Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Kilcher, Yannic"'
Autor:
Köpf, Andreas, Kilcher, Yannic, von Rütte, Dimitri, Anagnostidis, Sotiris, Tam, Zhi-Rui, Stevens, Keith, Barhoum, Abdullah, Duc, Nguyen Minh, Stanley, Oliver, Nagyfi, Richárd, ES, Shahul, Suri, Sameer, Glushkov, David, Dantuluri, Arnav, Maguire, Andrew, Schuhmann, Christoph, Nguyen, Huu, Mattick, Alexander
Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniques such as supervised fine-tuning (SFT) and reinforcement learning fr
Externí odkaz:
http://arxiv.org/abs/2304.07327
Generating music with deep neural networks has been an area of active research in recent years. While the quality of generated samples has been steadily increasing, most methods are only able to exert minimal control over the generated sequence, if a
Externí odkaz:
http://arxiv.org/abs/2201.10936
Autor:
Adolphs, Leonard, Boerschinger, Benjamin, Buck, Christian, Huebscher, Michelle Chen, Ciaramita, Massimiliano, Espeholt, Lasse, Hofmann, Thomas, Kilcher, Yannic, Rothe, Sascha, Sessa, Pier Giuseppe, Saralegui, Lierni Sestorain
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks. Our approach uses machine reading to guide the selection of refinement terms from aggregated
Externí odkaz:
http://arxiv.org/abs/2109.00527
Many applications in machine learning can be framed as minimization problems and solved efficiently using gradient-based techniques. However, recent applications of generative models, particularly GANs, have triggered interest in solving min-max game
Externí odkaz:
http://arxiv.org/abs/2103.12685
Benford's Law (BL) or the Significant Digit Law defines the probability distribution of the first digit of numerical values in a data sample. This Law is observed in many naturally occurring datasets. It can be seen as a measure of naturalness of a g
Externí odkaz:
http://arxiv.org/abs/2102.03313
Autor:
Borschinger, Benjamin, Boyd-Graber, Jordan, Buck, Christian, Bulian, Jannis, Ciaramita, Massimiliano, Huebscher, Michelle Chen, Gajewski, Wojciech, Kilcher, Yannic, Nogueira, Rodrigo, Saralegu, Lierni Sestorain
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment. The environment encapsulates a competitive machine reader based on BER
Externí odkaz:
http://arxiv.org/abs/1911.04156
We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks. Specifically, we prove that $\ell_p$-norm constrained projected gradient ascent based adversarial training with an $\ell_q$-norm l
Externí odkaz:
http://arxiv.org/abs/1906.01527
We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack. These statistics can be easily computed and calibrated by randomly corrupting inputs. They
Externí odkaz:
http://arxiv.org/abs/1902.04818
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Autor:
Kilcher, Yannic, Hofmann, Thomas
Black-Box attacks on machine learning models occur when an attacker, despite having no access to the inner workings of a model, can successfully craft an attack by means of model theft. The attacker will train an own substitute model that mimics the
Externí odkaz:
http://arxiv.org/abs/1711.05475
It is commonly agreed that the use of relevant invariances as a good statistical bias is important in machine-learning. However, most approaches that explicitly incorporate invariances into a model architecture only make use of very simple transforma
Externí odkaz:
http://arxiv.org/abs/1710.11386