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pro vyhledávání: '"Navaneet, KL"'
The goal of this paper is to improve (upcycle) an existing large language model without the prohibitive requirements of continued pre-training of the full-model. The idea is to split the pre-training data into semantically relevant groups and train a
Externí odkaz:
http://arxiv.org/abs/2410.09687
3D Gaussian Splatting (3DGS) is a new method for modeling and rendering 3D radiance fields that achieves much faster learning and rendering time compared to SOTA NeRF methods. However, it comes with a drawback in the much larger storage demand compar
Externí odkaz:
http://arxiv.org/abs/2311.18159
Autor:
Koohpayegani, Soroush Abbasi, Navaneet, KL, Nooralinejad, Parsa, Kolouri, Soheil, Pirsiavash, Hamed
Fine-tuning Large Language Models (LLMs) and storing them for each downstream task or domain is impractical because of the massive model size (e.g., 350GB in GPT-3). Current literature, such as LoRA, showcases the potential of low-rank modifications
Externí odkaz:
http://arxiv.org/abs/2310.02556
Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based
Externí odkaz:
http://arxiv.org/abs/2310.02544
Autor:
Navaneet, KL, Koohpayegani, Soroush Abbasi, Tejankar, Ajinkya, Pourahmadi, Kossar, Subramanya, Akshayvarun, Pirsiavash, Hamed
We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluster images by pulling the embedding of a query image to be closer to its
Externí odkaz:
http://arxiv.org/abs/2112.04607