Zobrazeno 11 - 20
of 61 254
pro vyhledávání: '"Kuang, P."'
Autor:
Lotfi, Sanae, Kuang, Yilun, Amos, Brandon, Goldblum, Micah, Finzi, Marc, Wilson, Andrew Gordon
Large language models (LLMs) with billions of parameters excel at predicting the next token in a sequence. Recent work computes non-vacuous compression-based generalization bounds for LLMs, but these bounds are vacuous for large models at the billion
Externí odkaz:
http://arxiv.org/abs/2407.18158
In this study, we explore the proactive ability of LLMs to seek user support, using text-to-SQL generation as a case study. We propose metrics to evaluate the trade-off between performance improvements and user burden, and investigate whether LLMs ca
Externí odkaz:
http://arxiv.org/abs/2407.14767
Causal inference plays an important role in explanatory analysis and decision making across various fields like statistics, marketing, health care, and education. Its main task is to estimate treatment effects and make intervention policies. Traditio
Externí odkaz:
http://arxiv.org/abs/2407.14022
Autor:
Eich, Thomas, Body, Thomas, Faitsch, Michael, Grover, Ondrej, Miller, Marco Andres, Manz, Peter, Looby, Tom, Kuang, Adam Qingyang, Redl, Andreas, Reinke, Matt, Creely, Alex J., Battaglia, Devon, Hillesheim, Jon, Wigram, Mike, Hughes, Jerry W., team, the ASDEX Upgrade
Fusion power plants require ELM-free, detached operation to prevent divertor damage and erosion. The separatrix operational space (SepOS) is proposed as a tool for identifying access to the type-I ELM-free quasi-continuous exhaust regime. In this wor
Externí odkaz:
http://arxiv.org/abs/2407.13539
Trajectory prediction is significant for intelligent vehicles to achieve high-level autonomous driving, and a lot of relevant research achievements have been made recently. Despite the rapid development, most existing studies solely focused on normal
Externí odkaz:
http://arxiv.org/abs/2407.13480
Autor:
Borisyuk, Fedor, Song, Qingquan, Zhou, Mingzhou, Parameswaran, Ganesh, Arun, Madhu, Popuri, Siva, Bingol, Tugrul, Pei, Zhuotao, Lee, Kuang-Hsuan, Zheng, Lu, Shao, Qizhan, Naqvi, Ali, Zhou, Sen, Gupta, Aman
This paper introduces LiNR, LinkedIn's large-scale, GPU-based retrieval system. LiNR supports a billion-sized index on GPU models. We discuss our experiences and challenges in creating scalable, differentiable search indexes using TensorFlow and PyTo
Externí odkaz:
http://arxiv.org/abs/2407.13218
The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. It can be solved in time polynomial in the domain size for sentences from the two-variable f
Externí odkaz:
http://arxiv.org/abs/2407.11877
Autor:
Tseng, Liang-Hsuan, Chen, Zih-Ching, Chang, Wei-Shun, Lee, Cheng-Kuang, Huang, Tsung-Ren, Lee, Hung-yi
Recent advances in automatic speech recognition (ASR) often rely on large speech foundation models for generating high-quality transcriptions. However, these models can be impractical due to limited computing resources. The situation is even more sev
Externí odkaz:
http://arxiv.org/abs/2407.10603
The existing change detection(CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information. However, CD is esse
Externí odkaz:
http://arxiv.org/abs/2407.09874
This study introduces a new approach to addressing positive and unlabeled (PU) data through the double exponential tilting model (DETM). Traditional methods often fall short because they only apply to selected completely at random (SCAR) PU data, whe
Externí odkaz:
http://arxiv.org/abs/2407.09735