Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 43 for “"Inductive Biases"”.
-
Social inductive biases for reinforcement learning
How can we build machines that collaborate and learn more seamlessly with humans, and with each other? How do we create fairer societies? How do we minimize the impact of information manipulation campaigns, and fight back? How do we build machine learning algorithms that are more sample efficient …
-
Injecting Inductive Biases into Distributed Representations of Text
… the desired properties into embeddings, use inductive biases. First, we use Knowledge Graphs (KGs) as a data-based inductive bias to derive the semantic representation of words and sentences. The explicit semantics that is encoded in a structure of a KG allows us to acquire the semantic …
-
On the Inductive Biases of Conditional Diffusion Models
… In particular, we are interested in the inductive biases of conditional diffusion models which predispose them to certain forms of interpolation in regions outside the support of the training data. We observe that neural networks are capable of learning qualitatively different forms of …
-
Inductive Biases in Learning Hierarchical Abstractions for Bipedal Locomotion
… and action space. Hierarchical abstractions and inductive biases emerge as critical components in navigating this complexity, offering pathways for effective learning and adaptation in bipedal locomotion tasks. By leveraging hierarchical structures and inductive biases, RL controllers can distill …
-
Inductive Biases and Generalisation in Models for Natural Language Processing
… model architectures with carefully considered inductive biases might lead to improved data-efficiency and performance, by nudging them towards solutions of particular forms and enabling them to generalise from small amounts of data in a more human-like way. This thesis describes work which …
-
Distributional and relational inductive biases for graph representation learning in biomedicine
… high dimensional data at scale and incorporating inductive biases. The emerging field of representation learning on graph structured data opens opportunities to leverage the abundance of structured biomedical knowledge and data to improve model performance. Grand international initiatives have …
-
BRIDGING INTERPRETABILITY AND PERFORMANCE IN 3D DEEP LEARNING THROUGH GEOMETRIC INDUCTIVE BIASES
Questa tesi indaga come i geometric inductive biases possano risolvere limitazioni fondamentali nel deep learning 3D, in particolare per applicazioni critiche di sicurezza come l’ispezione delle reti elettriche. Nonostante i progressi significativi nel campo del 3D scene understanding, la maggior …
-
Representation Learning Through the Lens of Science: Symmetry, Language and Symbolic Inductive Biases
… on the identification and development of novel inductive biases (assumptions made by the learning algorithm to improve generalization) based on symmetry, language, and symbolic properties. These inductive biases prove beneficial for both solving scientific problems using machine learning and …
-
Learning inside the prediction function
… learning (together with few but carefully chosen inductive biases for each domain) and train a neural network to approximate this unknown function. In this thesis, we show that this single-function, single-neural-network approach can be too constraining and instead suggest spawning per-point …
-
Meta-learning and Enforcing Useful Conservation Laws in Sequential Prediction Problems
… of both data and computing resources, the inductive biases induced by various training methods, model components, and architectures has helped enable efficient generalization as well. Useful biases often exploit symmetries in the prediction problem, such as convolutional neural networks …
-
Disentangling neural network representations for improved generalization
… in data collection and model design. Generally, inductive biases can make this process easier by leveraging knowledge about the world to guide neural network design. One such inductive bias is disentanglment, which can help preven neural networks from learning representations that capture …
-
New Algorithms for Attribute-Efficient on -Line Linear Learning
… goal of this work was to identify the inductive biases of each algorithm, so that they can be fairlycompared with each other. By examining their biases and properties using the results presented here, it is possible to view 2Pes as a particular generalization of the Winnow algorithm, …
-
Geometric Deep Learning for Biomolecules
… to model biological structures. By embedding inductive biases based on geometry and physical laws, we aim to enhance our understanding and predictive capabilities in biomolecular systems. We present methods using equivariant neural networks for geometrical protein representation learning, …
-
Deep Learning on Geometry Representations
… geometry to propose deep learning pipelines and inductive biases that are directly compatible with common geometry representations, without relying on simple uniform structure.
-
Exploring Neuroimaging-Specific Deep Learning Biases: Uncertainty, Dynamic Graphs, and Communities
… data. This proficiency is further refined by the inductive biases incorporated in DL model designs, specifically crafted to fit the unique characteristics of the input data. This thesis embarks on a deeper exploration of DL methods applied to functional neuroimaging data, moving beyond …
-
Towards High-Dimensional Generalization in Neural Networks
… scaling laws with respect to training time. 2. Inductive bias refers to the set of assumptions a learning algorithm makes to predict outputs on inputs it has not encountered. We propose quantifying the amount of inductive bias required for a model to generalize well with a fixed amount of …
-
Inductive Bias and Modular Design for Sample-Efficient Neural Language Learning
… this ability include 1) a set of in-born inductive biases and 2) the deep entrenchment of language in other perceptual and cognitive faculties, combined with the ability to transfer and recombine knowledge across these domains. The main contribution of my thesis is giving concrete form to …
-
Towards More Generalizable Neural Networks via Modularity
… task as the amount of information content in the inductive biases required to solve a task, and demonstrates that generalization difficulty relies crucially on the number of dimensions of generalization. Inspired by the modularity of biological learning systems, this thesis then demonstrates …
-
Scalable Representation Learning: On Data-scarcity, Uncertainty and Symmetry
… incorporating prior known symmetries and inductive biases of the problem, utilizing Bayesian and ensemble methods, and leveraging abundance of unlabeled data in a representation learning framework. We discuss and demonstrate practical applications of these novel tools in diverse domains …
-
Encoding parameter and structural efficiency in deep learning
… modelling assumptions about the data and inductive biases on network structure to propose simpler neural network models in several application areas. For each of the application areas I work in, I use a combination of these principles of efficiency to design novel approaches. First, within …
Page 1 of 3