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Found 10 papers, 2 papers with code
Improved analysis of randomized SVD for top-eigenvector approximation
We present a novel analysis of the randomized SVD algorithm of \citet{halko2011finding} and derive tight bounds in many cases of interest.
Rethinking Why Intermediate-Task Fine-Tuning Works
Supplementary Training on Intermediate Labeled-data Tasks (STILTs) is a widely applied technique, which first fine-tunes the pretrained language models on an intermediate task before on the target task of interest.
TinyGAN: Distilling BigGAN for Conditional Image Generation
Generative Adversarial Networks (GANs) have become a powerful approach for generative image modeling.
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training
The objective of non-parallel text style transfer, or controllable text generation, is to alter specific attributes (e. g. sentiment, mood, tense, politeness, etc) of a given text while preserving its remaining attributes and content.
On the Algorithmic Power of Spiking Neural Networks
Our main technical insight is a dual view of the SNN dynamics, under which SNN can be viewed as a new natural primal-dual algorithm for the l1 minimization problem.
Online Reinforcement Learning in Stochastic Games
We study online reinforcement learning in average-reward stochastic games (SGs).
Tracking the Best Expert in Non-stationary Stochastic Environments
We study the dynamic regret of multi-armed bandit and experts problem in non-stationary stochastic environments.
Tensor Decomposition via Simultaneous Power Iteration
Tensor decomposition is an important problem with many applications across several disciplines, and a popular approach for this problem is the tensor power method.
Generalized Mirror Descents in Congestion Games
Are there more cases of natural no-regret dynamics that perform well in suitable classes of games in terms of convergence and quality of outcomes that the dynamics converge to?
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA
In this paper, we analyze the convergence rate of a representative algorithm with decayed learning rate (Oja and Karhunen, 1985) in the first family for the general $k>1$ case.
