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Found 15 papers, 11 papers with code
DOC: Improving Long Story Coherence With Detailed Outline Control
In human evaluations of automatically generated stories, DOC substantially outperforms a strong Re3 baseline (Yang et al., 2022) on plot coherence (22. 5% absolute gain), outline relevance (28. 2%), and interestingness (20. 7%).
Re3: Generating Longer Stories With Recursive Reprompting and Revision
We consider the problem of automatically generating longer stories of over two thousand words.
Automated Crossword Solving
We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles.
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances.
Multi-objective Optimization by Learning Space Partitions
In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
Multi-objective Optimization by Learning Space Partition
In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
Learning Space Partitions for Path Planning
Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function.
FUDGE: Controlled Text Generation With Future Discriminators
We propose Future Discriminators for Generation (FUDGE), a flexible and modular method for controlled text generation.
A Streaming Approach For Efficient Batched Beam Search
We propose an efficient batching strategy for variable-length decoding on GPU architectures.
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.
Improving Molecular Design by Stochastic Iterative Target Augmentation
The property predictor is then used as a likelihood model for filtering candidate structures from the generative model.
Iterative Target Augmentation for Effective Conditional Generation
Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs.
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
We evaluate our model on multiple molecule optimization tasks and show that our model outperforms previous state-of-the-art baselines by a significant margin.
Analyzing Learned Molecular Representations for Property Prediction
In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets.
Ranked #3 on
Molecular Property Prediction
on ClinTox
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.
