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Beginners Baseline Model for Machine Learning Project

#artificialintelligence

What is a Baseline Model? We can define the baseline model as a reference to the actual model. The baseline model should be a simple model that acts as a comparison and is easy to explain. Moreover, the baseline model should be based on the dataset to create the actual model. Why do we want to have a baseline model in our project?


Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks

arXiv.org Machine Learning

As the will to deploy neural networks models on embedded systems grows, and considering the related memory footprint and energy consumption issues, finding lighter solutions to store neural networks such as weight quantization and more efficient inference methods become major research topics. Parallel to that, adversarial machine learning has risen recently with an impressive and significant attention, unveiling some critical flaws of machine learning models, especially neural networks. In particular, perturbed inputs called adversarial examples have been shown to fool a model into making incorrect predictions. In this article, we investigate the adversarial robustness of quantized neural networks under different threat models for a classical supervised image classification task. We show that quantization does not offer any robust protection, results in severe form of gradient masking and advance some hypotheses to explain it. However, we experimentally observe poor transferability capacities which we explain by quantization value shift phenomenon and gradient misalignment and explore how these results can be exploited with an ensemble-based defense.


When to Trust Your Model: Model-Based Policy Optimization

Neural Information Processing Systems

Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data. In this paper, we study the role of model usage in policy optimization both theoretically and empirically. We first formulate and analyze a model-based reinforcement learning algorithm with a guarantee of monotonic improvement at each step. In practice, this analysis is overly pessimistic and suggests that real off-policy data is always preferable to model-generated on-policy data, but we show that an empirical estimate of model generalization can be incorporated into such analysis to justify model usage. Motivated by this analysis, we then demonstrate that a simple procedure of using short model-generated rollouts branched from real data has the benefits of more complicated model-based algorithms without the usual pitfalls.


Deep Learning Models that Write Code - Issue #6

#artificialintelligence

Simply put, a language model is a statistical model that learns the distribution or probabilities of words in a sequence. It turns out that if we can achieve such a model with high fidelity, we can solve a few interesting tasks. For example, if we know that a word is likely to occur given some sequence of words, we can implement some useful functionality like email autocomplete (e.g., given the sequence "Have a great " .. we can predict that the next likely word is "day"). When these statistical models are derived using large neural networks with billions of parameters (hence the term large language models or LLMs), the results and application areas are even more impressive. Results from transformer-based model architectures like BERT, GPT etc., show that these models excel at several complex tasks e.g., they can mimic creative writing, predict sentiment, identify topics within sentences with few examples, meaningfully summarize lengthy documents, translate languages etc.


3 Main Approaches to Machine Learning Models - KDnuggets

#artificialintelligence

In September 2018, I published a blog about my forthcoming book on The Mathematical Foundations of Data Science. The central question we address is: How can we bridge the gap between mathematics needed for Artificial Intelligence (Deep Learning and Machine learning) with that taught in high schools (up to ages 17/18)? In this post, we present a chapter from this book called "A Taxonomy of Machine Learning Models." The book is now available for an early bird discount released as chapters. If you are interested in getting early discounted copies, please contact ajit.jaokar at feynlabs.ai.