The recent surge in fuel prices due to the war in Iran has spurred demand for electric vehicles around the world, and Chinese car makers are making the most of the opportunity. China is the world's top producer of EVs, and while its manufacturers remain largely shut out of the major car market of the United States, they are benefiting from an uptick in interest and orders via dealerships across Asia and elsewhere. BYD, which overtook Tesla as the world's largest seller of electric vehicles last year and is expanding aggressively overseas, is at the centre of this shift in focus. We survive and are successful without the US market today, BYD executive vice president Stella Li told the BBC at the Beijing Auto Show. Instead of aiming for US customers, the company says its challenge is meeting increased demand in other regions, including Brazil, the UK and Europe.

Word meaning may change over time as a reflection of changes in human society. Therefore, modeling time in word representation is necessary for some diachronic tasks. Most existing diachronic word representation approaches train the embeddings separately for each pre-grouped time-stamped corpus and align these embeddings, e.g., by orthogonal projections, vector initialization, temporal referencing, and compass. However, not only does word meaning change in a short time, word meaning may also be subject to evolution over long timespans, thus resulting in a unified continuous process. A recent approach called'DiffTime' models semantic evolution as functions parameterized by multiple-layer nonlinear neural networks over time. In this paper, we will carry on this line of work by learning explicit functions over time for each word. Our approach, called'Word2Fun', reduces the space complexity from O(TVD) to O(kVD) where kis a small constant (k T). In particular, a specific instance based on polynomial functions could provably approximate any function modeling word evolution with a given negligible error thanks to the Weierstrass Approximation Theorem. The effectiveness of the proposed approach is evaluated in diverse tasks including timeaware word clustering, temporal analogy, and semantic change detection.

Owner of US tech giant reveals breach of one of world's most powerful AI models. Reports of unauthorised access to one of the most powerful Artificial Intelligence models yet developed have emerged. Nothing malicious, say the owners - but it has intensified focus on such technology falling into the wrong hands. So, how is AI being controlled globally? Will complex EU loan deal intensify conflict?

Modern deep neural networks (DNNs) are extremely powerful; however, this comes at the price of increased depth and having more parameters per layer, making their training and inference more computationally challenging. In an attempt to address this key limitation, efforts have been devoted to the compression (e.g., sparsification and/or quantization) of these large-scale machine learning models, so that they can be deployed on low-power IoT devices. In this paper, building upon recent advances in neural tangent kernel (NTK) and random matrix theory (RMT), we provide a novel compression approach to wide and fully-connected deep neural nets. Specifically, we demonstrate that in the high-dimensional regime where the number of data points n and their dimension p are both large, and under a Gaussian mixture model for the data, there exists asymptotic spectral equivalence between the NTK matrices for a large family of DNN models. This theoretical result enables "lossless" compression of a given DNN to be performed, in the sense that the compressed network yields asymptotically the same NTK as the original (dense and unquantized) network, with its weights and activations taking values only in {0, 1} up to a scaling.

We develop a framework for generalized variational inference in infinitedimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI). GWI leverages the Wasserstein distance between Gaussian measures on the Hilbert space of square-integrable functions in order to determine a variational posterior using a tractable optimization criterion. It avoids pathologies arising in standard variational function space inference. An exciting application of GWI is the ability to use deep neural networks in the variational parametrization of GWI, combining their superior predictive performance with the principled uncertainty quantification analogous to that of Gaussian processes. The proposed method obtains state-of-the-art performance on several benchmark datasets.

Graph Neural Networks (GNNs) are limited in their expressive power, struggle with long-range interactions and lack a principled way to model higher-order structures. These problems can be attributed to the strong coupling between the computational graph and the input graph structure. The recently proposed Message Passing Simplicial Networks naturally decouple these elements by performing message passing on the clique complex of the graph. Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs). In this work, we extend recent theoretical results on SCs to regular Cell Complexes, topological objects that flexibly subsume SCs and graphs.