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Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Kevin Scaman, Francis Bach, Sebastien Bubeck, Laurent Massoulié, Yin Tat Lee
Neural Information Processing Systems http://nips.cc/
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond Taiji Suzuki 1,2, Denny Wu
Langevin dynamics (MFLD) (Mei et al., 2018; Hu et al., 2019) is particularly attractive due to the MFLD arises from a noisy gradient descent update on the parameters, where Gaussian noise is injected to the gradient to encourage "exploration". Furthermore, uniform-in-time estimates of the particle discretization error have also been established (Suzuki et al., The goal of this work is to address the following question.
Neighbourhood Consensus Networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic
Neural Information Processing Systems http://nips.cc/
Elliptical Perturbations for Differential Privacy
Neural Information Processing Systems http://nips.cc/
Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates
A popular trend in recent years is using symbolic planning models with reinforcement learning (RL) algorithms.
Approximate Inference Turns Deep Networks into Gaussian Processes
Mohammad Emtiyaz E. Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
We present theoretical results aimed at connecting the training methods of deep learning and GP models. We show that the Gaussian posterior approximations for Bayesian DNNs, such as those obtained by Laplace approximation and variational inference (VI), are equivalent to posterior distributions ofGPregression models.
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
Neural Information Processing Systems http://nips.cc/
AI is indeed coming – but there is also evidence to allay investor fears
Traders on the floor of the New York Stock Exchange as markets fell on Friday morning trading after a steep drop on Thursday, as investors continue to worry about the impact of AI on business and the wider economy. Traders on the floor of the New York Stock Exchange as markets fell on Friday morning trading after a steep drop on Thursday, as investors continue to worry about the impact of AI on business and the wider economy. The message from investors to the software, wealth management, legal services and logistics industries this month has been clear: AI is coming for your business. The release of new, ever more powerful AI tools has coincided with a stock market slide, which has swept up sectors as diverse as drug distribution, commercial property and price comparison sites. Advances in the technology are giving increasing credulity to predictions that it could render millions of white-collar jobs obsolete - or, at least, eat into the profits of established companies.
Piecewise Strong Convexity of Neural Networks
Neural Information Processing Systems http://nips.cc/