A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

Major Breakthrough in Neuroscience Research: Deep Learning Technique Achieves Decoding of Natural Speech from Brain Signals A cross-disciplinary research team at New York University recently achieved a major breakthrough in the fields of neuroscience and artificial intelligence. They developed a novel deep learning-based framework that can directly...

Efficient Learning of Accurate Surrogates for Simulations of Complex Systems

This research proposes an online learning method for efficiently constructing surrogate models that can accurately emulate complex systems. The method consists of three key components: Sampling strategy for generating new training and testing data; Learning strategy for generating candidate surrogate models based on the training data; Validation me...

Exploring the Psychology of LLMs' Moral and Legal Reasoning

Current Situation Nowadays, large language models (LLMs) have demonstrated expert-level performance in multiple fields, which has sparked great interest in understanding their internal reasoning processes. Comprehending how LLMs generate these remarkable results is crucial for the future development of artificial intelligence agents and ensuring th...

Mitigating Social Biases of Pre-trained Language Models via Contrastive Self-Debiasing with Double Data Augmentation

Introduction: Currently, pre-trained language models (PLMs) are widely applied in the field of natural language processing, but they have the problem of inheriting and amplifying social biases present in the training corpora. Social biases may lead to unpredictable risks in real-world applications of PLMs, such as automatic job screening systems te...

A Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex Models and Heterogeneous Data

A Unified Momentum-based Paradigm for Decentralized SGD for Non-Convex Models and Heterogeneous Data Research Background In recent years, with the rise of the Internet of Things and edge computing, distributed machine learning has developed rapidly, especially the decentralized training paradigm. However, in practical scenarios, non-convex objectiv...

Acquiring and Modeling Abstract Commonsense Knowledge via Conceptualization

Introduction The lack of commonsense knowledge in artificial intelligence systems has long been one of the main bottlenecks hindering the development of this field. Although great strides have been made in recent years through neural language models and commonsense knowledge graphs, the key component of human intelligence, “conceptualization,” has ...