In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: Dawid, Kjrerulff & Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact lo cal computations in junction trees. Utiliz ing the strengths of both methods, such hy brid inference methods has the potential of expanding the class of problems which can be solved under bounded resources as well as solving problems which otherwise resist ex act solutions. The paper provides a detailed description of a particular instance of such a hybrid scheme; namely, combination of ex act inference and Gibbs sampling in discrete Bayesian networks. We argue that this com bination calls for an extension of the usual message passing scheme of ordinary junction trees.

Output: HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees

Input: Consider Input: Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor control models, integrations of both and so on. In general, the robustness and precision of recognition is one of the key problems for object recognition models. In this paper, inspired by features of human recognition process and their biological mechanisms, a new integrated and dynamic framework is proposed to mimic the semantic extraction, concept formation and feature re-selection in human visual processing. The main contributions of the proposed model are as follows: (1) Semantic feature extraction: Local semantic features are learnt from episodic features that are extracted from raw images through a deep neural network; (2) Integrated concept formation: Concepts are formed with local semantic information and structural information learnt through network. (3) Feature re-selection: When ambiguity is detected during recognition process, distinctive features according to the difference between ambiguous candidates are re-selected for recognition. Experimental results on hand-written digits and facial shape dataset show that, compared with other methods, the new proposed model exhibits higher robustness and precision for visual recognition, especially in the condition when input samples are smantic ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science.

Output: A Novel Biologically Mechanism-Based Visual Cognition Model Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity

Input: Consider Input: We study the connection between the highly<lb>non-convex loss function of a simple model of<lb>the fully-connected feed-forward neural net-<lb>work and the Hamiltonian of the spherical<lb>spin-glass model under the assumptions of:<lb>i) variable independence, ii) redundancy in<lb>network parametrization, and iii) uniformity.<lb>These assumptions enable us to explain the<lb>complexity of the fully decoupled neural net-<lb>work through the prism of the results from<lb>the random matrix theory. We show that for<lb>large-size decoupled networks the lowest crit-<lb>ical values of the random loss function are<lb>located in a well-defined narrow band lower-<lb>bounded by the global minimum. Further-<lb>more, they form a layered structure. We<lb>show that the number of local minima out-<lb>side the narrow band diminishes exponen-<lb>tially with the size of the network. We em-<lb>pirically demonstrate that the mathemati-<lb>cal model exhibits similar behavior as the<lb>computer simulations, despite the presence<lb>of high dependencies in real networks. We<lb>conjecture that both simulated annealing and<lb>SGD converge to the band containing the<lb>largest number of critical points, and that<lb>all critical points found there are local min-<lb>ima and correspond to the same high learn-<lb>ing quality measured by the test error. This<lb>emphasizes a major difference between large-<lb>and small-size networks where for the lat-<lb>ter poor quality local minima have non-zero<lb>probability of being recovered. Simultane-<lb>ously we prove that recovering the global<lb>minimum becomes harder as the network size<lb>increases and that it is in practice irrelevant<lb>as global minimum often leads to overfitting.<lb>
Output: The Loss Surface of Multilayer Networks