Musings of a Computer Scientist.
We study how neural networks compress uninformative input space in models where data lie in $d$ dimensions, but whose label only vary within a linear manifold of dimension $d_\parallel < d$. We show that for a one-hidden layer network initialized with infinitesimal weights (i.e. in the feature learning regime) trained with gradient descent, the first layer of weights evolve to become nearly insensitive to the $d_\perp=d-d_\parallel$ uninformative directions. These are effectively compressed by a factor $\la
Can multi-agent LLM systems, when structured with genetic programming, discover novel neural network designs that outperform human-engineered architectures? This matters because it could automate a critical bottleneck in AI research
Introduction In this tutorial, we will explore the implementation of a 5-input XOR function using pre-calculated weights and threshold in a neural
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
← 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