Search Results for author: Andrew Gambardella

Found 6 papers, 2 papers with code

Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks

no code implementations4 Jun 2024 Andrew Gambardella, Yusuke Iwasawa, Yutaka Matsuo

The ability (and inability) of large language models (LLMs) to perform arithmetic tasks has been the subject of much theoretical and practical debate.

Real-World Robot Applications of Foundation Models: A Review

no code implementations8 Feb 2024 Kento Kawaharazuka, Tatsuya Matsushima, Andrew Gambardella, Jiaxian Guo, Chris Paxton, Andy Zeng

This paper provides an overview of the practical application of foundation models in real-world robotics, with a primary emphasis on the replacement of specific components within existing robot systems.

Motion Planning

Detecting and Quantifying Malicious Activity with Simulation-based Inference

no code implementations6 Oct 2021 Andrew Gambardella, Bogdan State, Naeemullah Khan, Leo Tsourides, Philip H. S. Torr, Atılım Güneş Baydin

We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm.

Probabilistic Programming

Simulation-Based Inference for Global Health Decisions

2 code implementations14 May 2020 Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, Atılım Güneş Baydin

The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.

Bayesian Inference Epidemiology

Transflow Learning: Repurposing Flow Models Without Retraining

no code implementations29 Nov 2019 Andrew Gambardella, Atılım Güneş Baydin, Philip H. S. Torr

It is well known that deep generative models have a rich latent space, and that it is possible to smoothly manipulate their outputs by traversing this latent space.

Bayesian Inference Style Transfer

Multitask Soft Option Learning

1 code implementation1 Apr 2019 Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson

We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference.

Transfer Learning

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