Part of the power of a recursive neural network is that the same framework can teach itself to generate text in a huge variety of styles. So far I’ve used it to generate things like recipes, Dr. Who episode titles, D&D spells, story titles, metal band names
This survey reviews neural abstractive summarization, detailing seq2seq architectures, advanced training techniques, and efficient decoding strategies
← MUSCLE: A Model Update Strategy for Compatible LLM Evolution A Perspective on Foundation Models for the Electric Power Grid → # NeuFair: Neural Network Fairness Repair with Dropout この論文では、ディープ ニューラル ネットワーク (DNN) の後処理バイアス軽減策としてのニューロン ドロップアウトについて調査します。 ニューラル駆動のソフトウェア ソリューションは
Explains how power laws govern neural network scaling. Topics include log-log analysis, fitting techniques, and how to predict model performance at any scale
Author summary Neurons in the brain form intricate networks that can produce a vast array of activity patterns. To support goal-directed behavior, the brain must adjust the connections between neurons so that network dynamics can perform desirable computations on behaviorally relevant variables. A fundamental goal in computational neuroscience is to provide an understanding of how network connectivity aligns the dynamics in the brain to the dynamics needed to track those variables. Here, we develop a mathem
表題の論文を読んだのでまとめます！ url: [1506.02617] Path-SGD: Path-Normalized Optimization in Deep Neural Networks Path-SGD を考えたモチベーション ニューラルネットワークがこの論文の主題です。 Rescaling 今、あるニューラルネットワークの $i, i+1$ 番目の隠れ層の重み $W_i, W_{i+1}$ を取り出して $i$ 番目の重みを $x$ 倍して $i+1$ 番目の重みを $1/x$ 倍する操作を考えてみます(バイアスの大きさは 0
Spatiotemporal structure of neural population dynamics in the motor system on Simons Foundation
# Neural Variational Inference: Variational Autoencoders and Helmholtz machines So far we had a little of "neural" in our VI methods. Now it's time to fix it, as we're going to consider Variational Autoencoders (VAE), a paper by D. Kingma and M. Welling, which made a lot of buzz in ML community. It has 2 main contributions: a new approach (AEVB) to large-scale inference in non-conjugate models with continuous latent variables, and a probabilistic model of autoencoders as an example of this approach. We the
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Having looked in some detail at the Ising model, we are now well equipped to tackle a class of neuronal networks that has been studied by several authors in the sixties, seventies and early eighties of the last century, but has become popular by an article [1] published by J. Hopfield in 1982. The idea