Currently we find ourselves in the middle of the hype surrounding Artificial Intelligence (AI) and all its buzzwords. It's natural to feel that terms related

ICML 2018 Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples Gail Weiss, Yoav Goldberg, Eran Yahav TL;DR — RNNs are black boxes, but you can extract interpretable DFAs from them using Angluin's L* algorithm. The RNN serves as the "oracle" — answering membership and equivalence queries — while L* constructs a minimal DFA that approximates the RNN's behavior. The Problem Recurrent neural networks can learn to recognize formal languages from examples, but their internal

A feedforward neural network (FNN), also known as a multilayer perceptron (MLP), is a fundamental type of artificial neural network in which information propagates unidirectionally from an input layer

To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures (VSA), and leverage properties of reservoir computing. In general, the storage in reservoir computing is lossy and crosstalk noise limits the retrieval accuracy and informati

And the Bit Goes Down: Revisiting the Quantization of Neural Networks Jethro's Braindump Search site And the Bit Goes Down: Revisiting the Quantization of Neural Networks tags Model Compression paper ( Stock et al., n.d. ) This method minimizes the loss reconstruction error for in-domain inputs, and does not require any labelled data. This method exploits the high correlation in the convolutions in ResNet-like architectures by the use of product quantization (PQ). The approach here focuses on reconstructing

Map > Data Science > Predicting the Future > Modeling > Classification / Regression > Artificial Neural Network ### Artificial Neural Network An artificial neutral network (ANN) is a system that is based on the biological neural network, such as the brain. The brain has approximately 100 billion neurons, which communicate through electro-chemical signals. The neurons are connected through junctions called synapses. Each neuron receives thousands of connections with other neurons, constantly receiving inco

In recent times, convolutional neural networks became an irreplaceable tool in many different machine learning applications, especially in image classifica

← Transcoders Beat Sparse Autoencoders for Interpretability Concentration Inequalities for the Stochastic Optimization of Unbounded Objectives with Application to Denoising Score Matching → # Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function 投稿日: 2025年2月13日 作成者: jarxiv 最新の機械学習アルゴリズム、特に深い学習ベースの手法では、通常

Theoretical analysis and evaluation of Graph Neural Networks' expressiveness in recommendation systems using graph isomorphism, node automorphism, and topological closeness

How to Define Hyperparameters Effectively Identifying the right hyperparameters is crucial for model performance.