Many cognitive models posit mental representations based on discrete substructures. Even connectionist models whose processing involves manipulation of real-valued activations typically represent objects as patterns of 0s and 1s across a set of units (Noelle, Cottrell, and Wilms, 1997). Often, individual units are taken to represent specific features of the objects and two representations will share features to the degree to which the two objects are similar. While this arrangement is intuitively appealing, it can be difficult to construct the features to be used in such a model. Using random feature assignments clouds the relationship between the model and the objects it is intended to represent, diminishing the model's value. As Clouse and Cottrell (1996) point out, hand-crafted representations are tedious to construct and it can be difficult to precisely justify (or even articulate) the principles that guided their design. These difficulties effectively limit the number of objects that can be encoded, constraining modeling efforts to small examples. In this paper, we investigate methods for automatically synthesizing feature-based representations directly from the pairwise object similarities that the model is intended to respect.

A humpback whale caught in a fishing rope off the western coast of Canada stunned rescuers when it pulled off a magnificent maneuver to free itself. A humpback whale entangled in fishing gear off the coast of western Canada was caught on video pulling off a spectacular maneuver to free itself as rescuers worked to help the sea creature. The ocean mammal was caught in the ropes of a buoy used to catch prawn for two days when rescuers caught up with the whale near Texada Island on Oct. 14, Fisheries and Oceans Canada said. The department's Marine Mammal Rescue team was following the distressed whale when its aerial drone spotted two more humpback whales swimming alongside the creature. Before rescuers attempted to cut the rope that was caught in the animal's mouth, they added some drag to slow the whale down, Paul Cottrell, with Fisheries and Oceans Canada, told the BBC.

If an oil spill were to hit B.C.'s southern coast, threatening the local orca population, the Department of Fisheries and Oceans (DFO) could respond in a way that wasn't technologically possible just two years ago, says Paul Cottrell. For years the marine mammal co-ordinator counted on a network of 18 hydrophones – underwater listening devices lining much of Vancouver Island – to detect calls of the endangered southern resident killer whales and track their movements in the Salish Sea. But what if artificial intelligence could be harnessed to automatically detect the calls of that one particular subgroup of orcas around the clock? That was the pitch Google's (Nasdaq:GOOG) artificial-intelligence division made to the DFO at a 2018 workshop in Victoria. "The opportunity to work with such cutting-edge individuals and technology was amazing," Cottrell said.

Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that is understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. The best indicators are selected via a classical forward scheme, leading to a much reduced number of indicators that are tuned to a data set. We illustrate the interest of the method on simulated data which contain realistic early signs of anomalies.

If the promise of computational modeling is to be fully realized in higherlevel cognitivedomains such as language processing, principled methods must be developed to construct the semantic representations used in such models. In this paper, we propose the use of an established formalism from mathematical psychology, additive clustering, as a means of automatically constructingbinary representations for objects using only pairwise similarity data. However, existing methods for the unsupervised learning of additive clustering models do not scale well to large problems. Wepresent a new algorithm for additive clustering, based on a novel heuristic technique for combinatorial optimization. The algorithm is simpler than previous formulations and makes fewer independence assumptions. Extensiveempirical tests on both human and synthetic data suggest that it is more effective than previous methods and that it also scales better to larger problems. By making additive clustering practical, we take a significant step toward scaling connectionist models beyond hand-coded examples.

If the promise of computational modeling is to be fully realized in higherlevel cognitive domains such as language processing, principled methods must be developed to construct the semantic representations used in such models. In this paper, we propose the use of an established formalism from mathematical psychology, additive clustering, as a means of automatically constructing binary representations for objects using only pairwise similarity data. However, existing methods for the unsupervised learning of additive clustering models do not scale well to large problems. We present a new algorithm for additive clustering, based on a novel heuristic technique for combinatorial optimization. The algorithm is simpler than previous formulations and makes fewer independence assumptions. Extensive empirical tests on both human and synthetic data suggest that it is more effective than previous methods and that it also scales better to larger problems. By making additive clustering practical, we take a significant step toward scaling connectionist models beyond hand-coded examples.

If the promise of computational modeling is to be fully realized in higherlevel cognitive domains such as language processing, principled methods must be developed to construct the semantic representations used in such models. In this paper, we propose the use of an established formalism from mathematical psychology, additive clustering, as a means of automatically constructing binary representations for objects using only pairwise similarity data. However, existing methods for the unsupervised learning of additive clustering models do not scale well to large problems. We present a new algorithm for additive clustering, based on a novel heuristic technique for combinatorial optimization. The algorithm is simpler than previous formulations and makes fewer independence assumptions. Extensive empirical tests on both human and synthetic data suggest that it is more effective than previous methods and that it also scales better to larger problems. By making additive clustering practical, we take a significant step toward scaling connectionist models beyond hand-coded examples.

Thishas resulted in t.he building of systems with semantically nameable nodes which perform inferencing by examining t.he pat,t.erns of activation spread.

One att·empt at explaining human inferencing is that of spreading activat,ion, particularly in the st.ructured connectionist paradigm. This has resulted in t.he building of systems with semantically nameable nodes which perform inferencing by examining t.he pat,t.erns of activation spread.

One att·empt at explaining human inferencing is that of spreading activat,ion, particularly in the st.ructured connectionist paradigm. This has resulted in t.he building of systems with semantically nameable nodes which perform inferencing by examining t.he pat,t.erns of activation spread.