This sampling process is embodied as the policyπ, which is responsible for outputting an actiona conditioned on past environmental states{s}.

Empirically, the gradual self-training method greatly improves the target domain's accuracy compared

We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-supervisedlearningtasks.

We conduct a systematic comparison of methods in a variety of domains, varying thenumber oflabeled instances available inthetargetdomain (k), as well as the number of target-domain classes.

DiTs (U-DiTs) in the paper and conduct extensive experiments to demonstrate the extraordinary performance of U-DiT models.

A spokesperson for Anthropic declined to comment on whether Claude was used in the operation, but said any use of the tool was required to comply with its policies. A spokesperson for Anthropic declined to comment on whether Claude was used in the operation, but said any use of the tool was required to comply with its policies. US military used Anthropic's AI model Claude in Venezuela raid, report says Wall Street Journal says Claude used in operation via Anthropic's partnership with Palantir Technologies Sat 14 Feb 2026 11.15 ESTFirst published on Sat 14 Feb 2026 10.53 EST Claude, the AI model developed by Anthropic, was used by the US military during its operation to kidnap Nicolás Maduro from Venezuela, the Wall Street Journal revealed on Saturday, a high-profile example of how the US defence department is using artificial intelligence in its operations. The US raid on Venezuela involved bombing across the capital, Caracas, and the killing of 83 people, according to Venezuela's defence ministry. Anthropic's terms of use prohibit the use of Claude for violent ends, for the development of weapons or for conducting surveillance.

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Here, we show how to benefit from the flexibility of RNNs while representing individual differences inalow-dimensional andinterpretable space. Toachievethis, wepropose anovelend-to-end learning frameworkinwhich an encoder istrained to map the behavior of subjects into alow-dimensional latent space.

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