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pro vyhledávání: '"Davidson, Tim"'
Autor:
Davidson, Tim R., Surkov, Viacheslav, Veselovsky, Veniamin, Russo, Giuseppe, West, Robert, Gulcehre, Caglar
A rapidly growing number of applications rely on a small set of closed-source language models (LMs). This dependency might introduce novel security risks if LMs develop self-recognition capabilities. Inspired by human identity verification methods, w
Externí odkaz:
http://arxiv.org/abs/2407.06946
Autor:
Latona, Giuseppe Russo, Ribeiro, Manoel Horta, Davidson, Tim R., Veselovsky, Veniamin, West, Robert
Journals and conferences worry that peer reviews assisted by artificial intelligence (AI), in particular, large language models (LLMs), may negatively influence the validity and fairness of the peer-review system, a cornerstone of modern science. In
Externí odkaz:
http://arxiv.org/abs/2405.02150
Autor:
Davidson, Tim R., Veselovsky, Veniamin, Josifoski, Martin, Peyrard, Maxime, Bosselut, Antoine, Kosinski, Michal, West, Robert
We introduce an approach to evaluate language model (LM) agency using negotiation games. This approach better reflects real-world use cases and addresses some of the shortcomings of alternative LM benchmarks. Negotiation games enable us to study mult
Externí odkaz:
http://arxiv.org/abs/2401.04536
Learning suitable latent representations for observed, high-dimensional data is an important research topic underlying many recent advances in machine learning. While traditionally the Gaussian normal distribution has been the go-to latent parameteri
Externí odkaz:
http://arxiv.org/abs/1910.02912
Reparameterizable densities are an important way to learn probability distributions in a deep learning setting. For many distributions it is possible to create low-variance gradient estimators by utilizing a `reparameterization trick'. Due to the abs
Externí odkaz:
http://arxiv.org/abs/1903.02958
Autor:
Falorsi, Luca, de Haan, Pim, Davidson, Tim R., De Cao, Nicola, Weiler, Maurice, Forré, Patrick, Cohen, Taco S.
The manifold hypothesis states that many kinds of high-dimensional data are concentrated near a low-dimensional manifold. If the topology of this data manifold is non-trivial, a continuous encoder network cannot embed it in a one-to-one manner withou
Externí odkaz:
http://arxiv.org/abs/1807.04689
Publikováno v:
Uncertainty in Artificial Intelligence (UAI). Proceedings of the Thirty-Fourth Conference (2018) 856- 865
The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often lea
Externí odkaz:
http://arxiv.org/abs/1804.00891
This paper presents a method for jointly designing the transmitter-receiver pair in a block-by-block communication system that employs (intra-block) decision feedback detection. We provide closed-form expressions for transmitter-receiver pairs that s
Externí odkaz:
http://arxiv.org/abs/cs/0504015
Akademický článek
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This workshop was prepared and presented for the winter 2023 session of ECE790 Graduate Seminars in Electrical & Computer Engineering. Primary presenters were John Bandler, Nicholas Simard, and Tim Davidson, with featured guests Erin Kiley (MCLA), Me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1154::09889b7a89e68a3f222d78fb525a71a2
http://hdl.handle.net/11375/28722
http://hdl.handle.net/11375/28722