**DocBERT**

Pre-trained language representation models achieve remarkable state of the art across a wide range of tasks in natural language processing. One of the latest advancements is BERT, a deep pre-trained transformer that yields much better results than its predecessors do. Despite its burgeoning popularity, however, BERT has not yet been applied to document classification. This task deserves attention, since it contains a few nuances: first, modeling syntactic structure matters less for document classification than for other problems, such as natural language inference and sentiment classification. Second, documents often have multiple labels across dozens of classes, which is uncharacteristic of the tasks that BERT explores. In this paper, we describe fine-tuning BERT for document classification. We are the first to demonstrate the success of BERT on this task, achieving state of the art across four popular datasets. … **Anonymous Information Delivery (AID)**

We introduce the problem of anonymous information delivery (AID), comprised of $K$ messages, a user, and $N$ servers (each holds $M$ messages) that wish to deliver one out of $K$ messages to the user anonymously, i.e., without revealing the delivered message index to the user. This AID problem may be viewed as the dual of the private information retrieval problem. The information theoretic capacity of AID, $C$, is defined as the maximum number of bits of the desired message that can be anonymously delivered per bit of total communication to the user. For the AID problem with $K$ messages, $N$ servers, $M$ messages stored per server, and $N \geq \lceil \frac{K}{M} \rceil$, we provide an achievable scheme of rate $1/\lceil \frac{K}{M} \rceil$ and an information theoretic converse of rate $M/K$, i.e., the AID capacity satisfies $1/\lceil \frac{K}{M} \rceil \leq C \leq M/K$. This settles the capacity of AID when $\frac{K}{M}$ is an integer. When $\frac{K}{M}$ is not an integer, we show that the converse rate of $M/K$ is achievable if $N \geq \frac{K}{\gcd(K,M)} – (\frac{M}{\gcd(K,M)}-1)(\lfloor \frac{K}{M} \rfloor -1)$, and the achievable rate of $1/\lceil \frac{K}{M} \rceil$ is optimal if $N = \lceil \frac{K}{M} \rceil$. Otherwise if $\lceil \frac{K}{M} \rceil < N < \frac{K}{\gcd(K,M)} – (\frac{M}{\gcd(K,M)}-1)(\lfloor \frac{K}{M} \rfloor -1)$, we give an improved achievable scheme and prove its optimality for several small settings. … **Spatial Broadcast Decoder**

We present a simple neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations. Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-‘coordinate’ channels, and apply a fully convolutional network with 1×1 stride. This provides an architectural prior for dissociating positional from non-positional features in the latent distribution of VAEs, yet without providing any explicit supervision to this effect. We show that this architecture, which we term the Spatial Broadcast decoder, improves disentangling, reconstruction accuracy, and generalization to held-out regions in data space. It provides a particularly dramatic benefit when applied to datasets with small objects. We also emphasize a method for visualizing learned latent spaces that helped us diagnose our models and may prove useful for others aiming to assess data representations. Finally, we show the Spatial Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling techniques and when incorporated improves their performance. … **Multi-Kernel Correntropy (MKC)**

As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths. In both correntropy and mixture correntropy, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multi-kernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum multi-kernel correntropy criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum mixture correntropy criterion (MMCC). …

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