Zobrazeno 1 - 10
of 6 233
pro vyhledávání: '"P, Theobald"'
Autor:
Mackraz, Natalie, Sivakumar, Nivedha, Khorshidi, Samira, Patel, Krishna, Theobald, Barry-John, Zappella, Luca, Apostoloff, Nicholas
Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuni
Externí odkaz:
http://arxiv.org/abs/2412.03537
Quint and Shubik (1997) conjectured that a non-degenerate n-by-n game has at most 2^n-1 Nash equilibria in mixed strategies. The conjecture is true for n at most 4 but false for n=6 or larger. We answer it positively for the remaining case n=5, which
Externí odkaz:
http://arxiv.org/abs/2411.12385
Accommodating human preferences is essential for creating AI agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs to infer preferences from user interactions, but they often produce broad and gener
Externí odkaz:
http://arxiv.org/abs/2410.06273
Autor:
Lang, Thomas, Heim, Anja, Dremel, Kilian, Prjamkov, Dimitri, Blaimer, Martin, Firsching, Markus, Papadaki, Anastasia, Kasperl, Stefan, Fuchs, Theobald OJ
Quantum computing is currently gaining significant attention, not only from the academic community but also from industry, due to its potential applications across several fields for addressing complex problems. For any practical problem which may be
Externí odkaz:
http://arxiv.org/abs/2410.00742
Autor:
Devnani, Bhavika, Seto, Skyler, Aldeneh, Zakaria, Toso, Alessandro, Menyaylenko, Elena, Theobald, Barry-John, Sheaffer, Jonathan, Sarabia, Miguel
Humans can picture a sound scene given an imprecise natural language description. For example, it is easy to imagine an acoustic environment given a phrase like "the lion roar came from right behind me!". For a machine to have the same degree of comp
Externí odkaz:
http://arxiv.org/abs/2409.11369
Autor:
Aldeneh, Zakaria, Higuchi, Takuya, Jung, Jee-weon, Chen, Li-Wei, Shum, Stephen, Abdelaziz, Ahmed Hussen, Watanabe, Shinji, Likhomanenko, Tatiana, Theobald, Barry-John
Iterative self-training, or iterative pseudo-labeling (IPL)--using an improved model from the current iteration to provide pseudo-labels for the next iteration--has proven to be a powerful approach to enhance the quality of speaker representations. R
Externí odkaz:
http://arxiv.org/abs/2409.10791
Autor:
Chen, Li-Wei, Higuchi, Takuya, Bai, He, Abdelaziz, Ahmed Hussen, Rudnicky, Alexander, Watanabe, Shinji, Likhomanenko, Tatiana, Theobald, Barry-John, Aldeneh, Zakaria
Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech for various downstream tasks. These models use a masked prediction objective, where the model learns to predict information about masked i
Externí odkaz:
http://arxiv.org/abs/2409.10788
Autor:
Aldeneh, Zakaria, Thilak, Vimal, Higuchi, Takuya, Theobald, Barry-John, Likhomanenko, Tatiana
This study explores using embedding rank as an unsupervised evaluation metric for general-purpose speech encoders trained via self-supervised learning (SSL). Traditionally, assessing the performance of these encoders is resource-intensive and require
Externí odkaz:
http://arxiv.org/abs/2409.10787
Autor:
Lin, Yong, Seto, Skyler, ter Hoeve, Maartje, Metcalf, Katherine, Theobald, Barry-John, Wang, Xuan, Zhang, Yizhe, Huang, Chen, Zhang, Tong
Reinforcement Learning from Human Feedback (RLHF) is an effective approach for aligning language models to human preferences. Central to RLHF is learning a reward function for scoring human preferences. Two main approaches for learning a reward model
Externí odkaz:
http://arxiv.org/abs/2409.03650
Autor:
Gölz, Thorsten, Baù, Enrico, Zhang, Jinhua, Kaltenecker, Korbinian, Trauner, Dirk, Maier, Stefan A., Keilmann, Fritz, Lohmüller, Theobald, Tittl, Andreas
Understanding the biophysical and biochemical properties of molecular nanocarriers under physiological conditions and with minimal interference is crucial for advancing nanomedicine, photopharmacology, drug delivery, nanotheranostics and synthetic bi
Externí odkaz:
http://arxiv.org/abs/2406.02513