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 875 for “"shot"”.
-
Few-Shot and Zero-Shot Learning for Information Extraction
… extraction in e-commerce, with few labeled (few-shot learning) or even no labeled (zero-shot learning) training data. We explore multi-source auxiliary information and novel learning techniques to integrate semantic auxiliary information with the input text to improve few-shot learning and …
-
Spin Shot Noise measurement
Charge shot noise, the intrinsic fluctuations in the electrical current resulting from the statistics of the arrival of discrete electronic charges, was predicted more than a century ago2 and has been an invaluable tool for examining the mechanism of charge transport and the nature of …
-
Low-shot Visual Recognition
… 1 to couple of tens of examples is called Lowshot Recognition. In this work, we attempt to solve this problem. Our framework is similar to [1]. We use a related dataset with sufficient number (a couple of hundred) of samples per class to learn representations using a Convolutional Neural …
-
SUCCESSFUL SHOT LOCATIONS AND SHOT TYPES USED IN NCAA MEN’S DIVISION I BASKETBALL
… location (distance and angle from basket) and shot types used on shot success in NCAA Men’s DI basketball during the 2017-18 season. A secondary purpose was to further expand the analysis based on two additional factors: player position (guard, forward, or center) and team ranking. All …
-
On Zero-Shot Reinforcement Learning
… this misalignment is the primary concern of zero-shot reinforcement learning, a problem setting where the agent must generalise to a new task or domain with zero practice shots. Whilst impressive progress has been made on methods that perform zero-shot RL in idealised settings, new work is needed …
-
Generalization and Specialization in Zero-Shot Learning
… deep models. To overcome these obstacles, Zero-Shot Learning (ZSL) is proposed. ZSL aims to transfer classification ability from seen to unseen classes with semantic side information as the bridge. The success of ZSL requires two crucial abilities, i.e., the generalization ability to transfer …
-
Born Waveform Inversion in Shot Coordinate Domain
The goal of this thesis is to integrate Born waveform inversion, variable projection algorithm and model extension concept to get a method that can improve the long scale background model updates reliably and efficiently from seismic data. Born waveform inversion is a partially linearized version …
-
Shot noise of atomic and molecular contacts
Diese Arbeit untersucht den ballistischen Elektronen-Transport in verschiedenen atomaren sowie molekularen Systemen unter der Verwendung der Rastertunnelmikroskopie und der Messung elektrischen Rauschens. Das Letztere wird genutzt, um Informationen über die Anzahl, die Transmission und die …
-
Few Shot Learning for Rare Disease Diagnosis
… The goal of this thesis is to develop few shot learning methods that can overcome the data limitations of deep learning approaches to diagnose patients with rare genetic conditions. Motivated by the need to infuse external knowledge into models, we first develop novel graph neural network …
-
Compositional Models for Few Shot Sequence Learning
Flexible neural sequence models outperform grammar- and automaton-based counterparts on a variety of tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data—particularly to rare or unseen subsequences. Past work has found symbolic …
-
Few-shot text classification with distributional signatures
We explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging-lexical features highly informative for one task …
-
Concept Vectors for Zero-Shot Video Generation
Zero-shot video generation involves generating videos of concepts (action classes) that are not seen in the training phase. Even though the research community has explored conditional video generation for long high-resolution videos, zero-shot video remains a fairly unexplored and challenging task. …
-
The Effect of Pettlep Imagery in a Pre Shot Routine on Full Swing Golf Shot Accuracy: A Single Subjects Design
… imagery intervention implemented into a pre shot routine had on a full swing golf shot. A single subjects design was used with three conditions: imagery before pre shot routine, imagery after pre shot routine and a control condition. Participants were nine undergraduate volunteers with an …
-
Optimising image feature zero-shot-learning with EEG
… not present during training, i.e. we apply zero-shot learning.<br/><br/>To achieve this goal, we first investigate a discriminative feature extraction process for EEG brain data and establish a baseline evaluation of exemplar and category-level decoding. Once competitive decoding rates are …
-
Code generation for few-shot event structure prediction
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
-
Zero-shot learning to execute tasks with robots
… with several features, namely: allowing for zero-shot learning agents that are robust and resilient in the event of failing midway during a task, allowing us to detect failures, and being highly generalizable to new environments. Initially, we focused mostly on training agents that are resilient …
-
Commonsense for Zero-Shot Natural Language Video Localization
Zero-shot Natural Language-Video Localization (NLVL) has shown promising results in training NLVL models solely with raw video data through dynamic video segment proposal generation and pseudo-query annotations. However, existing pseudo-queries lack grounding in the source video and suffer from a …
-
Zero-Shot Scene Graph Relationship Prediction using VLMs
… surge of interest in open-vocabulary and zero-shot SGG, most approaches still require some form of training or adaptation on the target dataset, even when using Vision-Language Models (VLMs). In this work, we propose a training-free framework for the VLMs to predict scene graph relationships. …
Page 1 of 44