In mathematical logic, deep inference names a general idea in structural proof theory that breaks with the classical sequent calculus by generalising the...

mathematical logic, the calculus of structures is a proof calculus with deep inference for studying the structural proof theory of noncommutative logic. The...

system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable...

logicians interested in structural proof theory have proposed calculi with deep inference, for instance display logic, hypersequents, the calculus of structures...

sequent calculus is a reformulation of the sequent calculus to allow deep inference. Alwen Tiu; Egor Ianovski; Rajeev Goré. "Grammar Logics in Nested Sequent...

the 1990s for both inference and training. In 2014, Chen et al. proposed DianNao (Chinese for "electric brain"), to accelerate deep neural networks especially...

complicated. Deep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference. The classic universal...

sensory input data. However, unlike DBNs and deep convolutional neural networks, they pursue the inference and training procedure in both directions, bottom-up...

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...

Girard. Linear logic Ludics Geometry of interaction Coherent space Deep inference Interaction nets Girard, Jean-Yves. Linear logic, Theoretical Computer...

unbounded context, and remain computationally efficient during training and inferencing. Mamba introduces significant enhancements to S4, particularly in its...

principal novelty of the calculus of structures was its pervasive use of deep inference, which it was argued is necessary for calculi combining commutative...

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...

logic was axiomatized by W. Xu. Syntactically, cirquent calculi are deep inference systems with the unique feature of subformula-sharing. This feature...

execute inference, using OpenVINO Runtime by specifying one of several inference modes. OpenVINO IR is the default format used to run inference. It is...

Trajectory inference or pseudotemporal ordering is a computational technique used in single-cell transcriptomics to determine the pattern of a dynamic...

calculus there is little need to analyse them, but proof calculi of deep inference such as display logic (introduced by Nuel Belnap in 1982) support structural...

particular in the subfields of neural networks, Bayesian inference and Bayesian optimization, and deep learning. De Freitas was born in Zimbabwe. He did his...

environment.' The inference is clearly that, since European countries have already destroyed their environment, Brazil also has the right to do so: deep ecological...

1970s and 1980s by Belavkin. It is known, however, that System BV, a deep inference fragment of linear logic that is very close to quantum logic, can handle...

Deductive closure principle Deductive fallacy Deductive reasoning Deep ecology Deep inference Deep structure Deepak Kumar (historian) Default logic Defeasible...

bunches are often applied deep within a tree-context, and not only at the top level: it is thus in a sense a calculus of deep inference. Corresponding to bunched...

In perceptual psychology, unconscious inference (German: unbewusster Schluss), also referred to as unconscious conclusion, is a term coined in 1867 by...

A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention mechanism, proposed in the 2017...

representations to steer model behaviors towards solving downstream tasks at inference time. One specific method within the ReFT family is Low-rank Linear Subspace...

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...

Bayesian programming Causal inference Causal loop diagram Chow–Liu tree Computational intelligence Computational phylogenetics Deep belief network Dempster–Shafer...

learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many...

artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal...

Information Geometry. MaxEnt 2015, the 35th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. arXiv:1512.09076...