전체 글 썸네일형 리스트형 Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model https://aifactory.space/task/8832/overview?utm_source=pytorchkr&ref=pytorchkr 디스캐닝 모델 체험디스캐닝 모델 체험aifactory.space https://arxiv.org/abs/2402.05350 Descanning: From Scanned to the Original Images with a Color Correction Diffusion ModelA significant volume of analog information, i.e., documents and images, have been digitized in the form of scanned copies for storing, sharing, and/or analyzing in .. 더보기 Traveling Waves Integrate Spatial Information Through Time https://arxiv.org/abs/2502.06034?utm_source=pytorchkr&ref=pytorchkr Traveling Waves Integrate Spatial Information Through TimeTraveling waves of neural activity are widely observed in the brain, but their precise computational function remains unclear. One prominent hypothesis is that they enable the transfer and integration of spatial information across neural populations. Howevarxiv.org 초록뇌에서는.. 더보기 2071. Maximum Number of Tasks You Can Assign from typing import Listimport collectionsimport bisectclass Solution: def maxTaskAssign(self, tasks: List[int], workers: List[int], pills: int, strength: int) -> int: # Sort tasks and workers in ascending order tasks.sort() workers.sort() n, m = len(tasks), len(workers) # Check if we can assign k tasks def can_assign(k): # C.. 더보기 Qwen3: Think Deeper, Act Faster https://github.com/QwenLM/Qwen3 GitHub - QwenLM/Qwen3: Qwen3 is the large language model series developed by Qwen team, Alibaba Cloud.Qwen3 is the large language model series developed by Qwen team, Alibaba Cloud. - QwenLM/Qwen3github.com https://huggingface.co/Qwen/Qwen3-235B-A22B Qwen/Qwen3-235B-A22B · Hugging FaceQwen3-235B-A22B Qwen3 Highlights Qwen3 is the latest generation of large languag.. 더보기 구글 콜랩 국가 가격 미국 $49.99 -> 71274.24원 일본 5767엔 -> 57703.22원 더보기 EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications https://arxiv.org/abs/2206.10589 EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision ApplicationsIn the pursuit of achieving ever-increasing accuracy, large and complex neural networks are usually developed. Such models demand high computational resources and therefore cannot be deployed on edge devices. It is of great interest to build resource-efficarxiv.org 초록†*동등.. 더보기 LLMs can see and hear without any training https://arxiv.org/abs/2501.18096 LLMs can see and hear without any trainingWe present MILS: Multimodal Iterative LLM Solver, a surprisingly simple, training-free approach, to imbue multimodal capabilities into your favorite LLM. Leveraging their innate ability to perform multi-step reasoning, MILS prompts the LLM to generate candarxiv.org 초록우리는 MILS(Multimodal Iterative LLM Solver)를 소개합니다. MILS는.. 더보기 Reasoning Models Can Be Effective Without Thinking https://arxiv.org/abs/2504.09858 Reasoning Models Can Be Effective Without ThinkingRecent LLMs have significantly improved reasoning capabilities, primarily by including an explicit, lengthy Thinking process as part of generation. In this paper, we question whether this explicit thinking is necessary. Using the state-of-the-art DeepSeek-arxiv.org 초록최근의 대형 언어 모델(LLM)은 명시적이고 긴 사고 과정(Thinking proce.. 더보기 이전 1 2 3 4 ··· 66 다음
