Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Data Science Understanding Neural Network Embeddings A deep dive into neural network embeddings Frank Liu Apr 30, 2022 10 min read Share I’ve broached the subject of embeddings/embedding vectors in prior blog posts on vector databases and ML application development , but haven’t yet done a deep dive on embeddings and some of the
Dreams are really weird, when you think about them. Let’s lie still for several hours and maybe vividly hallucinate? A lot of what people call AI these days are algorithms called artificial neural networks, a type of algorithm that’s modeled after the human brain. They have some things in common with human brains - virtual neurons, and even some of the same mechanisms for analyzing images and navigating mazes. But can they dream
This paper introduces a novel frequency tagging method using biological SSVEP responses to quantify neuron importance in CNNs with FFT and SNR analysis.
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NeurIPS 2020 On Completeness-aware Concept-Based Explanations in Deep Neural Networks Meta Review I recommend accepting this paper. I believe that this work will be of relevance to the machine learning community. Although reviewers initially had some doubts about a few aspects of the methodology, they were convincingly addressed in a rebuttal
Hello!! After passing all tests in Exercise 8, I have the following errors while running model. Help pls!!! logistic_regression_model = model(train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, num_iterations=20…
Examines Yoshua Bengio's early 2003 Neural Probabilistic Language Model that changed NLP by learning dense, continuous word embeddings
NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center Paper ID: 9024 Title: Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks Reviewer 1 Originality: the use use of the Legendre polynomial seems rather creative, it was certainly important to define RNNs with good models of coupled linear units. Quality: The set of benchmarks is well chosen to describe a broad scope of qualities that RNN require. One non-artificial task would have been a plus thou
Python, PyTorch
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural NetworksJuho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek