2,013 research outputs found
Retrospective Higher-Order Markov Processes for User Trails
Users form information trails as they browse the web, checkin with a
geolocation, rate items, or consume media. A common problem is to predict what
a user might do next for the purposes of guidance, recommendation, or
prefetching. First-order and higher-order Markov chains have been widely used
methods to study such sequences of data. First-order Markov chains are easy to
estimate, but lack accuracy when history matters. Higher-order Markov chains,
in contrast, have too many parameters and suffer from overfitting the training
data. Fitting these parameters with regularization and smoothing only offers
mild improvements. In this paper we propose the retrospective higher-order
Markov process (RHOMP) as a low-parameter model for such sequences. This model
is a special case of a higher-order Markov chain where the transitions depend
retrospectively on a single history state instead of an arbitrary combination
of history states. There are two immediate computational advantages: the number
of parameters is linear in the order of the Markov chain and the model can be
fit to large state spaces. Furthermore, by providing a specific structure to
the higher-order chain, RHOMPs improve the model accuracy by efficiently
utilizing history states without risks of overfitting the data. We demonstrate
how to estimate a RHOMP from data and we demonstrate the effectiveness of our
method on various real application datasets spanning geolocation data, review
sequences, and business locations. The RHOMP model uniformly outperforms
higher-order Markov chains, Kneser-Ney regularization, and tensor
factorizations in terms of prediction accuracy
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Exploiting the causal tensor network structure of quantum processes to efficiently simulate non-Markovian path integrals
In the path integral formulation of the evolution of an open quantum system
coupled to a Gaussian, non-interacting environment, the dynamical contribution
of the latter is encoded in an object called the influence functional. Here, we
relate the influence functional to the process tensor -- a more general
representation of a quantum stochastic process -- describing the evolution. We
then use this connection to motivate a tensor network algorithm for the
simulation of multi-time correlations in open systems, building on recent work
where the influence functional is represented in terms of time evolving matrix
product operators. By exploiting the symmetries of the influence functional, we
are able to use our algorithm to achieve orders-of-magnitude improvement in the
efficiency of the resulting numerical simulation. Our improved algorithm is
then applied to compute exact phonon emission spectra for the spin-boson model
with strong coupling, demonstrating a significant divergence from spectra
derived under commonly used assumptions of memorylessness.Comment: 6+5 pages, 4 figure
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Quantum-enhanced reinforcement learning
Dissertação de mestrado em Engenharia FísicaThe field of Artificial Intelligence has lately witnessed extraordinary results. The ability to
design a system capable of beating the world champion of Go, an ancient Chinese game
known as the holy grail of AI, caused a spark worldwide, making people believe that some thing revolutionary is about to happen. A different flavor of learning called Reinforcement
Learning is at the core of this revolution. In parallel, we are witnessing the emergence of a
new field, that of Quantum Machine Learning which has already shown promising results in
supervised/unsupervised learning. In this dissertation, we reach for the interplay between
Quantum Computing and Reinforcement Learning.
This learning by interaction was made possible in the quantum setting using the con cept of oraculization of task environments suggested by Dunjko in 2015. In this dissertation,
we extended the oracular instances previously suggested to work in more general stochastic
environments. On top of this quantum agent-environment paradigm we developed a novel
quantum algorithm for near-optimal decision-making based on the Reinforcement Learn ing paradigm known as Sparse Sampling, obtaining a quantum speedup compared to the
classical counterpart. The achievement was a quantum algorithm that exhibits a complexity
independent on the number of states of the environment. This independence guarantees its
suitability for dealing with large state spaces where planning may be inapplicable.
The most important open questions remain whether it is possible to improve the orac ular instances of task environments to deal with even more general environments, especially
the ability to represent negative rewards as a natural mechanism for negative feedback
instead of some normalization of the reward and the extension of the algorithm to perform
an informed tree-based search instead of the uninformed search proposed. Improvements
on this result would allow the comparison between the algorithm and more recent classical
Reinforcement Learning algorithms.O campo da Inteligência Artificial tem tido resultados extraordinários ultimamente, a capacidade de projetar um sistema capaz de vencer o campeão mundial de Go, um antigo jogo de origem Chinesa, conhecido como o santo graal da IA, causou uma faísca em todo o mundo, fazendo as pessoas acreditarem em que algo revolucionário estar a para acontecer. Um tipo diferente de aprendizagem, chamada Aprendizagem por Reforço está no cerne dessa revolução. Em paralelo surge também um novo campo, o da Aprendizagem Máquina Quântica, que já vem apresentando resultados promissores na aprendizagem supervisionada/não, supervisionada. Nesta dissertação, procuramos invés a interação entre Computação Quântica e a Aprendizagem por Reforço.
Esta interação entre agente e Ambiente foi possível no cenário quântico usando o conceito de oraculização de ambientes sugerido por Dunjko em 2015. Neste trabalho, estendemos as instâncias oraculares sugeridas anteriormente para trabalhar em ambientes estocásticos generalizados. Tendo em conta este paradigma quântico agente-ambiente, desenvolvemos um novo algoritmo quântico para tomada de decisão aproximadamente ótima com base no paradigma da Aprendizagem por Reforço conhecido como Amostragem Esparsa, obtendo uma aceleração quântica em comparação com o caso clássico que possibilitou a obtenção de um algoritmo quântico que exibe uma complexidade independente do número de estados do ambiente. Esta independência garante a sua adaptação para ambientes com um grande espaço de estados em que o planeamento pode ser intratável.
As questões mais pertinentes que se colocam é se é possível melhorar as instâncias oraculares de ambientes para lidar com ambientes ainda mais gerais, especialmente a capacidade de exprimir recompensas negativas como um mecanismo natural para feedback negativo em vez de alguma normalização da recompensa. Além disso, a extensão do algoritmo para realizar uma procura em árvore informada ao invés da procura não informada proposta. Melhorias neste resultado permitiriam a comparação entre o algoritmo quântico e os algoritmos clássicos mais recentes da Aprendizagem por Reforço
Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method
For decades, people are developing efficient numerical methods for solving
the challenging quantum many-body problem, whose Hilbert space grows
exponentially with the size of the problem. However, this journey is far from
over, as previous methods all have serious limitations. The recently developed
deep learning methods provide a very promising new route to solve the
long-standing quantum many-body problems. We report that a deep learning based
simulation protocol can achieve the solution with state-of-the-art precision in
the Hilbert space as large as for spin system and for
fermion system , using a HPC-AI hybrid framework on the new Sunway
supercomputer. With highly scalability up to 40 million heterogeneous cores,
our applications have measured 94% weak scaling efficiency and 72% strong
scaling efficiency. The accomplishment of this work opens the door to simulate
spin models and Fermion models on unprecedented lattice size with extreme high
precision.Comment: Massive ground state optimizations of CNN-based wave-functions for
- model and - model carried out on a heterogeneous cores
supercompute
- …