Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Pinchetti, Luca"'
Autor:
Pinchetti, Luca, Qi, Chang, Lokshyn, Oleh, Olivers, Gaspard, Emde, Cornelius, Tang, Mufeng, M'Charrak, Amine, Frieder, Simon, Menzat, Bayar, Bogacz, Rafal, Lukasiewicz, Thomas, Salvatori, Tommaso
In this work, we tackle the problems of efficiency and scalability for predictive coding networks in machine learning. To do so, we first propose a library called PCX, whose focus lies on performance and simplicity, and provides a user-friendly, deep
Externí odkaz:
http://arxiv.org/abs/2407.01163
Recently, there has been extensive research on the capabilities of biologically plausible algorithms. In this work, we show how one of such algorithms, called predictive coding, is able to perform causal inference tasks. First, we show how a simple c
Externí odkaz:
http://arxiv.org/abs/2306.15479
Autor:
Frieder, Simon, Pinchetti, Luca, Chevalier, Alexis, Griffiths, Ryan-Rhys, Salvatori, Tommaso, Lukasiewicz, Thomas, Petersen, Philipp Christian, Berner, Julius
Publikováno v:
NeurIPS 2023 Datasets and Benchmarks
We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In contrast
Externí odkaz:
http://arxiv.org/abs/2301.13867
Autor:
Pinchetti, Luca, Salvatori, Tommaso, Yordanov, Yordan, Millidge, Beren, Song, Yuhang, Lukasiewicz, Thomas
A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation (BP). A prominent example is predictive coding (PC), which is a neuroscience-inspired m
Externí odkaz:
http://arxiv.org/abs/2211.03481
Autor:
Salvatori, Tommaso, Pinchetti, Luca, Millidge, Beren, Song, Yuhang, Bao, Tianyi, Bogacz, Rafal, Lukasiewicz, Thomas
Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagates the error of this prediction back through the network. This process
Externí odkaz:
http://arxiv.org/abs/2201.13180
Bayesian and causal inference are fundamental processes for intelligence. Bayesian inference models observations: what can be inferred about y if we observe a related variable x? Causal inference models interventions: if we directly change x, how wil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37697ebccab27c82d7338b7401e1dc5f
Autor:
Salvatori T; Department of Computer Science, University of Oxford, UK., Pinchetti L; Department of Computer Science, University of Oxford, UK., Millidge B; MRC Brain Network Dynamics Unit, University of Oxford, UK., Song Y; Department of Computer Science, University of Oxford, UK.; MRC Brain Network Dynamics Unit, University of Oxford, UK., Bao T; Department of Computer Science, University of Oxford, UK., Bogacz R; MRC Brain Network Dynamics Unit, University of Oxford, UK., Lukasiewicz T; Institute of Logic and Computation, TU Wien, Austria.; Department of Computer Science, University of Oxford, UK.
Publikováno v:
Advances in neural information processing systems [Adv Neural Inf Process Syst] 2022 Nov; Vol. 35, pp. 38232-38244.
