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In mathematical logic, deep inference names a general idea in structural proof theory that breaks with the classical sequent calculus by generalising the notion of structure to permit inference to occur in contexts of high structural complexity. The term deep inference is generally reserved for proof calculi where the structural complexity is unbounded; in this article we will use non-shallow inference to refer to calculi that have structural complexity greater than the sequent calculus, but not unboundedly so, although this is not at present established terminology.
Deep inference is not important in logic outside of structural proof theory, since the phenomena that lead to the proposal of formal systems with deep inference are all related to the cut-elimination theorem. The first calculus of deep inference was proposed by Kurt Schütte,[1] but the idea did not generate much interest at the time.
Nuel Belnap proposed display logic in an attempt to characterise the essence of structural proof theory. The calculus of structures was proposed in order to give a cut-free characterisation of noncommutative logic. Cirquent calculus was developed as a system of deep inference allowing to explicitly account for the possibility of subcomponent-sharing.
In mathematical logic, deepinference names a general idea in structural proof theory that breaks with the classical sequent calculus by generalising the...
system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable...
mathematical logic, the calculus of structures is a proof calculus with deepinference for studying the structural proof theory of noncommutative logic. The...
execute inference, using OpenVINO Runtime by specifying one of several inference modes. OpenVINO IR is the default format used to run inference. It is...
like deep learning as just one element in a very complicated ensemble of techniques, ranging from the statistical technique of Bayesian inference to deductive...
logicians interested in structural proof theory have proposed calculi with deepinference, for instance display logic, hypersequents, the calculus of structures...
the 1990s for both inference and training. In 2014, Chen et al. proposed DianNao (Chinese for "electric brain"), to accelerate deep neural networks especially...
sequent calculus is a reformulation of the sequent calculus to allow deepinference. Alwen Tiu; Egor Ianovski; Rajeev Goré. "Grammar Logics in Nested Sequent...
Girard. Linear logic Ludics Geometry of interaction Coherent space Deepinference Interaction nets Girard, Jean-Yves. Linear logic, Theoretical Computer...
is a kind of proof calculus in which logical reasoning is expressed by inference rules closely related to the "natural" way of reasoning. This contrasts...
on the x86-64 designed to improve performance on deep learning tasks such as training and inference. DL Boost consists of two sets of features: AVX-512...
Trajectory inference or pseudotemporal ordering is a computational technique used in single-cell transcriptomics to determine the pattern of a dynamic...
In perceptual psychology, unconscious inference (German: unbewusster Schluss), also referred to as unconscious conclusion, is a term coined in 1867 by...
sensory input data. However, unlike DBNs and deep convolutional neural networks, they pursue the inference and training procedure in both directions, bottom-up...
principal novelty of the calculus of structures was its pervasive use of deepinference, which it was argued is necessary for calculi combining commutative...
1970s and 1980s by Belavkin. It is known, however, that System BV, a deepinference fragment of linear logic that is very close to quantum logic, can handle...
autoregressive flow-based models are non-auto-regressive when performing inference, the inference speed is faster than real-time. Meanwhile, Nvidia proposed a flow-based...
environment.' The inference is clearly that, since European countries have already destroyed their environment, Brazil also has the right to do so: deep ecological...
logic was axiomatized by W. Xu. Syntactically, cirquent calculi are deepinference systems with the unique feature of subformula-sharing. This feature...
across a range of tasks but has a particular focus on training and inference of deep neural networks. It was developed by the Google Brain team for Google's...
calculus there is little need to analyse them, but proof calculi of deepinference such as display logic (introduced by Nuel Belnap in 1982) support structural...
February 10, 2020. "microsoft/DeepSpeed". July 10, 2020 – via GitHub. "DeepSpeed: Accelerating large-scale model inference and training via system optimizations...
for training in C++ and Python and with additional support for model inference in C# and Java. TensorFlow: Apache 2.0-licensed Theano-like library with...
decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas. A knowledge base is a body of...
MXNet deep neural network training and inference are now supported within SageMaker. 2018-02-28: SageMaker automatically scales model inference to multiple...
artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal...
machines and Deep belief networks, which however employ different learning algorithms. Thus, the dual use of prediction errors for both inference and learning...
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