전체 글 썸네일형 리스트형 When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training https://arxiv.org/abs/2411.13476 When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context TrainingExtending context window sizes allows large language models (LLMs) to process longer sequences and handle more complex tasks. Rotary Positional Embedding (RoPE) has become the de facto standard due to its relative positional encoding properties that benefiarxiv.org 초록확장된 컨텍스트 윈도우(con.. 더보기 ChatAnyone: Stylized Real-time Portrait Video Generation with Hierarchical Motion Diffusion Model 취업이 어렵다... 뭐 다들 비슷하겠지만 말이다 https://humanaigc.github.io/chat-anyone/ ChatAnyoneChatAnyone: Stylized Real-time Portrait Video Generation with Hierarchical Motion Diffusion Model.humanaigc.github.io 초록실시간 상호작용이 가능한 비디오 챗(video-chat) 기반의 초상화 생성은, 최근 텍스트 및 음성 기반 챗 기술의 급속한 발전으로 인해 차세대 기술 트렌드로 주목받고 있다. 그러나 기존의 방법들은 주로 실시간으로 머리의 움직임을 생성하는 데 집중하고 있으며, 머리의 움직임과 일치하는 신체 동작을 자연스럽게 생성하는 데 어려움을 겪고 있다. 또한 말하는 .. 더보기 MusicInfuser: Making Video Diffusion Listen and Dance https://susunghong.github.io/MusicInfuser/ MusicInfuser: Making Video Diffusion Listen and DanceMusicInfuser: Making Video Diffusion Listen and Dance Please turn on your audio! 🔈 Comparison with Prior Work MusicInfuser infuses listening capability into the text-to-video model (Mochi) and produces dancing videos while preserving prompt adherence. Ysusunghong.github.io 본 논문에서는 지정된 음악 트랙과 동기화된 고품질.. 더보기 Transformers without Normalization https://arxiv.org/abs/2503.10622?_bhlid=1a87c33b8185a942533ee1886e23e7f6c2d5f90d Transformers without NormalizationNormalization layers are ubiquitous in modern neural networks and have long been considered essential. This work demonstrates that Transformers without normalization can achieve the same or better performance using a remarkably simple technique. We introduarxiv.org 정규화(Normalization.. 더보기 4SUM problem https://leetcode.com/problems/4sum/description/ class Solution: def fourSum(self, nums: List[int], target: int) -> List[List[int]]: nums.sort() # 결과 중복 제거를 위해 정렬 n = len(nums) answer = [] if n 0 and nums[i] == nums[i-1]: continue for j in range(i + 1, n - 2): # 중복된 두 번째 요소 건너뛰기 if j > i + 1 .. 더보기 14. Longest Common Prefix https://leetcode.com/problemset/ Write a function to find the longest common prefix string amongst an array of strings.If there is no common prefix, return an empty string "". Example 1:Input: strs = ["flower","flow","flight"]Output: "fl"Example 2:Input: strs = ["dog","racecar","car"]Output: ""Explanation: There is no common prefix among the input strings. Constraints:1 0 strs[i] consists of onl.. 더보기 LeetCode 75 - Merge Strings Alternately You are given two strings word1 and word2. Merge the strings by adding letters in alternating order, starting with word1. If a string is longer than the other, append the additional letters onto the end of the merged string.Return the merged string. Example 1:Input: word1 = "abc", word2 = "pqr"Output: "apbqcr"Explanation: The merged string will be merged as so:word1: a b cword2: p q .. 더보기 ALEXNET opensoure화 https://spectrum.ieee.org/alexnet-source-code How Did AlexNet Transform AI? Explore the Groundbreaking Source CodeIn a historic move, the Computer History Museum, in partnership with Google, has released the original 2012 source code for AlexNet, the neural network that revolutionized AI. Developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, AlexNet's souspectrum.ieee.org https://gi.. 더보기 이전 1 2 3 4 ··· 63 다음 목록 더보기
