Search Results for author:
Found 9 papers, 2 papers with code
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows
Downscaling is a landmark task in climate science and meteorology in which the goal is to use coarse scale, spatio-temporal data to infer values at finer scales.
Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data
The forecast of tropical cyclone trajectories is crucial for the protection of people and property.
Evaluating the distribution learning capabilities of GANs
We evaluate the distribution learning capabilities of generative adversarial networks by testing them on synthetic datasets.
Cooperative Online Learning: Keeping your Neighbors Updated
However, when agents can choose to ignore some of their neighbors based on the knowledge of the network structure, we prove a $O(\sqrt{\overline{\chi} T})$ sublinear regret bound, where $\overline{\chi} \ge \alpha$ is the clique-covering number of the network.
Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization
Auto-encoders are commonly used for unsupervised representation learning and for pre-training deeper neural networks.
Convergence rate of stochastic k-means
We analyze online \cite{BottouBengio} and mini-batch \cite{Sculley} $k$-means variants.
Streaming k-means approximation
We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting.
Privacy-preserving logistic regression
This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases.
