Reliable machine learning potentials based on artificial neural network for graphene

no code implementations • 12 Jun 2023 • Akash Singh, Yumeng Li

Lattice parameter, coefficient of thermal expansion (CTE), Young's modulus and yield strength are estimated using machine learning accelerated MD simulations (MLMD), which are compared to experimental/first principle calculations from previous literatures.

Deep set conditioned latent representations for action recognition

no code implementations • International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2022 • Akash Singh, Tom De Schepper, Kevin Mets, Peter Hellinckx, Jose Oramas, Steven Latre

The proposed method achieves an improvement of around 1. 49% mAP in atomic action recognition and 17. 57% mAP in composite action recognition, over a I3D-NL baseline, on the CATER dataset.

Composite action recognition Temporal Action Localization

MockingBERT: A Method for Retroactively Adding Resilience to NLP Models

1 code implementation • COLING 2022 • Jan Jezabek, Akash Singh

Protecting NLP models against misspellings whether accidental or adversarial has been the object of research interest for the past few years.

Task Independent Capsule-Based Agents for Deep Q-Learning

no code implementations • Benelux Conference on Artificial Intelligence 2022 • Akash Singh, Tom De Schepper, Kevin Mets, Peter Hellinckx, Jose ́ Oramas, Steven Latre ́

In this paper, we propose DCapsQN, a task-independent CapsNets-based architecture in the deep reinforcement learning setting.

Object Recognition Q-Learning +2

Playing Atari with Capsule Networks: A systematic comparison of CNN and CapsNets-based agents.

no code implementations • 1 Jan 2021 • Akash Singh, Kevin Mets, Jose Oramas, Steven Latré

In this paper, we conduct a systematic analysis to explore the potential of CapsNets-based agents in the deep reinforcement learning setting.

reinforcement-learning Reinforcement Learning (RL)