분류 전체보기21 XLNet = BERT + AR, in Permutation Setting (A quick note) Cheers! Here's to myself who recently got accepted by Carnegie Mellon University; School of Computer Science, Master of Science in Intelligent Information Systems for Fall 2024. XLNet: Generalized Autoregressive Pretraining for Language UnderstandingYang et al.2 Jan 2020 Representation Learning for NLPUnsupervised representation learning is known for handling large-scale unlabeled.. 2024. 5. 5. KMMLU: A Korean Benchmark for LLMs KMMLU: Measuring Massive Multitask Language Understanding in Korean Guijin Son, Hanwool Lee, Sungdong Kim, etc. 18 Feb 2024 MMLU There exist various benchmarks for evaluating and understanding the capabilities of Large Language Models (LLMs), such as commonsense reasoning, code generation, and multi-turn conversations. Massive Multitask Language Understanding (MMLU) is one of these benchmarks, c.. 2024. 3. 29. SPIn-NeRF: 3D Inpainting while enforcing viewpoint consistency SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields Mirzaei et al. 15 Mar 2023 A Novel Approach to Inpaint the NeRF Scene While editing 2D images or 3D videos, one of the important tasks is to remove the undesired part (object) from the original scene. This task should be followed by replacing it with a region that is visually plausible and consistent with the.. 2024. 3. 8. NeRF: Exploring the Frontiers of Modern 3D Scene Reconstruction NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T, Barron, Ravi Ramamoorthi, Ren Ng 3 Aug 2020 NeRF; Neural Randiance Field Neural Randiance Field (NeRF) is a sophisticated and novel method that addresses the long-standing problem of view synthesis. Prior to NeRF, there have been a lot of works such as using de.. 2024. 2. 23. 이전 1 2 3 4 ··· 6 다음
