The proposed indicators adequately measure the effectiveness of the soft labels generated in this process.

Label noise widely exists in large-scale image datasets.

Self-supervised learning (SSL) is a powerful tool in machine learning, but understanding the learned representations and their underlying mechanisms remains a challenge.

As recommended by the NeurIPS dataset and benchmark track, we documented QM1B and intended uses through the Datasheets for Datasets framework [1]. The goal of dataset datasheets as outlined by [1] is to provide a standardized process for documentating datasets. The authors of [1] present a list of carefully selected questions which dataset authors should answer. We hope our answers to these questions will facilitate better communication between us (the dataset creators) and future users of QM1B. For what purpose was the dataset created? Prior gaussian-based Density Functional Theory (DFT) datasets contained fewer than 20 million training examples.

Market research indicates that there is a significant opportunity for a new co ee bar located in the heart of the downtown business district.

Machine learning models--including prominent zero-shot models--are often trained on datasets whose labels are only a small proportion of a larger label space. Such spaces are commonly equipped with a metric that relates the labels via distances between them.

We propose UTSP, an Unsupervised Learning (UL) framework for solving the Travelling Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss. The GNN outputs a heat map representing the probability for each edge to be part of the optimal path. We then apply local search to generate our final prediction based on the heat map. Our loss function consists of two parts: one pushes the model to find the shortest path and the other serves as a surrogate for the constraint that the route should form a Hamiltonian Cycle. Experimental results show that UTSP outperforms the existing data-driven TSP heuristics. Our approach is parameter efficient as well as data efficient: the model takes 10% of the number of parameters and 0.2% of training samples compared with Reinforcement Learning or Supervised Learning methods.