Browsing by Subject Deep Learning
Showing results 1 to 20 of 20
| Issue Date | Title | Author(s) |
| 2022 | Bridging Theory and Practice in Deep Learning: Optimization and Generalization | Li, Zhiyuan |
| 2024 | Do we need a Reference Signal for Speech Quality Assessment? | Manocha, Pranay |
| 2021 | Essays on Granularity and Machine Learning in Macroeconomics | Vogler, Maximilian |
| 2016 | Extracting Cognition out of Images for the Purpose of Autonomous Driving | Chen, Chenyi |
| 2023 | From Learning to Optimal Learning: Understanding the impact of overparameterization on features of neural networks to optimal learning of expensive, noisy functions using low-dimensional belief models | Duzgun, Ahmet Cagri |
| 2022 | Generalization of Deep Neural Networks in Supervised Learning, Generative Modeling, and Adaptive Data Analysis | Zhang, Yi |
| 2020 | HARDWARE ACCELERATION TO ADDRESS THE COSTS OF DATA MOVEMENT | Valavi, Hossein |
| 2022 | Learning Algorithms for Intelligent Agents and Mechanisms | Rahme, Jad |
| 2019 | Learning and Deploying Local Features | Zhang, Linguang |
| 2023 | Learning Language through Interactions with the Digital World | Yang, John Boda |
| 2019 | Learning Visual Affordances for Robotic Manipulation | Zeng, Andy |
| 2019 | Meta-Learning for Data and Processing Efficiency | Ravi, Sachin |
| 2022 | Overcoming Sampling and Exploration Challenges in Deep Reinforcement Learning | Simmons-Edler, Riley |
| 2019 | Prediction of Cancer Phenotypes Through Machine Learning Approaches: From Gene Modularity to Deep Neural Networks | Zamalloa, Jose Antonio |
| 2019 | ROBOTIC ASSEMBLY: A GENERATIVE ARCHITECTURAL DESIGN STRATEGY THROUGH COMPONENT ARRANGEMENTS IN HIGHLY-CONSTRAINED DESIGN SPACES | Wu, Kaicong |
| 2021 | Security Meets Deep Learning | He, Zecheng |
| 2020 | Three Papers on Collective Action | Zhang, Han |
| 2022 | Towards Efficient and Effective Deep Model-based Reinforcement Learning | Luo, Yuping |
| 2021 | Towards Robust Models in Deep Learning: Regularizing Neural Networks and Generative Models | Bao, Ruying |
| 2023 | Training Deep Neural Networks with In-Memory Computing | Grimm, Christopher Lee |