Although neural networks have seen tremendous success as predictive models in a variety of domains, they can be overly confident in their predictions on out-of-distribution (OOD) data. To be viable for safety-critical applications, like autonomous vehicles, neural networks must accurately estimate their epistemic or model uncertainty, achieving a level of system self-awareness. Techniques for epistemic uncertainty quantification often require OOD data during training or multiple neural network forward passe
Building and Training Neural Networks in Emacs Lisp Yes you read that correctly. This blog post showcases nn.el , my (zero dependency) emacs lisp package that allows you to build and train neural networks. To answer the initial question as to “why on God’s green earth would you write a machine learning framework in emacs lisp?”, the answer is really just for fun (and to cause a bit of a splash, i mean emacs lisp is the last thing you’d associate with AI right now). This particular flavour is really
AI: Artificial Intelligence. Index AI: Artificial Neural Network is Nasty (2016) By Xah Lee. Date: 2016-03-13. Last updated: 2020-09-21. artificial neural network is nasty. Google's got TensorFlow, and SyntaxNet, AlphaGo … all based on neural networks aka “deep learning”. When neural networks solves a problem, such as language translation, it undestands nothings, and we can learn nothing from it. No theory, no reasoning, nothing. Zero. It simply shot a answer back at you. it doesn't have any insights
There’s a neural network framework called pix2pix that can be trained to learn how to transform one type of image into another. It’s been used for example to convert satellite images into line drawings and vice versa (think Google maps satellite view vs the one that show boxes where all the buildings are
Abstract page for arXiv paper 1810.12065: On the Convergence Rate of Training Recurrent Neural Networks
# Distributed Neural Network Inference over Low-Power Wireless Networks: Design and Real-World Evaluation Modern machine learning methods, such as transformer models and Bayesian optimization, are becoming increasingly important in the context of cyber-physical systems (CPS). A key component of CPS is distributed computation, wherein multiple agents collaborate toward a common goal. In this work, we investigated two distinct approaches: distributed transformer inference on wireless, ultra-low-power devices
Russell Cohen POSTS July 12, 2018 Dropout and the Deep Complexity of Neural Networks There’s a common misconception that neural networks’ recent success on a slew of problems is due to the increasing speed and decreasing cost of GPUs. In reality, that’s not the case. Modern processing power plays a critical role, but only when combined with a series of innovations in architecture and training. You can’t process million-image datasets like ImageNet without a GPU, but without Resnet you won’t be
Exploration—discovering unknown user preferences—normally requires expensive posterior uncertainty estimates. Can a neural architecture make Thompson sampling practical for real-world recommenders without prohibitive computational cost
← Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures → # Logical Characterizations of Recurrent Graph Neural Networks with Reals and Floats 投稿日: 2025年5月5日 作成者: jarxiv 2019年の先駆的な研究において、Barcel’oと共著者は、一階論理で定義可能な性質と比較して、一定の反復深さのグラフ・ニューラル・ネットワーク（GNN
Dropout Inference in Bayesian Neural Networks with Alpha-divergencesYingzhen Li, Yarin GalTo obtain uncertainty estimates with real-world Bayesian deep