Zobrazeno 61 - 70
of 52 710
pro vyhledávání: '"Lyle A."'
Generative models have demonstrated impressive results in vision, language, and speech. However, even with massive datasets, they struggle with precision, generating physically invalid or factually incorrect data. This is particularly problematic whe
Externí odkaz:
http://arxiv.org/abs/2306.15166
Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data. While being a flexible framework, where th
Externí odkaz:
http://arxiv.org/abs/2306.11201
Networks with different levels of interactions, including multilayer and multiplex networks, can display a rich diversity of dynamical behaviors and can be used to model and study a wide range of systems. Despite numerous efforts to investigate these
Externí odkaz:
http://arxiv.org/abs/2306.06532
Meaningfully comparing language models is challenging with current explanation methods. Current explanations are overwhelming for humans due to large vocabularies or incomparable across models. We present TopEx, an explanation method that enables a l
Externí odkaz:
http://arxiv.org/abs/2306.00976
We study the problem of temporal-difference-based policy evaluation in reinforcement learning. In particular, we analyse the use of a distributional reinforcement learning algorithm, quantile temporal-difference learning (QTD), for this task. We reac
Externí odkaz:
http://arxiv.org/abs/2305.18388
Autor:
Giorgi, Salvatore, Havaldar, Shreya, Ahmed, Farhan, Akhtar, Zuhaib, Vaidya, Shalaka, Pan, Gary, Ungar, Lyle H., Schwartz, H. Andrew, Sedoc, Joao
We present metrics for evaluating dialog systems through a psychologically-grounded "human" lens in which conversational agents express a diversity of both states (e.g., emotion) and traits (e.g., personality), just as people do. We present five inte
Externí odkaz:
http://arxiv.org/abs/2305.14757
Autor:
Nikishin, Evgenii, Oh, Junhyuk, Ostrovski, Georg, Lyle, Clare, Pascanu, Razvan, Dabney, Will, Barreto, André
A growing body of evidence suggests that neural networks employed in deep reinforcement learning (RL) gradually lose their plasticity, the ability to learn from new data; however, the analysis and mitigation of this phenomenon is hampered by the comp
Externí odkaz:
http://arxiv.org/abs/2305.15555
Designers may often ask themselves how to adjust their design concepts to achieve demanding functional goals. To answer such questions, designers must often consider counterfactuals, weighing design alternatives and their projected performance. This
Externí odkaz:
http://arxiv.org/abs/2305.11308
Photothermal conversion efficiency ({\eta}) plays a crucial role in selecting suitable gold nanoparticles for photothermal therapeutic applications. The photothermal efficiency depends on the material used for the nanoparticles as well as their vario
Externí odkaz:
http://arxiv.org/abs/2305.07203
Autor:
Rossi, Kalel L., Budzinski, Roberto C., Medeiros, Everton S., Boaretto, Bruno R. R., Muller, Lyle, Feudel, Ulrike
Metastability, characterized by a variability of regimes in time, is a ubiquitous type of neural dynamics. It has been formulated in many different ways in the neuroscience literature, however, which may cause some confusion. In this Perspective, we
Externí odkaz:
http://arxiv.org/abs/2305.05328