A Spider Bite Is Worth the Chance Of Becoming Spider-Man...

Tag machine learning

Automatic Differentiation Variational Inference: Empowering Efficient Probabilistic Modeling

Probabilistic modeling forms the foundation of scientific analysis, allowing researchers to describe complex phenomena and make predictions based on data. However, fitting complex models to large datasets has always been a challenging and time-consuming process. The advent of automatic differentiation… Continue Reading →

ALOJA: A Framework for Benchmarking and Predictive Analytics in Big Data Deployments

What is ALOJA project? The ALOJA project is a collaborative effort between the Barcelona Supercomputing Center (BSC) and Microsoft with the aim of automating the characterization of cost-effectiveness in Big Data deployments, with a specific focus on the Hadoop platform…. Continue Reading →

Understanding Submodular Functions: from Discrete to Continuous Domains

What are Submodular Set-Functions? Submodular set-functions are mathematical objects that have various applications in combinatorial optimization. These functions can be minimized and approximately maximized in polynomial time, making them valuable tools in solving optimization problems. Real-world example: Imagine you are… Continue Reading →

Achieving Optimal Learning Bounds with Nyström Type Subsampling Approaches

Nyström type subsampling approaches have garnered significant attention in large-scale kernel methods, offering potential solutions to computational challenges. In a research article titled “Less is More: Nyström Computational Regularization,” Alessandro Rudi, Raffaello Camoriano, and Lorenzo Rosasco delve into the study… Continue Reading →

Unlocking Big Topic Models with LightLDA and Modest Compute Clusters

In recent years, the field of machine learning has witnessed tremendous growth, with big topic models and deep neural networks playing a pivotal role in harnessing valuable insights from vast amounts of data. However, the conventional school of thought suggests… Continue Reading →

The Power of Distributed Representations of Words and Phrases in Natural Language Processing

Understanding the intricacies of language has always been a challenging task for machines. However, recent advancements in Natural Language Processing (NLP) have brought us closer to a breakthrough. In 2023, a significant research paper titled “Distributed Representations of Words and… Continue Reading →

Maxout Networks: Leveraging Dropout for Improved Model Averaging and Optimization

In the world of deep learning, researchers are constantly striving to develop models that can accurately classify and analyze complex datasets. In pursuit of this goal, a team of talented individuals including Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron… Continue Reading →

Understanding Variational Relevance Vector Machines: Overcoming Limitations of Support Vector Machines

The Support Vector Machine (SVM) is a widely recognized and highly successful approach in the field of pattern recognition and machine learning. However, it has its limitations, one of which is its inability to generate predictive distributions. In this article,… Continue Reading →

Discovering Scores with Rank Centrality: Unveiling the Power of Pair-wise Comparisons

The process of ranking a collection of objects based on pair-wise comparisons has been a topic of great interest for a long time. Whether it’s determining the ranking of online gamers, aggregating social opinions, or making decisions in various domains,… Continue Reading →

Mixture-of-Parents Maximum Entropy Markov Models

A complex topic in the field of directed graphical models is the mixture-of-parents maximum entropy Markov model (MoP-MEMM). This model, proposed by David S. Rosenberg, Dan Klein, and Ben Taskar, extends the Maximum Entropy Markov Model (MEMM) by allowing the… Continue Reading →

« Older posts Newer posts »

© 2024 Christophe Garon — Powered by WordPress

Theme by Anders NorenUp ↑