J. Li

We train very large end-to-end neural speech recognition models using the LibriSpeech dataset augmented with synthetic speech. Expand

We provide an alternative single-source shortest path algorithm with complexity Õ(n3/4D1/4). Expand

We show that many classical optimization problems --- such as $(1\pm\epsilon)$-approximate maximum flow, shortest path, and transshipment --- can be computed in $\newcommand{\tmix}{{\tau_{\text{mix}}}}\tmix(G)\cdot n^{o(1)}$ rounds of distributed message passing, where $\tmix (G)$ is the mixing time of the network graph $G$. Expand

We study adaptive network coding (NC), where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. Expand

We study the problem of deleting the smallest set $S$ of vertices (resp. edges) from a given graph $G$ such that the induced subgraph $G \setminus S$ belongs to some class $\mathcal{H}$. Expand

In the k-cut problem, we are given an edge-weighted graph G and an integer k, and have to remove a set of edges with minimum total weight so that G has at least k connected components. Expand