An approach is proposed to quantify, in bits of information, the actual relevance of analogies in analogy tests. The main component of this approach is a softaccuracy estimator that also yields entropy estimates with compensated biases. Experimental results obtained with pre-trained GloVe 300-D vectors and two public analogy test sets show that proximity hints are much more relevant than analogies in analogy tests, from an information content perspective. Accordingly, a simple word embedding model is used to predict that analogies carry about one bit of information, which is experimentally corroborated.

The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled data covering two different languages. To address this issue, we study a simple and effective approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis, either in a pipeline approach, or to generate pseudo-labels for training end-to-end speech translation models. Furthermore, we present an unsupervised domain adaptation technique for pre-trained speech models which improves the performance of downstream unsupervised speech recognition, especially for low-resource settings. Experiments show that unsupervised speech-to-text translation outperforms the previous unsupervised state of the art by 3.2 BLEU on the Libri-Trans benchmark, on CoVoST 2, our best systems outperform the best supervised end-to-end models (without pre-training) from only two years ago by an average of 5.0 BLEU over five X-En directions. We also report competitive results on MuST-C and CVSS benchmarks.

Generalized Additive Models (GAMs) have quickly become the leading choice for inherently-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they lack expressive power and easy scalability, and are hence not a feasible alternative for real-world tasks. We present a new class of GAMs that use tensor rank decompositions of polynomials to learn powerful, {\em inherently-interpretable} models. Our approach, titled Scalable Polynomial Additive Models (SPAM) is effortlessly scalable and models {\em all} higher-order feature interactions without a combinatorial parameter explosion. SPAM outperforms all current interpretable approaches, and matches DNN/XGBoost performance on a series of real-world benchmarks with up to hundreds of thousands of features. We demonstrate by human subject evaluations that SPAMs are demonstrably more interpretable in practice, and are hence an effortless replacement for DNNs for creating interpretable and high-performance systems suitable for large-scale machine learning. Source code is available at https://github.com/facebookresearch/nbm-spam.

In general, H is a structured matrix defined in such a way that the product Hu corresponds to a convolution between the image u and a given kernel h representing the Point Spread Function (PSF) of the imaging system employed to measure the data. The deblurring (or deconvolution) problem consists in finding an approximation of g, given the blurred image f and, possibly, some information on the system PSF. If the blurring kernel h, underlying the matrix H, is completely unknown and it has to be inferred together with g, the resulting problem is a blind deconvolution one [32]. Since the PSF h usually represents a low-pass filter, the matrix H is, at best, very ill conditioned and directly solving the inverse problem Hu = f, even when it is feasible, leads to unmeaningful solutions. On the other side, the variational approach consists in designing and solving an optimization problem whose solutions are a good approximation of the unknown image g. In general, a variational model is the set composed by the objective function, i.e., the function to be minimized, and the possible constraints. In the variational models arising in image restoration applications, the objective function, called also energy functional, encompasses different kinds of information: the nature of the noise introduced in the acquisition process, geometrical and/or analytical properties on the image content and physical constraints on the pixel values. Usually, in all image reconstruction problems, and more generally inverse problems, the energy functional, besides the data, depends on a set of parameters; they may simply reduce to tuning parameters balancing the relative weights of the different terms in the functional but can also represent more complicate structures of the functionals themselves.

This paper presents our submission to the 2022 edition of the CASE 2021 shared task 1, subtask 4. The EventGraph system adapts an end-to-end, graph-based semantic parser to the task of Protest Event Extraction and more specifically subtask 4 on event trigger and argument extraction. We experiment with various graphs, encoding the events as either "labeled-edge" or "node-centric" graphs. We show that the "node-centric" approach yields best results overall, performing well across the three languages of the task, namely English, Spanish, and Portuguese. EventGraph is ranked 3rd for English and Portuguese, and 4th for Spanish. Our code is available at: https://github.com/huiling-y/eventgraph_at_case

We present Burst2Vec, our multi-task learning approach to predict emotion, age, and origin (i.e., native country/language) from vocal bursts. Burst2Vec utilises pre-trained speech representations to capture acoustic information from raw waveforms and incorporates the concept of model debiasing via adversarial training. Our models achieve a relative 30 % performance gain over baselines using pre-extracted features and score the highest amongst all participants in the ICML ExVo 2022 Multi-Task Challenge.

Neural scaling laws define a predictable relationship between a model's parameter count and its performance after training in the form of a power law. However, most research to date has not explicitly investigated whether scaling laws can be used to accelerate model development. In this work, we perform such an empirical investigation across a wide range of language understanding tasks, starting from models with as few as 10K parameters, and evaluate downstream performance across 9 language understanding tasks. We find that scaling laws emerge at finetuning time in some NLP tasks, and that they can also be exploited for debugging convergence when training large models. Moreover, for tasks where scaling laws exist, they can be used to predict the performance of larger models, which enables effective model selection. However, revealing scaling laws requires careful hyperparameter tuning and multiple runs for the purpose of uncertainty estimation, which incurs additional overhead, partially offsetting the computational benefits.

An anonymous reader quotes a report from Motherboard: The Synthetic Party, a new Danish political party with an artificially intelligent representative and policies derived from AI, is eyeing a seat in parliament as it hopes to run in the country's November general election. The party was founded in May by the artist collective Computer Lars and the non-profit art and tech organization MindFuture Foundation. The Synthetic Party's public face and figurehead is the AI chatbot Leader Lars, which is programmed on the policies of Danish fringe parties since 1970 and is meant to represent the values of the 20 percent of Danes who do not vote in the election. Leader Lars won't be on the ballot anywhere, but the human members of The Synthetic Party are committed to carrying out their AI-derived platform. Leader Lars is an AI chatbot that people can speak with on Discord.

Adrienne Williams and Milagros Miceli are researchers at the Distributed AI Research (DAIR) Institute. Timnit Gebru is the institute's founder and executive director. She was previously co-lead of the Ethical AI research team at Google. The public's understanding of artificial intelligence (AI) is largely shaped by pop culture -- by blockbuster movies like "The Terminator" and their doomsday scenarios of machines going rogue and destroying humanity. This kind of AI narrative is also what grabs the attention of news outlets: a Google engineer claiming that its chatbot was sentient was among the most discussed AI-related news in recent months, even reaching Stephen Colbert's millions of viewers.

Clustering is an unsupervised learning problem that aims to partition unlabelled data points into groups with similar features. Traditional clustering algorithms provide limited insight into the groups they find as their main focus is accuracy and not the interpretability of the group assignments. This has spurred a recent line of work on explainable machine learning for clustering. In this paper we focus on the cluster description problem where, given a dataset and its partition into clusters, the task is to explain the clusters. We introduce a new approach to explain clusters by constructing polyhedra around each cluster while minimizing either the complexity of the resulting polyhedra or the number of features used in the description. We formulate the cluster description problem as an integer program and present a column generation approach to search over an exponential number of candidate half-spaces that can be used to build the polyhedra. To deal with large datasets, we introduce a novel grouping scheme that first forms smaller groups of data points and then builds the polyhedra around the grouped data, a strategy which out-performs simply sub-sampling data. Compared to state of the art cluster description algorithms, our approach is able to achieve competitive interpretability with improved description accuracy.