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pro vyhledávání: '"Nowak, Robert"'
Modern machine learning models deployed often encounter distribution shifts in real-world applications, manifesting as covariate or semantic out-of-distribution (OOD) shifts. These shifts give rise to challenges in OOD generalization and OOD detectio
Externí odkaz:
http://arxiv.org/abs/2410.08000
Autor:
Zhang, Jifan, Nowak, Robert
Creating specialized large language models requires vast amounts of clean, special purpose data for training and fine-tuning. With only a handful of existing large-scale, domain-specific datasets, creation of new datasets is required in most applicat
Externí odkaz:
http://arxiv.org/abs/2410.02755
Autor:
Zhang, Jifan, Jain, Lalit, Guo, Yang, Chen, Jiayi, Zhou, Kuan Lok, Suresh, Siddharth, Wagenmaker, Andrew, Sievert, Scott, Rogers, Timothy, Jamieson, Kevin, Mankoff, Robert, Nowak, Robert
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing rating data for The New Yorker's weekly cartoon caption contest over
Externí odkaz:
http://arxiv.org/abs/2406.10522
In this paper, we study multi-task structured bandit problem where the goal is to learn a near-optimal algorithm that minimizes cumulative regret. The tasks share a common structure and the algorithm exploits the shared structure to minimize the cumu
Externí odkaz:
http://arxiv.org/abs/2406.05064
In this paper, we study safe data collection for the purpose of policy evaluation in tabular Markov decision processes (MDPs). In policy evaluation, we are given a \textit{target} policy and asked to estimate the expected cumulative reward it will ob
Externí odkaz:
http://arxiv.org/abs/2406.02165
Motivated by the growing theoretical understanding of neural networks that employ the Rectified Linear Unit (ReLU) as their activation function, we revisit the use of ReLU activation functions for learning implicit neural representations (INRs). Insp
Externí odkaz:
http://arxiv.org/abs/2406.02529
Learning a good history representation is one of the core challenges of reinforcement learning (RL) in partially observable environments. Recent works have shown the advantages of various auxiliary tasks for facilitating representation learning. Howe
Externí odkaz:
http://arxiv.org/abs/2402.07102
Autor:
Soltani, Nasim, Zhang, Jifan, Salehi, Batool, Roy, Debashri, Nowak, Robert, Chowdhury, Kaushik
Collecting an over-the-air wireless communications training dataset for deep learning-based communication tasks is relatively simple. However, labeling the dataset requires expert involvement and domain knowledge, may involve private intellectual pro
Externí odkaz:
http://arxiv.org/abs/2402.04896
Autor:
Bhatt, Gantavya, Chen, Yifang, Das, Arnav M., Zhang, Jifan, Truong, Sang T., Mussmann, Stephen, Zhu, Yinglun, Bilmes, Jeffrey, Du, Simon S., Jamieson, Kevin, Ash, Jordan T., Nowak, Robert D.
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language models (LLMs). However, the annotation efforts required to produce high
Externí odkaz:
http://arxiv.org/abs/2401.06692
Autor:
Górniak, Daniel, Nowak, Robert
De Bruijn graph is one of the most important data structures used in de-novo genome assembly algorithms, especially for NGS data. There is a growing need for parallel data structures and algorithms due to the increasing number of cores in modern comp
Externí odkaz:
http://arxiv.org/abs/2401.02756