Exploiting Contextual Information with Deep Neural Networks
- URL: http://arxiv.org/abs/2006.11706v2
- Date: Sat, 27 Jun 2020 18:00:07 GMT
- Title: Exploiting Contextual Information with Deep Neural Networks
- Authors: Ismail Elezi
- Abstract summary: We show that contextual information can be exploited in 2 fundamentally different ways: implicitly and explicitly.
In this thesis, we show that contextual information can be exploited in 2 fundamentally different ways: implicitly and explicitly.
- Score: 5.787117733071416
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Context matters! Nevertheless, there has not been much research in exploiting
contextual information in deep neural networks. For most part, the entire usage
of contextual information has been limited to recurrent neural networks.
Attention models and capsule networks are two recent ways of introducing
contextual information in non-recurrent models, however both of these
algorithms have been developed after this work has started.
In this thesis, we show that contextual information can be exploited in 2
fundamentally different ways: implicitly and explicitly. In the DeepScore
project, where the usage of context is very important for the recognition of
many tiny objects, we show that by carefully crafting convolutional
architectures, we can achieve state-of-the-art results, while also being able
to implicitly correctly distinguish between objects which are virtually
identical, but have different meanings based on their surrounding. In parallel,
we show that by explicitly designing algorithms (motivated from graph theory
and game theory) that take into considerations the entire structure of the
dataset, we can achieve state-of-the-art results in different topics like
semi-supervised learning and similarity learning.
To the best of our knowledge, we are the first to integrate graph-theoretical
modules, carefully crafted for the problem of similarity learning and that are
designed to consider contextual information, not only outperforming the other
models, but also gaining a speed improvement while using a smaller number of
parameters.
Related papers
- Breaking the Curse of Dimensionality in Deep Neural Networks by Learning
Invariant Representations [1.9580473532948401]
This thesis explores the theoretical foundations of deep learning by studying the relationship between the architecture of these models and the inherent structures found within the data they process.
We ask What drives the efficacy of deep learning algorithms and allows them to beat the so-called curse of dimensionality.
Our methodology takes an empirical approach to deep learning, combining experimental studies with physics-inspired toy models.
arXiv Detail & Related papers (2023-10-24T19:50:41Z) - Homological Convolutional Neural Networks [4.615338063719135]
We propose a novel deep learning architecture that exploits the data structural organization through topologically constrained network representations.
We test our model on 18 benchmark datasets against 5 classic machine learning and 3 deep learning models.
arXiv Detail & Related papers (2023-08-26T08:48:51Z) - Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph
Propagation [68.13453771001522]
We propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings.
We conduct extensive experiments and evaluate our model on large-scale real-world data.
arXiv Detail & Related papers (2023-06-14T13:07:48Z) - Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning [74.6485604326913]
We provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation.
To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN)
Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method.
arXiv Detail & Related papers (2022-12-26T13:18:36Z) - TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
We propose a novel text-rich graph neural network with external knowledge (TeKo)
We first present a flexible heterogeneous semantic network that incorporates high-quality entities.
We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
arXiv Detail & Related papers (2022-06-15T02:33:10Z) - Context-based Deep Learning Architecture with Optimal Integration Layer
for Image Parsing [0.0]
The proposed three-layer context-based deep architecture is capable of integrating context explicitly with visual information.
The experimental outcomes when evaluated on benchmark datasets are promising.
arXiv Detail & Related papers (2022-04-13T07:35:39Z) - Information Flow in Deep Neural Networks [0.6922389632860545]
There is no comprehensive theoretical understanding of how deep neural networks work or are structured.
Deep networks are often seen as black boxes with unclear interpretations and reliability.
This work aims to apply principles and techniques from information theory to deep learning models to increase our theoretical understanding and design better algorithms.
arXiv Detail & Related papers (2022-02-10T23:32:26Z) - Dynamic Inference with Neural Interpreters [72.90231306252007]
We present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules.
inputs to the model are routed through a sequence of functions in a way that is end-to-end learned.
We show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner.
arXiv Detail & Related papers (2021-10-12T23:22:45Z) - A neural anisotropic view of underspecification in deep learning [60.119023683371736]
We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation.
Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
arXiv Detail & Related papers (2021-04-29T14:31:09Z) - Malicious Network Traffic Detection via Deep Learning: An Information
Theoretic View [0.0]
We study how homeomorphism affects learned representation of a malware traffic dataset.
Our results suggest that although the details of learned representations and the specific coordinate system defined over the manifold of all parameters differ slightly, the functional approximations are the same.
arXiv Detail & Related papers (2020-09-16T15:37:44Z) - A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for
Question Answering Over Dynamic Contexts [81.4757750425247]
We study question answering over a dynamic textual environment.
We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner.
arXiv Detail & Related papers (2020-04-25T04:53:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.