Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Weilbach, Christian"'
Inter-neuron communication delays are ubiquitous in physically realized neural networks such as biological neural circuits and neuromorphic hardware. These delays have significant and often disruptive consequences on network dynamics during training
Externí odkaz:
http://arxiv.org/abs/2407.05494
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data. However, current simulation-based amorti
Externí odkaz:
http://arxiv.org/abs/2404.09636
The recent advent of powerful Large-Language Models (LLM) provides a new conversational form of inquiry into historical memory (or, training data, in this case). We show that by augmenting such LLMs with vector embeddings from highly specialized acad
Externí odkaz:
http://arxiv.org/abs/2310.10808
Autor:
Campbell, Andrew, Harvey, William, Weilbach, Christian, De Bortoli, Valentin, Rainforth, Tom, Doucet, Arnaud
We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative process is formulated as a jump diffusion process that makes jumps between
Externí odkaz:
http://arxiv.org/abs/2305.16261
We introduce a framework for automatically defining and learning deep generative models with problem-specific structure. We tackle problem domains that are more traditionally solved by algorithms such as sorting, constraint satisfaction for Sudoku, a
Externí odkaz:
http://arxiv.org/abs/2210.11633
We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments. We introduce a generative model that can at test-time sample any arbitra
Externí odkaz:
http://arxiv.org/abs/2205.11495
Understanding physical phenomena oftentimes means understanding the underlying dynamical system that governs observational measurements. While accurate prediction can be achieved with black box systems, they often lack interpretability and are less a
Externí odkaz:
http://arxiv.org/abs/2107.07345
Autor:
Wood, Frank, Warrington, Andrew, Naderiparizi, Saeid, Weilbach, Christian, Masrani, Vaden, Harvey, William, Scibior, Adam, Beronov, Boyan, Grefenstette, John, Campbell, Duncan, Nasseri, Ali
Publikováno v:
Front Artif Intell. 2021; 4: 550603
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of inference tasks undertaken include computing the posterior distribution
Externí odkaz:
http://arxiv.org/abs/2003.13221
Autor:
Weilbach, Christian, Bieniusa, Annette
In recent years the two trends of edge computing and artificial intelligence became both crucial for information processing infrastructures. While the centralized analysis of massive amounts of data seems to be at odds with computation on the outer e
Externí odkaz:
http://arxiv.org/abs/1710.11057
Replikativ is a replication middleware supporting a new kind of confluent replicated datatype resembling a distributed version control system. It retains the order of write operations at the trade-off of reduced availability with after-the- fact conf
Externí odkaz:
http://arxiv.org/abs/1508.05545