Search Results for author:
Found 7 papers, 5 papers with code
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.
Continual Learning in Task-Oriented Dialogue Systems
Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.
Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming
We explore the tasks of one-shot learning and zero-shot domain transfer with DILOG on SimDial and MultiWoZ.
Optimization of Molecules via Deep Reinforcement Learning
We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning and randomized value functions).
Ranked #1 on
Molecular Graph Generation
on ZINC
(QED Top-3 metric)
Molecular Graph Generation
Multi-Objective Reinforcement Learning
+2
Graph Convolution: A High-Order and Adaptive Approach
In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the $k$-th order convolution operator and the adaptive filtering module.
