4 accepted papers
Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The recently proposed Iterative Normalization (IterNorm) method improves these propert…
This paper proposes an adaptive metric selection strategy called diagonal Barzilai-Borwein (DBB) stepsize for the popular Variable Metric Proximal Gradient (VM-PG) algorithm [1], [2]. The proposed approach better captures the local geometry of the problem while keeping the per-step computation cost…
We introduce the notion of learning from contradictions, a.k.a Universum learning, for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We show that learning from contradictions (using MU-SVM) incurs lower sample complexity compared to multiclass SVM (M-SVM)…
In this paper, we consider the problem of event prediction with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex dependencies between the variables combined with asynchronicity and sparsity of the data makes the event prediction problem p…