Zobrazeno 1 - 10
of 52 496
pro vyhledávání: '"Lyle A."'
Autor:
Nicolas L. Fernandez, Ziyuan Chen, David E. H. Fuller, Lieke A. van Gijtenbeek, Taylor M. Nye, Julie S. Biteen, Lyle A. Simmons
Publikováno v:
mBio, Vol 14, Iss 1 (2023)
ABSTRACT Bacterial DNA methyltransferases (MTases) function in restriction modification systems, cell cycle control, and the regulation of gene expression. DnmA is a recently described DNA MTase that forms N6-methyladenosine at nonpalindromic 5′-GA
Externí odkaz:
https://doaj.org/article/88805530266c47ca9aa234fe22b36fc5
Autor:
Hinck, Musashi, Holtermann, Carolin, Olson, Matthew Lyle, Schneider, Florian, Yu, Sungduk, Bhiwandiwalla, Anahita, Lauscher, Anne, Tseng, Shaoyen, Lal, Vasudev
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs). Including an image in the query to a LLaVA-style VLM significantly increases the likelihood of the model returning an English response
Externí odkaz:
http://arxiv.org/abs/2407.02333
Autor:
Lyle, Clare, Zheng, Zeyu, Khetarpal, Khimya, Martens, James, van Hasselt, Hado, Pascanu, Razvan, Dabney, Will
Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature, with several works highlighting diverse benefits such as improving loss landscape conditioning and combatting overestim
Externí odkaz:
http://arxiv.org/abs/2407.01800
Many failures in deep continual and reinforcement learning are associated with increasing magnitudes of the weights, making them hard to change and potentially causing overfitting. While many methods address these learning failures, they often change
Externí odkaz:
http://arxiv.org/abs/2407.01704
Autor:
Giorgi, Salvatore, Liu, Tingting, Aich, Ankit, Isman, Kelsey, Sherman, Garrick, Fried, Zachary, Sedoc, João, Ungar, Lyle H., Curtis, Brenda
Large language models (LLMs) are increasingly being used in human-centered social scientific tasks, such as data annotation, synthetic data creation, and engaging in dialog. However, these tasks are highly subjective and dependent on human factors, s
Externí odkaz:
http://arxiv.org/abs/2406.14462
Solving systems of linear equations is a fundamental problem, but it can be computationally intensive for classical algorithms in high dimensions. Existing quantum algorithms can achieve exponential speedups for the quantum linear system problem (QLS
Externí odkaz:
http://arxiv.org/abs/2406.13879
Large Language Models (LLMs) are increasingly being used in educational and learning applications. Research has demonstrated that controlling for style, to fit the needs of the learner, fosters increased understanding, promotes inclusion, and helps w
Externí odkaz:
http://arxiv.org/abs/2406.12679
Autor:
Havaldar, Shreya, Giorgi, Salvatore, Rai, Sunny, Talhelm, Thomas, Guntuku, Sharath Chandra, Ungar, Lyle
Cultural variation exists between nations (e.g., the United States vs. China), but also within regions (e.g., California vs. Texas, Los Angeles vs. San Francisco). Measuring this regional cultural variation can illuminate how and why people think and
Externí odkaz:
http://arxiv.org/abs/2406.11622
Autor:
Khetarpal, Khimya, Guo, Zhaohan Daniel, Pires, Bernardo Avila, Tang, Yunhao, Lyle, Clare, Rowland, Mark, Heess, Nicolas, Borsa, Diana, Guez, Arthur, Dabney, Will
Learning a good representation is a crucial challenge for Reinforcement Learning (RL) agents. Self-predictive learning provides means to jointly learn a latent representation and dynamics model by bootstrapping from future latent representations (BYO
Externí odkaz:
http://arxiv.org/abs/2406.02035
Understanding the process of learning in neural networks is crucial for improving their performance and interpreting their behavior. This can be approximately understood by asking how a model's output is influenced when we fine-tune on a new training
Externí odkaz:
http://arxiv.org/abs/2406.00509