Linglong Kong

Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm.

2021 · arXiv (Cornell University)

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its …

L2NAS

2021

Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures into a hyperparameter optimization problem which can be solved by gradient descent. However, …

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic\n Information Preserving

2021 · arXiv (Cornell University)

With widening deployments of natural language processing (NLP) in daily life,\ninherited social biases from NLP models have become more severe and\nproblematic. Previous studies have shown that word embeddings trained on\nhuman-generated corpora have strong gender biases …

Mathematical Challenges in Deep Learning

2023 · arXiv (Cornell University)

Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell …