97 citations · 256 across the 10 of their papers we have counts for
7 papers · 1 filter
Multi-objective Optimization by Learning Space Partitions
Yiyang Zhao, Linnan Wang, Kevin Yang +3
In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are no…
Few-shot Neural Architecture Search
Yiyang Zhao, Linnan Wang, Yuandong Tian +2
Efficient evaluation of a network architecture drawn from a large search space remains a key challenge in Neural Architecture Search (NAS). Vanilla NAS evaluates each architecture…
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Tian Guo, Tao Lin, Nino Antulov-Fantulin
For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights i…
Low-dimensional statistical manifold embedding of directed graphs
Thorben Funke, Tian Guo, Alen Lancic +1
We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-lin…
Multi-variable LSTM neural network for autoregressive exogenous model
Tian Guo, Tao Lin
In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mec…
An interpretable LSTM neural network for autoregressive exogenous model
Tian Guo, Tao Lin, Yao Lu
In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mecha…
Is Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings
Vaibhav Krishna, Tian Guo, Nino Antulov-Fantulin
Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems. In recent times, different variants of deep learning algorith…