Ni Lao

Memory Augmented Policy Optimization for Program Synthesis with Generalization

2018 · arXiv (Cornell University)

This paper presents Memory Augmented Policy Optimization (MAPO): a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions. …

TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning

2024 · arXiv (Cornell University)

Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, networks, images, etc.) in their native formats. Learning good spatial representations is a fundamental …

Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

2017

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic …

Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

2016 · arXiv (Cornell University)

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic …