Author is also affiliated with RaySearch Laboratories. Here is an incomplete list. Our proof consist of two parts. There are two cases to consider. There are two cases to consider.

Most large-scale network models use neurons with static nonlinearities that produce analog output, despite the fact that information processing in the brain is predominantly carried out by dynamic neurons that produce discrete pulses called spikes. Research in spike-based computation has been impeded by the lack of efficient supervised learning algorithm for spiking neural networks. Here, we present a gradient descent method for optimizing spiking network models by introducing a differentiable formulation of spiking dynamics and deriving the exact gradient calculation. For demonstration, we trained recurrent spiking networks on two dynamic tasks: one that requires optimizing fast (~ millisecond) spike-based interactions for efficient encoding of information, and a delayed-memory task over extended duration (~ second). The results show that the gradient descent approach indeed optimizes networks dynamics on the time scale of individual spikes as well as on behavioral time scales. In conclusion, our method yields a general purpose supervised learning algorithm for spiking neural networks, which can facilitate further investigations on spike-based computations.

Learning a decision tree from data is a difficult optimization problem. The most widespread algorithm in practice, dating to the 1980s, is based on a greedy growth of the tree structure by recursively splitting nodes, and possibly pruning back the final tree. The parameters (decision function) of an internal node are approximately estimated by minimizing an impurity measure. We give an algorithm that, given an input tree (its structure and the parameter values at its nodes), produces a new tree with the same or smaller structure but new parameter values that provably lower or leave unchanged the misclassification error. This can be applied to both axis-aligned and oblique trees and our experiments show it consistently outperforms various other algorithms while being highly scalable to large datasets and trees. Further, the same algorithm can handle a sparsity penalty, so it can learn sparse oblique trees, having a structure that is a subset of the original tree and few nonzero parameters. This combines the best of axis-aligned and oblique trees: flexibility to model correlated data, low generalization error, fast inference and interpretable nodes that involve only a few features in their decision.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Hedonic Games (HGs) are a classical framework modeling coalition formation of strategic agents guided by their individual preferences.

But the only native stork found in the U.S. is not out of the woods just yet. Breakthroughs, discoveries, and DIY tips sent six days a week. After over 40 years of recovery efforts, one population of the wood stork ()is being removed from the federal list of endangered and threatened wildlife. The large birds are as tall as 45 inches with wingspans that can reach 65 inches and are the only native storks in the United States. They are primarily found in the southeastern United States, where they feed on fish.

Samsung Galaxy Unpacked 2026 is Feb. 25 Russia's recent blocking of Telegram is reportedly disrupting its military operations in Ukraine Telegram is among a number of Western apps banned by Russian authorities. A decision to ban Telegram on home soil may have backfired on the Kremlin. Last week, Russia went on a, banning a number of Western apps in an effort to push domestic users towards Max, an unencrypted state-owned app. One of the restricted apps was WhatsApp (which was also blocked) rival Telegram, a move that drew rare internal from soldiers and pro-war bloggers, with the army being heavily reliant on the cloud-based messaging service for communications. As reported by, pro-Russian military channels are now complaining that the sudden Telegram blackout -- coupled with Elon Musk Russia's access to Starlink earlier this month -- is now actively harming frontline operations.