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Found 11 papers, 3 papers with code
Improving Molecular Properties Prediction Through Latent Space Fusion
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network
Property prediction plays an important role in material discovery.
Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology
We are interested in in silico evaluation methodology for molecular optimization methods.
Classical Planning in Deep Latent Space
Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck.
A Differentiable Point Process with Its Application to Spiking Neural Networks
We develop a differentiable point process, which is the technical highlight of this paper, and apply it to derive the path-wise gradient estimator for SNNs.
Variational Domain Adaptation
It reformulates the posterior with a natural paring $\langle, \rangle: \mathcal{Z} \times \mathcal{Z}^* \rightarrow \Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation.
Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder
We analyze the problem in Latplan both formally and empirically, and propose "Zero-Suppressed SAE", an enhancement that stabilizes the propositions using the idea of closed-world assumption as a prior for NN optimization.
Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process
This paper presents a learning algorithm called ST-SafeMDP for exploring Markov decision processes (MDPs) that is based on the assumption that the safety features are a priori unknown and time-variant.
Molecular Hypergraph Grammar with its Application to Molecular Optimization
Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited.
Neuron as an Agent
Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments.
Bidirectional learning for time-series models with hidden units
Hidden units can play essential roles in modeling time-series having long-term dependency or on-linearity but make it difficult to learn associated parameters.
