Essays about: "Distribution Embeddings"

Showing result 1 - 5 of 13 essays containing the words Distribution Embeddings.

1. 1. Artificial Transactional Data Generation for Benchmarking Algorithms

   University essay from Umeå universitet/Institutionen för fysik

   Author : Veronica Lundgren; [2023]
   Keywords : Data Analytics; Synthetic Data; Statistical Distribution; Embedding; Transactional Data;

   Abstract : Modern retailers have been collecting more and more data over the past decades. The increased sizes of collected data have led to higher demand for data analytics expertise tools, which the Umeå-founded company Infobaleen provides. A recurring challenge when developing such tools is the data itself. READ MORE

3. 2. Decoding communication of non-human species - Unsupervised machine learning to infer syntactical and temporal patterns in fruit-bats vocalizations.

   University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

   Author : Luigi Assom; [2023]
   Keywords : animal decision making; unsupervised machine learning; UMAP; autoencoders; classifiers; bioacoustics; combinatory syntax; animal communication;

   Abstract : Decoding non-human species communication offers a unique chance to explore alternative intelligence forms using machine learning. This master thesis focuses on discreteness and grammar, two of five linguistic areas machine learning can support, and tackles inferring syntax and temporal structures from bioacoustics data annotated with animal behavior. READ MORE

4. 3. Messing With The Gap: On The Modality Gap Phenomenon In Multimodal Contrastive Representation Learning

   University essay from Uppsala universitet/Industriell teknik

   Author : Mohammad Al-Jaff; [2023]
   Keywords : Multimodal machine learning; Representation learning; Self-supervised learning; contrastive learning; computer vision; computational biology; bioinformatics;

   Abstract : In machine learning, a sub-field of computer science, a two-tower architecture model is a specialised type of neural network model that encodes paired data from different modalities (like text and images, sound and video, or proteomics and gene expression profiles) into a shared latent representation space. However, when training these models using a specific contrastive loss function, known as the multimodalinfoNCE loss, seems to often lead to a unique geometric phenomenon known as the modality gap. READ MORE

5. 4. Reliable graph predictions : Conformal prediction for Graph Neural Networks

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Albin Bååw; [2022]
   Keywords : Conformal prediction; Graph Neural Networks; Dynamic graphs; Distribution shift; Coverage gap; Konform prediktion; Neurala Nätverk för Grafer; Dynamiska grafer; Distributionsförändring; täckningsgap;

   Abstract : We have seen a rapid increase in the development of deep learning algorithms in recent decades. However, while these algorithms have unlocked new business areas and led to great development in many fields, they are usually limited to Euclidean data. READ MORE

6. 5. Synthetic Graph Generation at Scale : A novel framework for generating large graphs using clustering, generative models and node embeddings

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Johan Hammarstedt; [2022]
   Keywords : Data Anonymization; Graph Learning; Generative Graph Modeling; Graph Clustering; Node Embedding; Synthetic Data; Dataanonymisering; Grafinlärning; Generativa graf-modeller; Graf klustring; Länk prediktion; Nodinbäddning; Syntetisk data;

   Abstract : The field of generative graph models has seen increased popularity during recent years as it allows us to model the underlying distribution of a network and thus recreate it. From allowing anonymization of sensitive information in social networks to data augmentation of rare diseases in the brain, the ability to generate synthetic data has multiple applications in various domains. READ MORE