While this could be done using the synthesis formulation, we demonstrate that this leads to slower performances.

SA TNet is a differentiable constraint solver with a custom backpropagation algorithm, which can be used as a layer in a deep-learning system. It is a promising proposal for bridging deep learning and logical reasoning. In fact, SA TNet has been successfully applied to learn, among others, the rules of a complex logical puzzle, such as Sudoku, just from input and output pairs where inputs are given as images. In this paper, we show how to improve the learning of SA TNet by exploiting symmetries in the target rules of a given but unknown logical puzzle or more generally a logical formula. We present SymSA TNet, a variant of SA T - Net that translates the given symmetries of the target rules to a condition on the parameters of SA TNet and requires that the parameters should have a particular parametric form that guarantees the condition. The requirement dramatically reduces the number of parameters to learn for the rules with enough symmetries, and makes the parameter learning of SymSA TNet much easier than that of SA TNet.

This shows that local minibatching and device sampling are both beneficial for linearly speeding up communication. A few observations are in order.

Modeling feature interactions in tabular data remains a key challenge in predictive modeling, for example, as used for insurance pricing. This paper proposes the Tree-like Pairwise Interaction Network (PIN), a novel neural network architecture that explicitly captures pairwise feature interactions through a shared feed-forward neural network architecture that mimics the structure of decision trees. PIN enables intrinsic interpretability by design, allowing for direct inspection of interaction effects. Moreover, it allows for efficient SHapley's Additive exPlanation (SHAP) computations because it only involves pairwise interactions. We highlight connections between PIN and established models such as GA2Ms, gradient boosting machines, and graph neural networks. Empirical results on the popular French motor insurance dataset show that PIN outperforms both traditional and modern neural networks benchmarks in predictive accuracy, while also providing insight into how features interact with each another and how they contribute to the predictions.

Programmable task-oriented dialogue (TOD) agents enable language models to follow structured dialogue policies, but their effectiveness hinges on accurate state tracking. We present PyTOD, an agent that generates executable code to track dialogue state and uses policy and execution feedback for efficient error correction. To this end, PyTOD employs a simple constrained decoding approach, using a language model instead of grammar rules to follow API schemata. This leads to state-of-the-art state tracking performance on the challenging SGD benchmark. Our experiments show that PyTOD surpasses strong baselines in both accuracy and robust user goal estimation as the dialogue progresses, demonstrating the effectiveness of execution-aware state tracking.

Modest statistical differences between the sampling performances of the D-Wave quantum annealer (QA) and the classical Markov Chain Monte Carlo (MCMC), when applied to Restricted Boltzmann Machines (RBMs), are explored to explain, and possibly address, the absence of significant and consistent improvements in RBM trainability when the D-Wave sampling was used in previous investigations. A novel hybrid sampling approach, combining the classical and the QA contributions, is investigated as a promising way to benefit from the modest differences between the two sampling methods. No improvements in the RBM training are achieved in this work, thereby suggesting that the differences between the QA-based and MCMC sampling, mainly found in the medium-to-low probability regions of the distribution, which are less important for the quality of the sample, are insufficient to benefit the training. Difficulties in achieving sufficiently high quality of embedding RBMs into the lattice of the newer generation of D-Wave hardware could be further complicating the task. On the other hand, the ability to generate samples of sufficient variety from lower-probability parts of the distribution has a potential to benefit other machine learning applications, such as the mitigation of catastrophic forgetting (CF) during incremental learning. The feasibility of using QA-generated patterns of desirable classes for CF mitigation by the generative replay is demonstrated in this work for the first time. While the efficiency of the CF mitigation using the D-Wave QA was comparable to that of the classical mitigation, both the speed of generating a large number of distinct desirable patterns and the potential for further improvement make this approach promising for a variety of challenging machine learning applications.

Model personalization, with broad applications in several fields, including precision medicine and healthcare (Johnson et al., 2021; Hilton et al., 2020; Abul-Husn and Kenny, 2019), advertising (Bilenko and Richardson, 2011), and interactive user inter faces (Ma et al., 2021), involves tailoring models to account for the unique characteristics and featur es of individuals (or subgroups) within a population. A key challenge in achieving model personaliza tion is addressing heterogeneity among individuals while leveraging their similarities. When eac h individual has access to a large amount of data, leveraging similarity is not essential, and one strai ghtforward method is to fit separate models to each individual, allowing for fully individualized m odeling. However, in most applications, including healthcare, access to a large sample size for each individual is difficult and expensive. An alternative approach to fully individualized modeling tra ins one model to fit all by combining data from all individuals. While this approach increases the sam ple size, it overlooks the unique traits of individuals and the variations between them. Therefore, middle-ground methods that can use shared information and capture data heterogeneity are nece ssary. An example of such a trade-off appears in telemonitoring appl ications for chronic disease management, such as remote assessment of Parkinson's disease s everity using smartphone-based sensor 1