Zobrazeno 1 - 10
of 16 786
pro vyhledávání: '"P Ahuja"'
Autor:
He, Yifei, Benhaim, Alon, Patra, Barun, Vaddamanu, Praneetha, Ahuja, Sanchit, Chopra, Parul, Chaudhary, Vishrav, Zhao, Han, Song, Xia
We propose a novel scaling law for general-purpose decoder-only language models (LMs) trained on multilingual data, addressing the problem of balancing languages during multilingual pretraining. A primary challenge in studying multilingual scaling is
Externí odkaz:
http://arxiv.org/abs/2410.12883
Autor:
Gupta, Pranav, Rengarajan, Rishabh, Bankapur, Viren, Mannem, Vedansh, Ahuja, Lakshit, Vijay, Surya, Wang, Kevin
Publikováno v:
Curieux Academic Journal Part 2 Issue 43 (2024), pp. 626-634
Combining LiDAR and Camera-view data has become a common approach for 3D Object Detection. However, previous approaches combine the two input streams at a point-level, throwing away semantic information derived from camera features. In this paper we
Externí odkaz:
http://arxiv.org/abs/2410.11211
In this work, we tackle a challenging and extreme form of subpopulation shift, which is termed compositional shift. Under compositional shifts, some combinations of attributes are totally absent from the training distribution but present in the test
Externí odkaz:
http://arxiv.org/abs/2410.06303
Large language models (LLMs) have been used to generate formal proofs of mathematical theorems in proofs assistants such as Lean. However, we often want to optimize a formal proof with respect to various criteria, depending on its downstream use. For
Externí odkaz:
http://arxiv.org/abs/2410.04753
We present the results obtained from X-ray and optical analysis of the Be/X-ray binary IGR~J06074+2205, focusing on before, during, and after the X-ray outbursts in October and December 2023. The properties of the neutron star in the binary are inves
Externí odkaz:
http://arxiv.org/abs/2410.00747
Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prompts are learned for each class to associate their embeddings with clas
Externí odkaz:
http://arxiv.org/abs/2409.08381
Autor:
Wulfmeier, Markus, Bloesch, Michael, Vieillard, Nino, Ahuja, Arun, Bornschein, Jorg, Huang, Sandy, Sokolov, Artem, Barnes, Matt, Desjardins, Guillaume, Bewley, Alex, Bechtle, Sarah Maria Elisabeth, Springenberg, Jost Tobias, Momchev, Nikola, Bachem, Olivier, Geist, Matthieu, Riedmiller, Martin
The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback (RLHF). The simplicity and scalability of maximum
Externí odkaz:
http://arxiv.org/abs/2409.01369
The healthcare system collects extensive data, encompassing patient administrative information, clinical measurements, and home-monitored health metrics. To support informed decision-making in patient care and treatment management, it is essential to
Externí odkaz:
http://arxiv.org/abs/2409.10537
Autor:
Bovo, Riccardo, Abreu, Steven, Ahuja, Karan, Gonzalez, Eric J, Cheng, Li-Te, Gonzalez-Franco, Mar
XR devices running chat-bots powered by Large Language Models (LLMs) have tremendous potential as always-on agents that can enable much better productivity scenarios. However, screen based chat-bots do not take advantage of the the full-suite of natu
Externí odkaz:
http://arxiv.org/abs/2408.08158
Without adhering to any specific model, we have presented 4 X 4 quark mixing matrix as an extension of the 3 X 3 PDG parametrization of the CKM matrix. Using unitarity constraints as well as the hierarchy among the elements of the 3 X 3 CKM matrix, w
Externí odkaz:
http://arxiv.org/abs/2408.07407