The world of artificial intelligence is an ever-evolving landscape, and today we're delving into a fascinating development that draws inspiration from the human brain. You see, our brains have this incredible ability to consolidate memories during sleep, sifting through the day's experiences and dec...
In this blog post, we'll be building a simple neural network from scratch using Pytorch. We'll go through the necessary steps to construct our network and
Can a machine learning algorithm learn to tell a joke? I’ve experimented with neural networks and jokes before, teaching them to tell knock-knock jokes, or to generate April Fools pranks. In each case, the results were… underwhelming. However, that could have been because the algorithm didn’t have much data to work with, just a couple of hundred examples of each type of joke. What happens when I give a neural network a LOT of examples to copy
## Neural Tree Indexers for Text Understanding Tsendsuren Munkhdalai , Hong Yu Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-indepe
Current trends in Machine Learning: Probabilistic Programming, Deep Learning and “ Big Data ” are among the biggest topics in machine learning. Inside of PP, a lot of innovation is focused on makin...
The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a substantial social impact. However, link prediction algorithms tend to increase the segregation in social networks by disfavoring the links between individuals in specific demographic groups. This paper proposes a novel way to enforce fairness on graph neural networks with a fine-tuning strategy
Today a clear and practical view of GCN theory. We'll go through Kipf's PyTorch 🔦 implementation of GCN 👩🎓. Then, we'll apply what...
Learn Graph Neural Network (GNN), how it works, and how it improves fraud detection, cash flow forecasting, and financial decision-making
# Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Prediction John Moody, Joachim Utans The notion of generalization ability can be defined precisely as the pre(cid:173) diction risk, the expected performance of an estimator in predicting new observations. In this paper, we propose the prediction risk as a measure of the generalization ability of multi-layer perceptron networks and use it to select an optimal network architecture from a set of possible architec(c
CSP Test --> Main Navigation ICLR Help/FAQ Contact ICLR Create Profile Code of Conduct Journal To Conference Track Diversity & Inclusion Proceedings at OpenReview Future Meetings Press Exhibitor Information ICLR Blog ICLR Twitter About ICLR Downloads Privacy Policy Reset Password My Stuff Login Select Year: (2022) 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 Poster Deconstructing the Inductive Biases of Hamiltonian Neural Networks Nate Gruver ⋅ Marc A Finzi ⋅ Samuel Stanton