Introduction Imagine you are working at a major investment bank like J.P. Morgan or Goldman Sachs, or maybe at a hedge fund, where modeling how the market behaves is critical, whether for trading strategies, risk controls, or stress scenario simulations. In these types of environments, being able to generate realistic synthetic financial time-series data isn't…
Neural architecture search with reinforcement learning Zoph & Le, ICLR'17 Earlier this year we looked at 'Large scale evolution of image classifiers' which used an evolutionary algorithm to guide a search for the best network architectures. In today's paper, Zoph & Le also demonstrate that learning network architectures (and also in their case recurrent cell
# Invertible Neural Skinning ###### Abstract Building animatable and editable models of clothed humans from raw 3D scans and poses is a challenging problem. Existing reposing methods suffer from the limited expressiveness of Linear Blend Skinning (LBS), require costly mesh extraction to generate each new pose, and typically do not preserve surface correspondences across different poses. In this work, we introduce Invertible Neural Skinning (INS) to address these shortcomings. To maintain correspondences
Veronika Koren, Ph.D. Computational Neuroscientist Home Contact me Biologically plausible solutions for spiking networks with efficient coding Authors : Veronika Koren and Stefano Panzeri High-level description Neurons communicate with each other by sending to each other brief electrical impulses that we call spikes. In the last decades, outstanding research on spiking networks has brought crucial insights into the dynamics of these complex interacting systems. We however still lack the link between neural
Code for the paper “Characterizing Well-behaved vs. Pathological Deep Neural Networks“ published in 36th International Conference on Machine Learning (ICML 2019): https://arxiv.org/abs/1811.03087 - alabatie/moments-dnns
Instead of being flipped like a switch, brain networks spontaneously reorganize their web of connections when a person forms new memories
We are in the midst of a scientific and technological revolution. The computers of today use artificial intelligence to learn from example and to execute sophisticated functions that, until recently, were thought impossible. These smart algorithms can recognize faces and even drive autonomous vehicles. Deep learning networks, which are responsible for many of these technological advances, are based on the same principles that form the structure of our brain: they are composed of artificial nerve cells that
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The text discusses the development of NEAT neural networks for financial modeling, emphasizing the importance of finding stable dependencies through optimization and hyperparameter tuning. It also covers challenges in overfitting, the use of real ticks in trading platforms, and methods to improve AI longevity without over-optimization, such as boosting and multi-level training. The author shares personal experiences with model training, testing, and the importance of assessing model generalization through a