Zobrazeno 1 - 10
of 5 763
pro vyhledávání: '"Lin, Xi"'
Heuristics are commonly used to tackle diverse search and optimization problems. Design heuristics usually require tedious manual crafting with domain knowledge. Recent works have incorporated large language models (LLMs) into automatic heuristic sea
Externí odkaz:
http://arxiv.org/abs/2409.16867
Effective retinal vessel segmentation requires a sophisticated integration of global contextual awareness and local vessel continuity. To address this challenge, we propose the Graph Capsule Convolution Network (GCC-UNet), which merges capsule convol
Externí odkaz:
http://arxiv.org/abs/2409.11508
Multiobjective optimization problems (MOPs) are prevalent in machine learning, with applications in multi-task learning, learning under fairness or robustness constraints, etc. Instead of reducing multiple objective functions into a scalar objective,
Externí odkaz:
http://arxiv.org/abs/2409.02969
Effective activation functions introduce non-linear transformations, providing neural networks with stronger fitting capa-bilities, which help them better adapt to real data distributions. Huawei Noah's Lab believes that dynamic activation functions
Externí odkaz:
http://arxiv.org/abs/2409.08283
Video generation models hold substantial potential in areas such as filmmaking. However, current video diffusion models need high computational costs and produce suboptimal results due to high complexity of video generation task. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2408.13423
We introduce a novel framework to financial time series forecasting that leverages causality-inspired models to balance the trade-off between invariance to distributional changes and minimization of prediction errors. To the best of our knowledge, th
Externí odkaz:
http://arxiv.org/abs/2408.09960
Autor:
Lin, Xi Victoria, Shrivastava, Akshat, Luo, Liang, Iyer, Srinivasan, Lewis, Mike, Ghosh, Gargi, Zettlemoyer, Luke, Aghajanyan, Armen
We introduce MoMa, a novel modality-aware mixture-of-experts (MoE) architecture designed for pre-training mixed-modal, early-fusion language models. MoMa processes images and text in arbitrary sequences by dividing expert modules into modality-specif
Externí odkaz:
http://arxiv.org/abs/2407.21770
Automated heuristic design (AHD) has gained considerable attention for its potential to automate the development of effective heuristics. The recent advent of large language models (LLMs) has paved a new avenue for AHD, with initial efforts focusing
Externí odkaz:
http://arxiv.org/abs/2407.10873
The integration of real-world data (RWD) and randomized controlled trials (RCT) is increasingly important for advancing causal inference in scientific research. This combination holds great promise for enhancing the efficiency of causal effect estima
Externí odkaz:
http://arxiv.org/abs/2407.01186
In the era of 5G and beyond, the increasing complexity of wireless networks necessitates innovative frameworks for efficient management and deployment. Digital twins (DTs), embodying real-time monitoring, predictive configurations, and enhanced decis
Externí odkaz:
http://arxiv.org/abs/2407.01917