho_0\|_1>\varepsilon$, given $n$ copies of $\rho$ and a known state description $\rho_0$. In practice, not all measurements can be ea
Externí odkaz:
http://arxiv.org/abs/2408.17439