New method to tune LLMs is RLMF, reinforcement learning with metacognitive feedback. It is akin to RLAIF and somewhat like ...

Biologically plausible learning now reaches 96.7% on MNIST and 61.7% on CIFAR-10 without backpropagation, as Sakana AI ...

Graduates of the CUNY Graduate Center's Ph.D. program in Computer Science become masters of the computer science discipline and obtain in-depth knowledge of a specialized area. CUNY Graduate Center Ph ...

An intensive, fast-paced programme for predoctoral students to build mathematical intuitions and skills necessary to enter the fields of theoretical neuroscience and foundational machine learning ...

Develop an in-depth knowledge of modern information engineering problems and their solutions. Explore theoretical and practical solutions that can help overcome real-world challenges at the forefront ...

Note: The official name for the course is “Convex optimization for electrical engineering”. However, the course is suitable for any student within SEAS (or beyond) provided you satisfy the math ...

This course aims to develop a computational view of stochastic differential equations (SDEs) for students who have an applied or engineering background, e.g., machine learning, signal processing, ...

Abstract: This survey provides a comprehensive review of the evolving pedagogical approaches in Digital Signal Processing (DSP) education. By employing an AI-assisted recursive search methodology, the ...

Abstract: Linear regression models have a wide range of applications in statistics, signal processing, and machine learning. In this Lecture Notes column we will examine the performance of the ...

Radiology has an image problem. Specifically, the visual of a future where artificial intelligence (AI) algorithms have put radiologists out of work. In 2016, machine-learning pioneer Geoffrey Hinton ...