Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accuracy, monetary cost, and latency, and their accuracy further depends on the exact wording of the question ( i .

LLMs can now act as autonomous agents that interact with digital environments and complete specific objectives ( e.g., arranging an online meeting).

Intuitively, by using more information from the distribution of latents, our approach allows a more distribution-aware manipulation of the relationships within augmented sample sets.

Therefore, they fall short in facilitating methodological advances in semantic modeling within dynamic graphs and exploring the impact of text attributes on downstream tasks.

We present NN4SysBench, a benchmark suite for neural network verification that is composed of applications from the domain of computer systems. We call these neural networks for computer systems or NN4Sys . NN4Sys is booming: there are many proposals for using neural networks in computer systems--for example, databases, OSes, and networked systems--many of which are safety-critical. Neural network verification is a technique to formally verify whether neural networks satisfy safety properties.

Recognition (ASR), we show strong baseline performance on predicting key-presses using sEMG signals alone.

Most real-world datasets consist of a natural hierarchy between classes or an inherent label structure that is either already available or can be constructed cheaply. However, most existing representation learning methods ignore this hierarchy, treating labels as permutation invariant.

In this work, we propose data-efficient and interpretable machine learning models for learning to solve numeric planning tasks.