Geminet learns a topology-agnostic iterative process based on gradient descent and edge dual variables to enable lightweight ML-based traffic engineering that handles dynamic topologies with far lower resource use than prior methods.
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A fully differentiable parser that stochastically samples projective dependency trees using Gumbel perturbations and dynamic programming to boost downstream task performance without direct supervision.
signADAM and signADAM++ are new first-order optimizers that incorporate sign operations and a confidence-based sparsity mechanism, with claimed empirical superiority and theoretical convergence over ADAM and sign-based baselines.
Proposes Adaptive Margin Loss (AML) for TransE-style KG embeddings that uses a correntropy objective to adaptively expand the margin during training, requiring only a single center value instead of upper/lower bounds.
DPText learns text representations that are differentially private, free of private attributes, and retain utility for NLP tasks.
citing papers explorer
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Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
Geminet learns a topology-agnostic iterative process based on gradient descent and edge dual variables to enable lightweight ML-based traffic engineering that handles dynamic topologies with far lower resource use than prior methods.
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Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming
A fully differentiable parser that stochastically samples projective dependency trees using Gumbel perturbations and dynamic programming to boost downstream task performance without direct supervision.
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signADAM: Learning Confidences for Deep Neural Networks
signADAM and signADAM++ are new first-order optimizers that incorporate sign operations and a confidence-based sparsity mechanism, with claimed empirical superiority and theoretical convergence over ADAM and sign-based baselines.
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Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function
Proposes Adaptive Margin Loss (AML) for TransE-style KG embeddings that uses a correntropy objective to adaptively expand the margin during training, requiring only a single center value instead of upper/lower bounds.
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I Am Not What I Write: Privacy Preserving Text Representation Learning
DPText learns text representations that are differentially private, free of private attributes, and retain utility for NLP tasks.