Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Including prior knowledge is important for effective machine learning models in physics and
is usually achieved by explicitly adding loss terms or constraints on model architectures …

We consider the black-box adversarial setting, where the adversary has to generate
adversarial perturbations without access to the target models to compute gradients. Previous …

While deep learning models have replaced hand-designed features across many domains,
these models are still trained with hand-designed optimizers. In this work, we leverage the …

The deep learning revolution has greatly been accelerated by the'hardware lottery': Recent
advances in modern hardware accelerators, compilers and the availability of open-source …

The job shop scheduling problem (JSSP) with dynamic events and uncertainty is a strongly
NP-hard combinatorial optimization problem (COP) with extensive applications in the …

Unrolled computation graphs arise in many scenarios, including training RNNs, tuning
hyperparameters through unrolled optimization, and training learned optimizers. Current …

Improving the predictive capability of molecular properties in ab initio simulations is
essential for advanced material discovery. Despite recent progress making use of machine …

During the optimization process, a large number of data are generated through the search.
Machine learning techniques and algorithms can be used to handle the generated data to …

Adversarial attacks have been extensively studied in recent years since they can identify the
vulnerability of deep learning models before deployed. In this paper, we consider the black …