We consider learning the structures of Gaussian latent tree models with vector observations when a subset of them are arbitrarily corrupted. First, we present the sample complexities of Recursive Grouping (RG) and Chow-Liu Recursive Grouping (CLRG) without the assumption that the effective depth is bounded in the number of observed nodes, significantly generalizing the results in Choi et al. (2011). We show that Chow-Liu initialization in CLRG greatly reduces the sample complexity of RG from being exponential in the diameter of the tree to only logarithmic in the diameter for the hidden Markov model (HMM).

The company will add up to $40 billion to its recent investments in the AI startup. Google plans to invest up to $40 billion into Anthropic in what could be viewed as a circular deal with the AI startup (and frequent competitor), reports . The search giant has invested in Anthropic at multiple points in the past, but this new investment comes after an announcement that the AI startup had signed a joint agreement with Google and Broadcom for multiple gigawatts of next-generation TPU capacity. According to Anthropic, Google is committing $10 billion now at the company's current valuation, with an additional $30 billion on offer if Anthropic meets specific performance milestones. Through Anthropic's existing commitment to use Google's TPUs (tensor processing units) and servers, Anthropic says Google will also provide 5 gigawatts of computing capacity in 2027.

The section explains detailed CipherNav network architecture in Table 4, 5 and 6. The view encoder E is shown in Table 4 and map encoder E is shown in Table 5. The encoders are trained end-to-end during plaintext training and freezed during ciphertext training. Each party has a copy of the encoder models and locally computes all forward passes in ciphertext training. The action classification network Gis shown in Table 6.