Abstract: The relevant limit for machine learning is not N → infinity but instead N → 0. The human visual system is proof that it is possible to learn categories […]
Abstract: Recently, the transfer learning paradigm has seen a surge of interest due to its impressive capabilities in vision and language. Models are pretrained on ever-growing datasets with enormous parameter […]
Deep Learning has recently made significant progress in learning challenges such as speech and image recognition, automatic translation, and text generation, much of that progress being fueled by the success of […]
ABSTRACT: Machine learning systems, in virtually every deployed system, encounter data from a qualitatively different distribution than what they were trained upon. Effectively dealing with this problem, known as domain […]
In many classification tasks, the ground truth is either noisy or subjective. Examples of noisy ground truth include: does this radiology image show a cancerous growth? does this radar data […]
Blockchains that support a general contract layer (e.g., Ethereum) export the functionality of a general-purpose, ownerless, and open-access computer that can enforce property rights for digital data. How is such […]
ABSTRACT:BIO Machine learning has become an indispensable aid to researchers developing the next generation of novel therapeutics. In this talk, I will discuss how some of the most important problems in […]
There have been few high-impact deployments of hardware implementations of cryptographic primitives. We present the benefits and challenges of hardware acceleration of sophisticated cryptographic primitives and protocols, and describe our […]
There have been few high-impact deployments of hardware implementations of cryptographic primitives. We present the benefits and challenges of hardware acceleration of sophisticated cryptographic primitives and protocols, and describe our […]
For those that may not be able to attend the talk please sue this zoom link:
https://upenn.zoom.us/j/92928358554?pwd=MWdDU0lJRmE3U0hDWUdmU284UmNGZz09
Meeting ID: 929 2835 8554
Passcode: 488035
